Fundometry

From Largecaps to Microcaps: Stock Herding within ETFs

2010-10-20T21:04:00.000-07:00

Even before the Flash Crash on May 6, investors and reporters pondered whether ETFs were raising correlations among stocks. Solely buying or selling ETFs directly does not promote correlation. Instead, differences between the ETF's traded price and the indicative net asset value (INAV) create arbitrage opportunities. That difference in price encourages highly efficient traders ("arbitrageurs") to transact in a large sample of stocks underlying the ETF, either buying or selling the stocks and executing an opposing trade in the ETF. Creation Units and Redemption Units further increase the means of executing such arbitrage strategies.

However, arbitrageurs do perform a very important market function for ETFs: they keep the differences between the ETF traded price and the INAV very small (also known to many as "tracking error"). (A very similar term, "basis risk", is commonly used in the context of derivatives, but Fundometry does not seek to blur the lines between ETFs with derivatives any further.) As a result, an ETF will trade closer to its INAV. Or does the INAV trade closer to its ETF? Arbitrageurs likely do not assess which price (ETF or INAV) reflects the true value of a portfolio of equities, but one can argue at length about whether either price reflects fundamental value or the efficient processing of information.

Instead of focusing on how arbitrageurs make a living, this analysis seeks to slice broad equity sectors, according to each stock's market cap, and observe how stocks correlate to their respective ETFs. Frequently, the S&P 500 is cited as the worthwhile benchmark because its components comprise such a large share of the market's value. However, the herding effect of stocks within an ETF may be more acute among for a smaller company, which does not have much analyst coverage to influence its traded price and where less liquidity can create artificial volatility.

The methodology generating the following results is straight-forward. Four ETFs are selected based on their coverage of different segments of the market based on equity market cap.

iShares S&P 500 Index Fund (IVV)
iShares S&P Midcap 400 Index Fund (IJH)
iShares S&P SmallCap 600 Index Fund (IJR)
iShares Russell Microcap Index Fund (IWC) (holding approx 1300-1400 stocks)

The stocks held by each ETF are monitored on a monthly basis, starting with the first quarter of 2008 (an arbitrary point in time but one which avoids too much data squeezing into one graph). Each month, every stock is classified into a quintile based on its weight in the respective ETF. (This assumes that each stock is a member of only one of the four selected ETFs at any one time.) For example, the 100 stocks with the smallest weights in the S&P 500 Index (hence, the smallest 100 stocks by market cap among the 500 largecap stocks) get classified in the lowest quintile ("quintile 1"). By another example, the 120 stocks with the largest weights in the S&P SmallCap 600 Index (the largest 120 stocks by market cap among the 600 smallcap stocks) get classified in the highest quintile ("quintile 5").

Using daily total returns over a 20-day period, a model computes historical correlations between each stock and its corresponding ETF. These correlations are further classified into segments, identified in the graphs below. These segments help to visually demonstrate the breadth of correlation among stocks inside an ETF. If all of these individual correlations were averaged, an important dimension of the outcome would be missing.Finally, these monthly correlations are aggregated into quarterly periods and presented in the following graphs.

The first graph shows the historical correlations among largecap stocks in the S&P 500 index. (Click on any graph to enlarge.)

Above each stacked bar is a quintile number (1 through 5) at the top of the graph, and a legend provides a definition for each numbered quintile. In the case of the S&P 500 index, each quintile contains 100 largecap stocks. Each vertical stacked bar represents the range of correlations for the 100 stocks, as observed during a quarterly period. Represented by different shades of grey, the correlations are grouped according to where they fall within the distribution. Given the wide variations in correlation, among different stocks and over time, the stacked bars provide a more meaningful picture than a simple average or median. (In case median is easier to follow, a red dot depicts the median correlation for each group.)

More often than not, the smallest 100 stocks (quintile 1) within the S&P 500 index correlate more tightly (as viewed by the full height of the stacked bars) than do the largest 100 stocks (quintile 5). As one moves from left to right, the range of correlation broadens in many of the quarterly periods. As one would expect, the overall tightest ranges of correlations occured in 2008Q4, 2009Q1, and 2010Q2, each characteristic of elevated market volatility. Within each quarter, the median correlations (red dots) did not vary materially from the smallest to the largest quintile.

Why might larger stocks within the S&P 500 index have a broader range of correlation than their smaller peers? Liquidity should be fairly deep for all of these largecap stocks. All of these stocks should have reasonable analyst coverage, although larger stocks probably attract more interest from reporters and analysts. Does the availability of more information cause investors to trade a company according to its specific economic fundamentals, as opposed to a less-publicized company being considered part of another largecap basket trade?

Moving down the market cap spectrum, the next graph displays the same correlation profile for the S&P MidCap 400 index.

When looking at the range of correlations within each stacked bar, differences between the smallest and largest quintiles (of 80 stocks) begin to blur over time. In 2008Q1 and 2008Q3, the smallest quintile exhibited the tightest range of correlations among the five quintiles. In some quarters, the largest quintile exhibited the largest range of correlations. As with the largecap profile, the median correlations did not vary materially from the smallest to the largest quintile in each quarter.

The next segment of the market cap spectrum, smallcap stocks, is represented by the S&P SmallCap 600 index.

In this case, the median correlations (red dots) in most quarters showed a steady increase from the smallest quintile (of 120 stocks) and incrementally with each quintile of larger stocks. This pattern is a distinct divergence from the correlation profiles of largecap and midcap stocks above. In certain quarters (2008Q1, 2008Q4, 2009Q4, 2010Q1, 2010Q3), the range of correlations for quintile 1 was materially larger than for other quintiles within the same quarter. As seen from this graph, as smallcap companies moved up in the market cap ranks, their stock prices became more correlated to the S&P SmallCap 600 index.

Finally, rounding out the smallest end of the market cap spectrum is the Russell Microcap Index. (iShares did not license a microcap index from S&P.)

The pattern of increasing correlation with increasing relative market cap was even more prevalent among microcap stocks than smallcap or larger peers. In every quarter, the correlation of stocks to the index (whether based on the median or full range from 5th to 95th percentile) increased as the market cap of the stock increased relative to peers. Quintile 1 exhibited the lowest correlation to the index, in terms of the median and the top/bottom (95th and 5th percentiles) of the stacked bars. Quintile 2 exhibited higher correlations than quintile 1, in terms of the median and 95th and 5th percentiles. This pattern continued through quintile 5 and was remarkably consistent throughout every quarter in the sampled period.

Given the different correlation profiles within each ETF, a convenient summary of these results shows the correlation ranges across the market cap spectrum.

In this final graph, we observe that the median correlation (red dots) increased from the smallest quintile of microcap stocks (quintile 1 of the Russell Microcap Index) to the largest smallcap stocks (quintiles 4 and 5 of the S&P SmallCapp 600 Index). Thereafter, midcap and largecap stocks did not correlate significantly more or less with their respective ETF as their market caps increased. What makes micocap and smallcap stocks different from their larger peers?

1. Mathematically, a cap-weighted index and its corresponding ETF should corrrelate to a greater extent with larger companies in the index or portfolio than with smaller companies. The larger the weight of a specific stock, the greater its influence on the returns of the ETF, hence a higher expected correlation with the ETF over time. The correlation profiles of the microcap and smallcap ETFs were consistent with this premise, but not so for the midcap and largecap ETFs.

2. Despite explanation #1, midcap and largecap stocks of varying weights (quintiles) correlated with the ETF, and plausibly with each other, inside a relatively tight range. Overall, midcap quintile 1 stocks and largecap quintile 3 stocks exhibited the narrowest correlation range among their respective peers. Do arbitrageurs have greater influence on the price movements of the smaller quintile stocks, while a larger population of traders contribute to price movements in the larger quintile stocks?

3. Analyst coverage tends to decline as the market cap of a company declines. Therefore, institutional portfolio managers, and even individual investors, need to spend a greater amount of resources and cost to analyze and monitor smaller companies. Therefore, most investors who gravitate toward investing in largecap stocks can utilize analyst reports and press releases to make trading decisions, which implies a notable factor of company-specific criteria influencing price movements. Conversely, microcap and smallcap investors must invest in more companies (a large "basket" of stocks) in order to diversify their risk to any one company which may suddenly go out of business. In fact, one of the greatest appeals of a microcap ETF is the ability to gain diversified exposure to a market segment which would be otherwise quite expensive to trade (i.e. paying bid-offer spreads and commissions across a very large number of stocks).

4. Microcap and smallcap stocks typically have less liquidity (trading volume) than their larger peers. In order for arbitrageurs to profit from small discrepancies between an ETF and its underlying stocks, transaction costs, specifically bid-offer spreads, must be minimized. Trading a certain numbers of shares within a target bid-offer spread is easier with stocks which have greater trading volumes. Therefore, midcap and largecap stocks should be more favored by traders seeking arbitrage profits between the ETF and its underlying stocks. Microcap and small cap stocks (especially quintiles 1 and 2) would not be liquid enough for such traders. (Also see comment below.)

In fact, a fair absence of arbitrageurs in microcap stocks may explain the steady increase in correlation when moving from quintile 1 to quintile 5. The microcap ETF may provide the best example of how stocks should correlate to an ETF when arbitrage trades are difficult to execute in great frequency, and consequently less profitable. If the microcap segment is the least susceptible to arbitrage trades across a large number of stocks, then it should be the most favorable market segment for traditional fundamental analysts to generate alpha.

Regardless of which explanation, or combination thereof, is the most convincing, stock correlations with an ETF can change significantly depending on company-specific market cap (proxied by weight quintiles) and overall market volatility (proxied by time). One should note that the stocks within a quintile varies from month to month, as their ETF weights change regularly. At the individual stock level, correlations vary substantially from month to month, depending on company-specific news and events. For investors, the more stocks held in a portfolio, the more relevant these results become. For active investors, generating alpha (i.e. outperforming the index) should be more difficult with largecap stocks than with microcap stocks, all else being equal.

From the perspective of public companies, these results probably quantify what CEOs and CFOs already knew. As market cap grows, the more a stock's price becomes influenced by programmatic trading systems supporting index-linked investment vehicles (mutual funds, ETFs, insurance subaccounts, hedge funds, etc). However, at some point, trading volume for a stock may become large enough to dilute the impact of these programmatic factors and increase the influence of analyst coverage and company-specific fundamentals.

This analysis does not dig deep enough to determine how high-frequency trading firms, ETF authorized participants and market structure play a role in the relationship between stocks and their associated ETFs. Hence, the question still remains: "Do ETFs track their INAVs, or is it the other way around?"

Further reading:

The Herd Instinct Takes Over Component Stocks' Correlation to S&P 500 at Highest Level Since '87 Crash (WSJ)

Should ETFs be allowed to include illiquid stocks? (Felix Salmon)

The Real Trend In Fund Flows Will Crush Mutual Fund Managers, And Forever Change The Way Stocks Behave (Clusterstock)

Did nobody ever consider that indexing was dangerous? (FT Alphaville)

Redemptions are Kind to Tax Efficiency, Vanguard ETF Edition

2010-09-29T09:02:00.000-07:00

Tax-deferral synergies from bringing together mutual fund and ETF investors

ETF providers promote many benefits of their products: convenience, liquidity, and tax-efficiency, among others. This analysis focuses on some of the mechanics behind tax efficiency, and a unique twist which Vanguard brings to the table with their ETF structure.

Of course, tax efficiency matters when an investor is subject to capital gains taxes. Investors in ETFs can realize capital gains and losses in multiple ways. Buying and selling on the exchanges triggers taxable gains and losses. Like mutual funds, ETFs must distribute any net capital gains realized at the end of the their fiscal year. For a small subset of investors, an in-kind redemption, where an investor exchanges ETF shares for a proportionate number of shares in every stock the ETF holds, can lead to a taxable event for the investor ... but not for the ETF. (Hereafter, we will focus on equity ETFs, not fixed income, commodity, or swap-based ETNs.)

Tax-efficiency can be maximized as long as investors continue purchasing an ETF in sufficient volumes (by way of creation units) while redemptions do not spike for a prolonged period. ETF managers strive to reduce capital gains distributions by trading underlying securities in a manner which offsets realized capital gains with capital losses. The mechanics are no different than with any individual taxable account, in which one tries to harvest losses at opportune times. Ideally, within its underlying securities, an ETF would continue realizing and rolling over net capital losses which can offset any future capital gains.

For Vanguard, the scope of tax-efficiency goes beyond the ETF. Among those Vanguard funds which offer an ETF, the ETF investors and mutual fund investors belong to the same fund. Every investor owns a pro rata share of the same basket of stocks, including all of the associated tax lots. The difference in ownership is signified by the share class. An excerpt from a Vanguard brochure nicely summarizes the benefit to ETF investors:

The more tax lots available, the better a portfolio manager can minimize taxable distributions to all shareholders. This rule applies regardless of whether a fund is a traditional mutual fund or ETF. Thus, when structuring their funds, Vanguard decided to combine the strengths of both the mutual fund and the ETF to maximize tax efficiency.

A historical comparison of two leading Vanguard index funds demonstrates the impact of having mutual fund and ETF investors share the same underlying portfolio. On May 24, 2000, Vanguard launched the ETF share class for the Vanguard Total Stock Market Index Fund. At the time, Vanguard's 500 Index Fund was larger and had existed for a much longer time. However, the ETF share class did not get launched until over ten years later, on September 7, 2010. Did the "patented share-class system" give the Total Stock Market Index Fund advantages which the 500 Index Fund may only be starting to realize?

The annual and semi-annual financial reports for the Vanguard funds provide some very useful details on potential capital gains tax liabilities and the impact of in-kind redemptions ("IKRs"). The following data points, collected from the financial reports, were utilized in this analysis:

1. Realized Net Capital Gains and Losses on securities
2. Realized Net Capital Gains and Losses resulting from in-kind redemptions
3. Purchases and Redemptions by share class.
4. Cumulative Paid-In Capital

Items 1 and 2 directly pertain to the potential capital gains tax liability. Item 3 will help demonstrate the relationship between IKRs and all institutional-size redemptions. Finally, item 4 has a less clear impact, if any, on the potential capital gains tax liability of a mutual fund or ETF. The following excerpt from the latest Vanguard 500 Index Fund semi-annual report helps to explain the relevance of paid-in capital:

During the six months ended June 30, 2010, the fund realized $131,817,000 of net capital gains resulting from in-kind redemptions—in which shareholders exchanged fund shares for securities held by the fund rather than for cash. Because such gains are not taxable to the fund, and are not distributed to shareholders, they have been reclassified from accumulated net realized losses to paid-in capital.

This disclosure is quite common for Vanguard ETFs, as well as ETFs from other sponsors. In fact, Vanguard's index funds disclosed capital gains realized from IKRs even before the ETF class existed. Such disclosures clearly show how good a job the sponsor does at removing share lots of underlying securities with the largest capital gains (without incurring any immediate tax liability). When capital gains are realized from IKRs, the fund simply reclassifies them as paid-in capital. (More specifically, these capital gains are added to paid-in capital instead of increasing Accumulated Net Realized Gains or offsetting Accumulated Net Realized Losses.)

In terms of tax efficiency, how has the tax profile of the 500 Index Fund fared over the last ten years? The following graph shows the two sources of paid-in capital — new investments (green bars) and capital gains resulting from IKRs (yellow bars) — and compares them to the taxable realized capital losses (red bars).

As the fund AUM grows over time due to new investments (rising green bars), so does the cumulative balance of IKR capital gains classified as paid-in capital (yellow bars). In fact, those capital gains resulting from IKRs are almost as large as the accumulated net realized capital losses (red bars). If the yellow bars did not exist, how would the tax profile haved fared over the same period?

For tax years 2001 and 2009, the 500 Index Fund would have been forced to make capital gain distributions to all classes of shareholders, proportionate to the value of shares allocated to each class and shareholder. The tax-deferral benefit from IKRs (presumably from institutional investors who have positions large enough to warrant accepting the underlying 500 securities instead of cash) accrues to all classes of shareholders.

We can assume that retail investors accessed this fund through the Investors share class. The other classes (Admiral and Signal) have lower expense ratios but much higher minimum account size requirements, hence more appropriate for institutional investors. How does the redemption activity of institutional investors compare to that of individual investors?

In most years, the value of institutional shares redeemed for cash or in-kind (red bars) is a fraction of the equivalent amount for retail shares (green bars). Furthermore, the capital gains classified as paid-in capital represent an even smaller fraction of these redemptions.

The following graphs convey the same data for the Total Stock Market ("TSM") Index Fund, which launched its ETF share class on May 24, 2000. (Remember, the 500 Index Fund did not have an ETF share class until after its latest published financial report.)

First, the AUM growth rate for the TSM Index Fund (depicted by the increasing green bars over time) is much higher than that of the 500 Index Fund. Normally, such a large and consistent inflow of new investments would give the portfolio manager ample flexibility to minimize capital gain distributions to shareholders. In fact, the magnitude of tax-deferral is impressive as the portfolio manager realized only net capital losses (red bars) since 2000. These capital losses are more than offset by the paid-in capital resulting from IKRs (yellow bars).

However, excluding the impact of IKRs, the tax profile changes significantly.

In every tax year, the fund likely would have made capital gain distributions to shareholders. Thanks to IKRs, the portfolio manager was able to avoid a series of capital gain distributions and retain more AUM (from which the manager's fees are computed).

The following graph shows historical redemption activity for retail and institutional investors. For the TSM Index Fund, institutional investors are deemed to have utilized the Institutional and ETF share classes, both of which do not exist in the 500 Index Fund. The Vanguard Institutional Index Fund, which also holds approximately 500 stocks, offers institutional share classes, but its financial and tax books are entirely separate from those of the 500 Index Fund. Perhaps differences in legal opinion explain the separation.

Here one can observe that the amount of capital gains resulting from IKRs (yellow bars) fluctuates consistently with the amount of institutional and ETF shares redeemed (red bars). (ETF redemptions are presumed to always be in-kind redemptions since the other means of liquidating an ETF position — via the seconday market — is not a redemption from the fund's perspective.) As long as IKRs keep occurring, the portfolio manager should be able to continue realizing net capital losses every tax year. Of course, if the market rallies strongly and steadily, the manager may run out of tax lots with embedded capital losses. (One could think of worse challenges to have.)

On the surface, everyone appears to have benefitted. The portfolio manager removes the largest capital gain tax lots from the fund, reducing the likelihood of realizing net capital gains in future tax years. If the fund continues to realize only net capital losses every tax year, a capital gain distribution should not occur, thus avoiding a taxable event for investors in non-qualified accounts. However, by avoiding a capital gains distribution, the AUM of the fund does not decrease either, increasing the management fee incrementally. Only when an investor eventually sells shares, either back to the mutual fund or in the ETF's secondary market, a capital gains tax liability could be incurred. Essentially, those net capital gains resulting from IKRs, including any unrealized net capital gains, are accumulated within the fund's NAV and become taxable when an investor redeems shares. Whether the capital gains are classified as long-term or short-term depends on the holding period of the fund investor, not the fund's holding period at the underlying security level.

Long-term investors benefit the most. Not only do they avoid receiving taxable distributions until redeeming fund shares, their profits should be mostly (or entirely, depending on investor-specific holding periods) taxed at the lower long-term capital gains tax rate. Short-term investors should benefit, but not necessarily in all cases. If a short-term investor (who holds a position under one year) realizes a capital gain, the entire gain would presumeably be taxed as current income (subject to the investor's specific circumstances, of course). If instead the investor had received a capital gain distribution from the fund (which likely could have happened if not for the IKRs), part of the distribution may have been classified as long-term capital gains (pursuant to the portfolio manager's choice of which security-level tax lots to sell). Since Vanguard promotes long-term investment horizons, one can understand the focus on benefitting long-term investors.

This brief analysis does not draw any new conclusions. Rather, the comparison between two leading Vanguard funds, one with and one without an ETF class, demonstrates the importance of in-kind redemptions on tax efficiency. Investors in the Vanguard 500 Index Fund should look forward to greater tax efficiency in the future. What about the investors in the Vanguard Institutional Index Fund, which does not offer an ETF class? Could a very large fund merger be on the horizon?

ETF Trade Execution Quality during the Flash Crash Month

2010-08-20T14:01:00.000-07:00

Subtitle: Some market orders received better price execution than limit orders.

Recently, all market centers disclosed Rule 605 reports for May 2010. This data provides insights into the quality of trade execution across all market centers by ticker, order size, and order type. The following analysis focuses on a favorite focus point of the Flash Crash debate: ETFs. By utilizing the Rule 605 disclosures for May 2010, a volatile month for equities trading, we can observe where ETF trades received better price execution.

There are no clear strong or weak points apparent in the numerous graphs below. Each reader may have an interest in specific ETFs or market centers. Unfortunately, there are too many permutations of ETFs and market centers to be covered within this post. The following graphs sample a few ETF complexes and market centers with the highest trading volumes during May 2010.

More importantly, Rule 605 disclosures summarize trades for the entire month, not itemized by day. Hence, the following graphs do not solely represent trading activity on May 6, 2010, when many ETF orders were cancelled as a result of extreme price volatility resulting from brief market illiquidity. (Judging from the wide range of price improvement statistics, these cancelled trades appear to be included in the data, but one cannot assume the all market centers included erroneous trades in their disclosures.) For some ETFs and specific market centers, the price volatility on May 6 may have been large enough to significantly impact the summary statistics for the entire month. Nevertheless, a comparison of the same statistics across ETFs and market centers should demonstrate how trade execution performed on a relative basis.

In previous posts (iShares/NFS and Schwab/UBS) concerning ETF trade execution quality, Fundometry studied how much trades were executed inside the NBBO, at the market, and outside the NBBO for specific market centers. One post defined a useful summary metric called Weighted Average Price Impact ("WAPI"). In the Rule 605 disclosures, price impact (in dollars per share) is disclosed separately for trades where price improved and degraded (or, as some might say, disimproved) relative to the subsequent NBBO. The overall price impact of trades can be summarized by averaging the price impact for individual categories of trades, based on improvement or degradation relative to the NBBO and weighted according to respective volumes in each category. A positive WAPI indicates that overall more shares traded with price improvement (i.e. price at execution was better than the NBBO when the order was submitted) than with price degradation, and the opposite would be implied by a negative WAPI.

In addition to WAPI, the following graphs plot volume of executed orders, including orders routed to other market centers. Ideally, this data on trade execution would be itemized separately for internal orders and orders routed to other market centers (in cases where the best price existed at another venue). Unfortunately, the data summarizes trade execution quality across all orders executed regardless of venue (internal or external). In order to highlight the impact of orders executed internally to any given market center, only data is included if the volume of internal orders contituted at least 80% of total executed volume. (The final graphs in this post follow a stricter threshold of 5%.)

Finally, both WAPI and volume are broken down by market orders and marketable limit orders - the only two types of orders for which price improvement/degradation statistics are available.

Based on the Rule 605 disclosures sampled in this analysis, three ETF complexes had the largest trading volumes of market and marketable limit orders during May: SPDR, iShares, and ProShares. Initially, ETFs will be summarized at the complex-level (volume-weighted summary of all tickers within an ETF family) across different market centers (exchanges, ECNs, ATS, etc). Subsequently, selected individual ETFs will be analyzed in greater detail, by order size and order type. (Given the number of graphs involved, including other ETF complexes or selecting more individual ETFs would cause this post to become rather voluminous.)

Complex-Level Perspective

In these complex-level graphs, market centers displayed along the horizontal axis are sorted according to overall WAPI. Market centers which executed orders with overall price degradation (outside the NBBO recorded at the time the order was submitted) are displayed closer to the left side. Despite this sorting order, some market centers achieved notably better or worse trade execution quality depending on the order type (market vs marketable limit).

The following graph summarizes trade execution quality for SPDR ETFs during May 2010.

For the SPDR family of ETFs, a few low-volume market centers exhibited the least favorable trade execution quality. Among market centers with notable volume, National Financial Services (NFS), BATS, and Knight Capital Markets performed less favorably relative to their peers, in terms of WAPI. Depending on the market center, market orders received better price execution than marketable limit orders (as seen when red diamonds are higher than blue diamonds). While there is no clear indication of where market orders should have fared better, marketable limit orders (a buy order above the best offer or a sell order below the best bid) did not always result in better price execution than market orders.

The following graph, which summarizes trade execution quality for iShares ETFs during May 2010, shows that orders executed at UBS, Knight, and Chicago Stock Exchange (CHX) Matching System received less favorable price execution relative to their peers. CHX and NFS executed market orders (red diamonds) at better prices than marketable limit orders (blue diamonds), but the opposite was the case at UBS and Knight. Direct Edge X exhibited the most negative WAPI among market orders.

The following graph for ProShares ETFs shows that Knight Equity Markets executed marketable limit orders with a reasonable WAPI but achieved relatively poor execution quality with market orders. Conversely, Knight Capital Markets achieved reasonable WAPI for both types of orders. Among peers, Oppenheimer exhibited the best WAPI for marketable limit orders but performed poorly with market orders. Again, one can observe how order type matters depending on the venue.

Focus: SPDR ETFs

The graphs above summarize trade execution quality for different market centers across all ETFs within a single family. Conversely, the following graph summarizes trade execution for different ETFs in the SPDR complex across all market centers.

Two ETFs with the least favorable WAPI, XSD and XBI, exhibited worse price execution for market orders than for marketable limit orders. Otherwise, market orders received more favorable execution, based on price, than marketable limit orders for most of the other tickers (even though volume for market orders was substantially lower than for marketable limit orders). In comparison to other tickers, MDY and GWX exhibited less favorable price execution for marketable limit orders. These four ETFs merit more detailed analysis.

The following graph drills down into trade execution quality by order type and order size for XSD.

Interestingly, two specific categories of market orders account for the unfavorable WAPI: (1) order quantity 2000-4999 (order size code "C") executed at UBS Securities and (2) order quantity under 500 shares (order size code "A") executed at Knight Capital Markets. Furthermore, the most favorable WAPI for market orders in this graph occurred at Knight Capital Markets for order quantity over 5000 shares (order size code "D"). Given that the fragmentation of market structure generally favors smaller orders for achieving better execution quality, the results at Knight are unexpected.

Does the same graph for XBI result in similar observations?

Yes and no. As was the case for large-quantity market orders for XSD executed at Knight, the largest-quantity market orders (2000-4999 shares) for XBI executed at NYSE Arca achieved the most favorable WAPI, certainly better than smaller-quantity market orders. At Knight, larger market orders (2000-4999 shares) received substantially less favorable WAPI than market orders with the largest-quantity (over 5000 shares). However, at Automated Trading Desk, the largest-quantity market orders (over 5000 shares) received substantially unfavorable WAPI than all other comparable orders on this graph. (Note that the total volume on some of these categories of market orders is very small.)

In the two graphs above, the WAPI for marketable limit orders was very consistent relative to market orders. Two SPDR ETFs which exhibited unfavorable execution, based on WAPI, for marketable limit orders are MDY and GWX. The following graph drills down into trade execution quality for MDY.

Marketable limit orders for MDY executed at Barclays Capital and Direct Edge X received increasingly less favorable WAPI as the order size increased. This pattern follows the broad expectation that smaller orders receive better price execution. To a lesser extent, the same pattern is evident at BATS Exchange and NASDAQ. However, market orders received more favorable WAPI than marketable limit orders, based on comparable order sizes.

The same graph for GWX yields less conclusive observations.

At NYSE Arca, Knight Capital Markets, and UBS Securities, marketable limit orders received better WAPI when the order size was smaller, but the opposite trend occurred at LavaFlow ECN (where volume was also lower). At UBS Securities, market orders received better WAPI when their order size was smaller, but at Knight there was no conclusive pattern to how well a market order would be executed according to its size.

Focus: iShares ETFs

Switching from the SPDR to iShares complex, the following graph summarizes trade execution for different ETFs in the iShares complex across all market centers.

Two ETFs stand out in terms of unfavorable WAPI for market orders: IWV and IWR. In addition, for marketable limit orders, EFG exhibited a less favorable WAPI than its peers. The following three graphs analyze what happened with each of these iShares ETFs.

For IWV, one could easily miss any anomalies in the trade execution quality. The red diamond at the lower left region represents substantially negative WAPI for large-quantity market orders executed at Knight Capital Markets. Smaller market orders at Knight received better execution. Other market centers exhibited reasonable results.

The results for IWR are surprisingly consistent with IWV. Large-quantity market orders executed at Knight received substantially negative WAPI, as indicated by the red diamond in the lower left region. The remaining data points are easier to view when this one negative outlier is excluded from the graph.

Across all five market centers, marketable limit orders received less favorable WAPI as the order size increased, with a few exceptions. At Knight, only the smallest-quantity market and marketable limit orders achieved positive WAPI. At NYSE Arca, market orders once again exhibited more favorable WAPI than marketable limit orders of comparable order size. While Automated Trading Desk executed market orders with more favorable WAPI as the order size decreased, the same pattern could not be observed for marketable limit orders.

Before switching focus to EFG, one may wonder whether orders for other iShares ETFs exhibited substantially negative WAPI at Knight. The following graph indicates that IWV and IWR were unique outliers, but some other ETFs also exhibited less favorable WAPI for market orders: IVW, IWP, IJT, IJH, and IJR.

For EFG, the least favorable WAPI for marketable limit orders occurred at E*Trade Capital Markets, although the magnitude is insignificant when compared to results for IWV and IWR.

At E*Trade, the largest-quantity marketable limit orders for EFG exhibited substantially unfavorable WAPI versus other market centers. Knight Capital Markets executed marketable limit orders with improving WAPI as the order size increased (again, counter to expectations based on general market structure). Even NYSE Arca and Pershing followed a similar pattern with respect to market orders (i.e. more favorable WAPI as order size increased), except when Pershing executed the largest-quantity market orders.

Focus: ProShares ETFs

The following graph summarizes trade execution for different ETFs in the ProShares complex across all market centers.

As shown in the next two graphs, both SQQQ and UPRO exhibited similar patterns to earlier examples for selected SPDR and iShares ETFs. In the case of SQQQ, market orders of quantity 2000-4999 executed at Knight Capital Markets received the least favorable WAPI, although not of significant magnitude in comparison to IWV and IWR.

For UPRO, the largest market orders executed at Knight received the most negative WAPI.

However, for ROM, market orders of the largest-quantity (5000 or more shares) exhibited the least favorable WAPI at Citadel Securities, although Knight and Automated Trading Desk also executed market orders with relatively less favorable WAPI.

Conclusions

Depending on the market center and specific ticker, trade execution quality, as measured by Weighted Average Price Improvement, varies wildly. As seen in the graphs above, robust generalizations are difficult to draw.

1. Marketable limit orders do not always receive better price improvement than market orders. Marketable limit orders are limit orders which should be executed immediately, as with market orders, because either the order bid is higher than the best offer or the order offer is lower than the best bid. Depending on the market center, market orders can receive better price improvement versus marketable limit orders of comparable order size. In other words, executing a market order may result in price improvement despite the fact that many erroneous trades involved stop loss market orders.

2. Depending on the specific ETF, market center does matter. Among the small sample of graphs above, some market centers repeatedly achieved less favorable price improvement than their peers. The Rule 605 disclosures cover only orders for which a market center was not specified by the investor. As a result, the data should reflect efforts to route orders to the market center which has the best NBBO at a given time. However, identifying the reasons why an order executes at a specific venue requires more data than available through the Rule 605 disclosures.

3. Major market centers with a large share of volume (usually synonymous with deep liquidity) do not necessarily achieve better price improvement. Regardless of trading volume, some market centers appear to execute market orders with better price improvement than for marketable limit orders. The same complex-level graphs shown above are available with market centers sorted from left to right in order of trading volume: SPDR, iShares, ProShares.

4. Typically, order size does matter. Depending on the market center and individual ETF, smaller orders might achieve better price improvement than larger orders, but many instances of the opposite exist. At least one should not assume that smaller orders always achieve better price improvement, especially with market orders. Since some trade execution statistics are based on small order volumes, one should study more periods of history in order to draw statistically robust conclusions.

For those seeking a more league-tablesque summary, the following graphs may be more satisfying. These graphs list the most and least favorable price improvement performances for combinations of individual ETFs and market centers. Market orders and marketable limit orders are shown separately, as their overall magnitudes of WAPI are different.

One of the market centers should be familiar by now (if not, this WSJ article might help). For those seeking a comparison, the same graphs for April are available for market orders and marketable limit orders.

Clearly, May was an exceptional month as highlighted by the price impact of ETF trades. In such challenging and volatile markets, is the quest for price improvement worthwhile, or would a market center be better off routing an order elsewhere? For investors, the dilemma is even more complicated: which ETF, order type, and order quantity would work best? There can be no assurance that if Flash Crash II ever occurs, the quality of price improvement will follow similar patterns.

Using Leveraged ETFs to Trade during Circuit Breakers

2010-07-27T16:59:00.000-07:00

Subtitle: Intraday Correlations between ProShares ETFs and Broad Market Indices

On June 30, 2010, the SEC announced its intention to expand the circuit breaker pilot program to many more stocks and, for the first time, ETFs. Public comments have expressed concern that, when applied to ETFs, circuit breakers may themselves create market disruptions. If a circuit breaker is triggered, whether appropriately or erroneously, for a widely-traded ETF, ~~HFT firms~~ major market participants may pause trading and deprive the market of liquidity when it is most needed. Once an ETF selected for the pilot program triggers a circuit breaker, firms which trade the basket of underlying stocks may also step away for five minutes until trading in the ETF resumes. In such a scenario, the impact on the liquidity of stocks underlying a halted ETF will demonstrate whether the circuit breaker program meets its objectives in practice.

Not all ETFs have been selected for the circuit breaker pilot program. NYSE Euronext compiled a list of 344 ETFs ("piloted ETFs"), as reported by Index Universe, utilizing a volume cutoff:

"NYSE Euronext, the parent company of the New York Stock Exchange, developed the list of 344 ETFs in consultation with other major exchanges and the Financial Industry Regulatory Authority.

"NYSE Euronext winnowed down the universe of U.S. ETFs by excluding products whose average daily trade volume was less than $2 million worth of shares, it said in a press release."

This criteria for selected ETFs produces some interesting and potential trading scenarios. For example, SPY and IVV, two ETF's which track the S&P 500, are included in the proposed expansion of the circuit breaker program. Therefore, if one of these two ETFs triggers a circuit breaker, will the market simply move to trade the other ETF until five minutes passes? What are the odds that both SPY and IVV will simulateously trigger circuit breakers? (This is not a rhetorical question.)

Given the complex trading relationships between an ETF and its underlying stocks, concerns about market disruption resulting from a circuit breaker are difficult to prove in theory and potentially difficult to quantify. A further review of the list of ETFs proposed for the circuit breaker pilot program reveals another interesting scenario: if a broad-market ETF triggers a circuit breaker, can traders utilize leveraged versions of the ETF to continue trading through the duration of the circuit breaker?

To address that possibility, we start with a summary of ETFs which may serve as substitutes (or proxies) for three broad market ETFs: SPY, QQQQ, and IWM.

Those ETFs participating in the circuit breaker pilot program are only unleveraged (long and short) versions of their underlying indices. The obvious choice for trading through the duration of a circuit breaker would be to use a leveraged (long or short) ETF, sponsored by ProShares. The remainder of this analysis studies how effectively these ProShares ETFs followed their stated leverage ratio during 2010 Q2.

First a definition of the term "leverage ratio" as utilized in this analysis. To take an example, the ProShares Ultra S&P 500 (SSO) has a stated objective of going up or down twice as much as the S&P 500 index, on an intraday basis. Hence, the stated target leverage ratio for SSO is +2. Conversely, the stated target leverage ratio for ProShares UltraShort S&P 500 (SDS) is -2 because SDS moves in the opposite direction to the S&P 500. By looking at the returns of leveraged long/short ETFs over 5-minute intervals during 2010 Q2, we compare their returns to the underlying index by utilizing linear regression analysis. These regressions are constrained to have a zero alpha (zero intercept) so that the resulting beta (slope) provides a useful (albeit not perfect) measure of the leverage ratio. Each regression compares a single ETF to its underlying index, ignoring any 5-minute intervals when such ETF did not trade. Otherwise, volume has no impact on the regressions. By constraining the alpha to zero, the following graphs need to display only the slope (termed the "observed leverage ratio").

The closer an ETF trades to its stated target leverage ratio, the better such ETF may serve as a proxy if another related ETF triggers a circuit breaker. How well might various leveraged long/short ETFs of the S&P 500 index serve as proxies to SPY, IVV, or SH? The following graph shows the consistency of the leverage ratio for every trading day furing 2010 Q2.

(For convenient viewing throughout all graphs, those ETFs which will be subject to circuit breakers have markers with yellow centers. For graphs pertaining to the S&P 500, the iShares ETF is excluded so that the higher-volume SPDR ETF is easier to view. On graphs plotting the observed leverage ratio over time, a black line represents the daily standard deviation of 5-minute returns to help compare deviations in the observed leverage ratio to underlying index volatility.)

On a daily basis, the leveraged ETFs appear to trade according to their stated target leverage ratios. As the target level rises to 2x and 3x, the observed leverage ratio for both long and short ETFs exhibits greater variability around the target level. Perhaps the efficiency of instruments used to gain leverage suffers as leverage rises. Liquidity might also play a role. Leveraged ETFs typically have lower trading volume than their corresponding long unleveraged ETFs (discussed in detail further below).

Can one draw the same conclusion by looking at the observed leverage ratio over intraday 5-minute intervals? In this graph, the leverage ratio is computed through a linear regression utilizing all trading days during 2010 Q2 but for a specific 5-minute interval during each trading day.

During certain times of the day, on average, the observed leverage ratio tended to deviate from the stated objective. Most likely (especially between 2:30 and 3:00) these deviations reflected aberrant activity on specific days (e.g. May 6), as opposed to repetitive behavior during most trading days. Interestingly, the interval from 9:30 to 9:34, where 5-minute volatility is highest overall, shows the largest discrepancy in observed leverage ratio. Currently, the SEC does not apply circuit breakers during the beginning and end of regular trading hours (9:30 to 9:45 and 3:45 to 4:00).

Switching from a time-based perspective, the next graph shows the observed leverage ratio according to the return in the underlying index during 5-minute intervals.

(For clarification, the horizontal axis shows return bands, each spanning 0.10%. The return band labelled -0.30% includes any 5-minute intervals when the underlying index dropped by 0.3000% to 0.3999%. The return band labelled +0.30% includes any 5-minute intervals when the underlying index rose by 0.2001 % to 0.3000%. The return band labelled 0.00% includes any 5-minute intervals when the underlying index dropped by -0.0999% to 0.0000%. Furthermore, since the return bands are based on the underlying index, and not the ETFs, circuit breakers might have triggered if they had been in effect for ETFs at that time.)

All ETFs based on the S&P 500 traded close to their stated target leverage ratios as long as the underlying index did not rise or fall more than 0.40% within any 5-minute interval. As the underlying index became more volatile, the observed leverage ratio tended to shrink. Note that as the magnitude of change increases, the number of historical data samples drops, which may in turn reduce the regression quality and increase the standard error of the slope (beta).

In the same manner, we can determine how well various leveraged long/short ETFs of the NASDAQ 100 index served as proxies to QQQQ or PSQ. The following two graphs plot the observed leverage ratio over time, first daily followed by intraday, and roughly follow the patterns seen in the graphs for the S&P 500. For the ProShares triple-leveraged ETFs, SQQQ and TQQQ, the observed leverage ratios deviated from the stated target levels more so than for the corresponding ProShares ETFs tracking the S&P 500 .

According to the return in the NASDAQ 100 during 5-minute intervals, the following graph shows that the observed leverage ratios for the related ETFs tracked their stated target levels most of the time. Only when the Nasdaq 100 index rose or fell by more than 0.40% did the observed leverage ratio begin to decline. In this case, the spike in standard error which we observed for the S&P 500 is not present among the ETFs which track the NASDAQ 100.

Finally, we can determine how well various leveraged long/short ETFs of the Russell 2000 index serve as proxies to IWM or RWM. The following two graphs plot the observed leverage ratio over time, first daily followed by intraday.

In this case, the observed leverage ratio of the ETFs fluctuated even more from their stated objective than for the ETFs which track the NASDAQ 100. Could these fluctuations be the result of lower trading volume in the ETF and its component stocks? Additional analysis further below should address this question.

According to the return in the Russell 2000 during 5-minute intervals, the following graph shows that the observed leverage ratios tracked (and many times exceeded) the stated target levels most of the time. Only when the Russell 2000 index rose or fell by more than 0.50% did the observed leverage ratio decline significantly.

Thus far, the leveraged ETFs corresponding to the S&P 500, NASDAQ 100, and Russell 2000 appear to maintain an observed leverage ratio consistent with expectations, except during sharp price movements. However, brief periods of sharp price movements would be most likely to trigger circuit breakers, outside of erroneous trading activity. Might the participation of unleveraged ETFs in the circuit breaker pilot program prompt a change in how leveraged ETFs behave during extreme price volatility? In other words, can leveraged ETFs attract more volume, and in turn achieve observed leverage ratios consistent with stated levels, if any of their corresponding unleveraged ETFs triggers a circuit breaker?

The following three graphs compare the daily volume of the ETFs listed above (except for IVV). When selecting ETFs to include in the pilot program, the SEC focused on the highest-volume ETFs for the three broad-market indices. In addition, the corresponding unleveraged inverse ETFs had respectable volumes but lower than some of their leveraged counterparts. (Note the log scale for the vertical axis.)

In fact, for all three indices, the double-leveraged ETFs (both long and short) traded with materially higher volumes than their corresponding unleveraged short counterparts. The same conclusion can be drawn from the following three graphs, which plot average intraday volume during 5-minute intervals according to the 5-minute return in the underlying index. (Note that the graph plots average, not total, 5-minute volume because not all ETFs traded during every interval in every trading day of 2010 Q2.)

Once again, the double-leveraged ETFs consistently traded in greater volumes than their unleveraged short counterparts but less than their unleveraged long counterparts. Furthermore, these three graphs exhibit a very consistent "smile" shape, indicating that 5-minute interval volume does increase as 5-minute price movement (positive or negative) increases. While the overall shape is intuitive, the consistency among all ETFs (across different leverage ratios and long vs short) demonstrates the potential for a leveraged ETF to attract more volume should an unleveraged long or short ETF trigger a circuit breaker. However, these graphs do not and cannot prove how the observed leverage ratio of leveraged ETFs might behave during such a market scenario in the future.

In conclusion, the above analysis demonstrates some key aspects of leveraged ETFs which require careful consideration during normal and volatile periods of intraday trading.

1. As intraday (5-minute interval) price volatility of an underlying index increases, the observed leverage ratio drops significantly below stated levels. If an underlying index rises or drops by more then 0.50% in a 5-minute interval, then the leveraged ETFs may not trade at a predictable correlation to the undering index within the same interval.

2. As intraday price volatility of an underlying index increases, the corresponding volume also increases, confirming that ETFs are popular among traders who seek to rapidly gain market exposure or hedge existing positions. Both leveraged and unleveraged ETFs exhibit the same pattern of trading volume, which demonstrates that a leveraged ETF may be a viable proxy for an unleveraged ETF which has triggered its circuit breaker.

3. Both the NYSE and SEC appear to have postponed determining how leveraged ETFs may be included in the circuit breaker program. Most likely, the embedded leverage of such ETFs increases their chance of triggering a circuit breaker at times when other similar ETFs may trade inside their trigger levels. Such a potential scenario is further complicated by past behavior of observed leverage ratios, as shown above. (Currently, the SEC is not granting exemptive relief to ETFs which request to make significant investments in derivatives.)

4. The SEC is already concerned with the performance of leveraged ETFs, specifically due to their daily resets (which should not be pertinent to intraday leverage ratios). Might the exercise of identifying circuit breaker trigger levels create further scrutiny on a product where FINRA states "typically are not suitable for retail investors who plan to hold them for more than one trading session, particularly in volatile markets"?

Once the proposed ETFs are rolled out into the circuit breaker pilot program, leveraged ETFs will be put to a potentially new test . How might leveraged ETFs behave empirically should a related ETF trigger its circuit breaker?

Are Money Market Funds like CDOs?

2010-07-09T16:26:00.000-07:00

A comparison of a money market fund to a collateralized debt obligation ("CDO") or structured investment vehicle ("SIV") may readily seem far-fetched, but given the complexity of risks and evolving regulations, one may appreciate the relevance of their similarities and resulting implications.

Why does such a comparison even matter, and why now? Recent amendments to Rule 17g-5, under the Securities Exchange Act of 1934, seek to improve the integrity of ratings issued by NRSROs (aka rating agencies) for structured finance products. What is a structured finance product under Rule 17g-5? According to the Federal Register (Vol. 74, No. 232):

The Commission intends this provision, which mirrors, in part, the text of Section 15E(i)(1)(B) of the Exchange Act (enacted as part of the Rating Agency Act), to cover the full range of structured finance products, including, but not limited to, securities collateralized by static and actively managed pools of loans or receivables (e.g., commercial and residential mortgages, corporate loans, auto loans, education loans, credit card receivables, and leases), collateralized debt obligations, collateralized loan obligations, collateralized mortgage obligations, structured investment vehicles, synthetic collateralized debt obligations that reference debt securities or indexes, and hybrid collateralized debt obligations.

In brief, a structured finance product includes static or actively managed pools of corporate loans and residential mortgages, among a number of other fixed-income securities and synthetic instruments.

The shortcomings of NRSRO-issued ratings of structured finance products is one of the commonly cited contributors to the Great Recession. However, NRSROs also play an important role in the credit quality of money market funds: Rule 2a-7 under the Investment Company Act of 1940 contains provisions which refer to NRSRO ratings for determining investment eligibility. Over the recent months, the SEC considered whether to allow fund managers to rely on ratings under Rule 2a-7. That debate led to the preservation of references to ratings in the investment process but added the requirement to use multiple NRSROs whenever possible and additional annual NRSRO quality assessments by the managers. According to a Mayer Brown Securitization Update (March 24, 2010):

Money market funds are regulated pursuant to rule 2a-7 under the Investment Company Act. Although recently amended to (among other things) remove some of its references to ratings, rule 2a-7 continues to rely to a substantial degree on ratings from NRSROs in defining minimum credit standards for fund investments. Some important rating standards in the rule are phrased in terms of specified ratings from the “Requisite NRSROs.” Ordinarily, “Requisite NRSROs” means any two NRSROs from a list of at least four that must be designated annually by the fund’s board of directors, but it can mean just one of the designated NRSROs if only one of them maintains a rating of the subject security.

NRSROs expanded their expertise beyond rating individual corporate bonds and structured finance products: they rated investment vehicles which held such rated instruments. Among such investment vehicles, the most often cited examples are CDOs and SIVs. Another less-publicized but more familiar example is the money market fund. How many money market funds do NRSROs rate? Using Standard & Poor's as an indicative NRSRO (throughout the remainder of this analysis), approximately 100 US money market funds were rated representing $283.9 billion of AUM, reported as of March 31, 2010. (The total AUM of US money market funds was approximately $2.8 trillion, according to the ICI as of June 30, 2010.)

The fact that certain types of investment vehicles are rated and invest in rated securities does not mean that they are all similar in credit risk posed to investors or sensitivity to the default risk in underlying investments. According to S&P in their article entitled "CDOs: An Introduction To CDOs And Standard & Poor's Global CDO Ratings" (June 8, 2007), CDOs cannot be deemed similar to mutual funds:

In a mutual fund, all investors share in the risks and rewards of the investments equally. In a CDO, the transaction is structured with different classes of notes, each having a different risk/reward profile. If any of the assets in the underlying asset pool default, the lowest note class (typically referred to as the CDO equity class) will suffer a loss. As losses increase in the asset pool, then the other classes of notes may also be affected.

This perspective differentiates CDOs from mutual funds on the key basis that mutual funds issue one class of security (generally an accurate assumption, with a caveat noted later) versus the multi-tranche liabilities of CDOs. However, this reasoning does not speak to credit and market risks from the underlying portfolio of fixed-income securities. In the same article, S&P further elaborates:

Investors can look at the asset portfolio to get a sense of the companies in the pool, how the companies are rated, and their line of business. Standard & Poor's also provides portfolio benchmarks of each CDO. The benchmarks include: Weighted average rating (WAR): The average rating on the companies in the pool; Weighted average maturity (WAM): The average life of assets in the portfolio; Default measure (DM): The expected annual average default rate of the portfolio; Variability measure (VM): The deviation around the average portfolio default rate; and Rated overcollateralization (ROC): The risk-adjusted collateral available to support a rated tranche.

In summary, S&P assesses the collective risks of underlying securities in a CDO based on their ratings, maturity profile, and expected default rates (with a certain amount of variability). Even if a CDO issues only one class or tranche of debt (much like a money market fund issuing one share class), the last benchmark, Rated Overcollateralization, is still relevant but simpler to analyze than for a multi-tranche structure.

The market-value CDO ("MV-CDO") is one type of CDO which serves as a useful and representative example of a rated investment vehicle. According to a criteria article entitled "CDO Spotlight: Criteria For Rating Market Value CDO Transactions
" (September 15, 2005), S&P provides the following definition of a MV-CDO:

Market value CDO transactions involve SPEs designed to purchase and actively manage a diversified pool of financial assets. Structurally, they are similar to cash flow CDOs in that their capital structures consist of a series of debt and equity classes. The primary difference between cash flow CDOs and market value CDOs is the nature of the risk passed from the pool of assets to the investor. As the name suggests, a market value CDOs risk is linked to the market value of the assets within it. The risk in cash flow CDOs is based on the pure credit risk of the assets, measured by the assets' ratings.

In addition to the risks attributed to the generic cash flow CDO, the MV-CDO includes a distinct exposure to fluctuations in the market value of underlying securities.

How does S&P characterize the risks of securities held by money market funds?

In a criteria article entitled "Fixed-Income Funds: Process And Overview" (February 2, 2007), S&P lists the following key risks within money market funds:

A Standard & Poor's Principal Stability fund rating, also known as a money-market fund rating, is a current opinion of a fund's capacity to maintain stable principal or net asset value. When assigning a Principal Stability rating to a fund, we evaluate the creditworthiness of a fund's investments and counterparties, the market price exposure of its investments, sufficiency of the fund's portfolio liquidity, and management's policies and overall ability to maintain the fund's stable net asset value (NAV) by limiting exposure to loss. In our view, funds that seek to maintain a stable NAV should be managed conservatively in regards to average maturity, credit quality, and liquidity and should follow well-defined guidelines and investment policies (such as those specified within SEC Rule 2a-7 guidelines).

In another criteria article entitled "Fixed-Income Funds: Market Price Exposure" (February 5, 2007), S&P further elaborates on key risks to the stable NAV of money market funds:

By far, the most complex part of money market fund analysis is judging a fund's sensitivity to changing market conditions. Absolute stability of net asset value (NAV) is a myth perpetuated by the amortized cost method of pricing securities. All fixed income securities are subject to price fluctuations based on the following: interest rate movements; maturity; liquidity; credit risk or perceived credit risk; and the supply and demand for each type of security. These factors are just as true for money market funds as for longer-term fixed-income mutual funds.

In summary, S&P assesses the collective risks of underlying securities in a money market fund based on their credit risk and credit quality (i.e. ratings), maturity profile, liquidity, and market price exposure. Do these risks sound familiar?

While there are several differences between CDOs and money market funds, once one compares how an NRSRO analyzes the risks of securities in the underlying asset pools, those differences begin to look more nominal and less substantive. Based on the above highlighted words, the risks underlying market-value CDOs and money market funds appear quite similar. Both investment vehicles predominantly, if not exclusively, hold fixed-income securities; hence, their risks should be assessed in a very similar manner.

The following diagram shows the similarities between the underlying securities in CDOs and money market funds.

This diagram classifies the structured investment vehicle (SIV) and CDO as comparable investment vehicles, both issuing multiple classes of debt securities. Several SIVs and CDOs issued short-term (commercial paper) tranches which, when rated A-1+ (or equivalent) by an NRSRO, were considered eligible for investment by money market funds. Interesting to note, those highly-rated short-term tranches of CDO/SIVs were held by some money market funds which were also highly-rated by the same NRSROs (too many examples exist to mention only a few judiciously).

As noted above, S&P stated that the single-class shares of money market funds distinguish themselves from multi-tranche debt securities typically issued by CDOs. Indeed, CDOs issued short-term and long-term debt tranches as well as an equity tranche (deemed the first-loss position). Do money market fund shareholders absorb any and all losses which may result from adverse performance in underlying securities, after deducting for expenses charged by the portfolio manager to act in its capacity? If you ask the shareholders of The Reserve Fund after Lehman Brothers filed for bankruptcy, the answer would be a resounding "yes". If you ask Federated, Charles Schwab, or BlackRock, based on their earnings reports, the answer would probably be "no" only because those firms (and many others) waived asset management fees in order to prevent their money market funds from "breaking the buck". Might such fee waivers be equivalent to a form of contingent capital injected into an otherwise loss-making business? (Posing this question does not seek to undermine the appropriateness of the fee waivers in the first place, rather to point out that capital was injected to avoid a loss to fund shareholders.)

In Comparison B, the equity tranches for CDOs and money market funds are added to Comparison A above.

At this point, one may still disagree with the premise that CDOs and money market funds, while regulated differently and usually sold to different types of investors, share similar types of risks from underlying fixed-income securities. When looking at the recent amendments to Rule 17g-5, this comparison can be extended to include a new dimension. Through Rule 17g-5, the SEC sought to mitigate the effect of conflicts of interest created by the manner in which NRSROs are engaged and compensated. According to the same Mayer Brown publication referenced earlier:

Among other changes, the Commission amended rule 17g-5 to facilitate unsolicited ratings from NSROs that were not hired by issuers, sponsors, or underwriters to rate particular asset-backed securities (ABS) and other structured finance products by enabling these non-hired NRSROs (Accessing NRSROs) to access the same rating-related information as “Hired NRSROs.”.

Do these conflicts of interest, deemed to have compromised the quality of past ratings on structured finance products, have any relevance to money market fund ratings? Building on Comparison B, the scope of Rule 17g-5 is highlighted in yellow in Comparison C.

Money market funds, already impacted by changes to Rule 2a-7 regarding the use of ratings, need to be mindful of potential, yet remote, side-effects of Rule 17g-5. With respect to the unsolicited rating of a structured finance product, Mayer Brown further elaborates:

When a fund relies on ratings from two of its designated NRSROs (which we would expect to occur in the vast majority of cases), no issues should arise from any unsolicited ratings. By definition, a security has the specified ratings from the Requisite NRSROs as long as any two of the designated NRSROs have provided the minimum ratings, regardless of what other ratings other designated NRSROs may have provided, whether on a solicited or unsolicited basis. However, if a fund seeks to satisfy the rating requirement based on just one rating, believing that only one NRSRO has rated a Structured Finance Product that the fund is buying, the fund could find itself unexpectedly holding an ineligible security because, unknown to the fund, another of its designated NRSROs rates the Structured Finance Product on an unsolicited basis. This would not generally require fund to dispose of the affected Structured Finance Product. Also, it should seldom occur, since most Structured Finance Products have two ratings from NRSROs, and the two NRSROs providing those ratings seem likely to be included in a purchasing fund’s designated group.

In summary, if an NRSRO issues an unsolicited rating for a structured finance product which is lower than the rating issued by designated NRSROs utilized by the money market fund portfolio manager, corrective action may be required. The changes to Rule 17g-5 are too recent to empirically assess whether this risk will be a material concern. If an NRSRO issues an unsolicited rating for a specific structured product security which is ~~meaningfully~~ lower than the ratings issued by other NRSROs, would the marketplace question or doubt the validity of the higher rating on the same security? Would the portfolio manager of a money market fund be able to argue against utilizing the lower unsolicited rating?

However, the real key take-away question is:

Should an NRSRO proactively issue an unsolicited rating of a money market fund, especially given any significant differences among NRSRO's in the perception of risks among the underlying securities?

The SEC has clearly targeted structured finance products as vulnerable to "ratings arbitrage". Since money market funds are not categorized as structured finance products (such as CDOs), how are their ratings (e.g. S&P's Principal Stability Fund Rating) protected from the conflicts of interest which prompted the SEC to amend Rule 17g-5?

Currently, a large share of US money market funds (possibly over 90%, based on S&P's coverage compared to ICI's statistics) are not even rated by S&P. Of the US money market funds rated by S&P, their average maturity of underlying securities (33 days as of March 31, 2010) is well under the SEC-mandated maximum of 60 days, indicative of the conservatism among those funds which request and pay to be rated. If a money market fund holds a portfolio which has an average maturity of 55 days, would the fund sponsor be less likely to pay an NRSRO for a rating (due to the higher risk of a potential downgrade should average maturity extend beyond 60 days)? In such a scenario, should an NRSRO issue an unsolicited rating?

After pondering this question, one might want (or not want) to consider an alternative question concerning accounting treatment: If money market funds are similar to CDOs, then should asset managers be required to consolidate money market funds onto their balance sheets under FAS 166/167 aka ASC 860? Under proposed rules, CDO managers already face a material possibility of consolidating their investment vehicles (see FASB Comment Letter Summary).

Additional Notes:

1. The above analysis is not exhaustive but rather seeks to raise awareness, through a concise discussion, of specific sources of potential (yet probably remote) risk in money market funds.

2. In order to comprehensively interpret the excerpts cited above, review the context from which the excerpts originated by clicking on the links to original source documents. Free registration may be required for certain source documents.

3. Certain words in the excerpts above are highlighted in yellow by Fundometry, not the original authors of the excerpts.

4. NRSROs other than S&P may have materially different perceptions of the sources of risk in structured finance products and money market funds.

Drilling Down into the Flash Crash, ETF Edition

2010-06-14T13:32:00.000-07:00

Many media reports have commented on the severity with which stock prices collapsed during the afternoon of May 6, 2010 ("Flash Crash"). The most surprising casualty from the Flash Crash are ETF's. In general, ETF prices fluctuate consistently with the underlying index (for passive indexed, not active, portfolios) due to sufficient trading volume in the ETF itself and/or the ability for authorized dealers to use baskets of the underlying securities to profit from pricing discrepancies between the ETF and its constituent securities. ETF Trends wrote an article (referenced below) discussing the mechanisms for trading large blocks of ETF's either directly or indirectly via its constituent securities.

While ETF's justifiably raised concerns in light of the Flash Crash, the media has portrayed ETF's as a single asset class in this context. Some Wall Street Journal articles (referenced below) have presented examples of specific ETF's which suffered from extreme volatility that day. Although most of the trades which the exchanges cancelled on the evening of May 6 occurred in ETF's, many other ETF's did not incur enough price volatility to require trades to be cancelled. Few, if any, reporters have drilled down to determine which types of ETF's, and their sponsoring firms, performed better or worse on May 6.

This article attempts to reveal important distinctions among US-listed ETF's during the Flash Crash. In order to simplify the analysis, certain ETF's have been excluded. Those ETF's which utilize derivatives to achieve leverage (funds named with "ultra", "2x", "3x", and "double") are excluded in all comparisons because, by definition, their volatility is supposed to be higher than the general market, which would be confused with the exceptional volatility of un-leveraged ETF's on May 6. For similar reasons, inverse ETF's (funds named with "bear" and "short") are excluded. While the market was falling precipitously on May 6, inverse ETF's, by definition, should have not surprised investors in the same manner as long-only ETF's. Studying a single date of performance gives a limited scope of observations, but, nevertheless, readers can draw some very useful conclusions to help make future decisions on how to utilize ETF's in short-term trading or portfolio management strategies.

The simplest way to focus attention on the ETF's which exhibited unusually high volatility is to break down performance according to broad sectors. In the following table, we observe some sectors with severe price declines on May 6 regardless of high or low trading volume. The extreme magnitude of downside volatility is measured by how low prices fell in relation to the closing price that day ("Low-to-Close Difference", or "LCD"). For example, an ETF which traded as low as $1 but closed at $10 on May 6 would have an LCD of 90%. In order to better reflect the overall impact on market participants and investors, each ETF's LCD is weighted by the trading volume on May 6 to arrive at the Weighted Average Low-to-Close Difference ("WA-LCD"). Admittedly, a more accurate weighted average would consider the trading volume during the brief time during which most of the intraday volatility occurred, but that data requires more time to aggregate and process (perhaps a worthwhile objective for a future article on Fundometry).

As expected, ETF's invested in equities, including Asset Allocation, suffered from the most severe price drops. Large Cap (US) Equity ETF's performed better than Mid/Small Cap (US) Equity ETF's, which is consistent with the history of higher observed volatility in the prices of smaller companies. While the Asset Allocation sector exhibited the largest overall drop in price, that performance spans a broad range at the level of specific sectors (Moderate Allocation, Target Date, etc. as seen further below). Perhaps most significant, several sectors should be credited with avoiding the extreme price volatility of May 6: Taxable Bond, Municipal Bond, Global Bond, Currency, and Commodity ETF's. Finally, even though a broad sector may exhibit modest volatility overall, a single ETF within the sector may have performed much more poorly, as evidenced by the rightmost column "Worst Single ETF Low-to-Close Difference".

The next logical step in this analysis is to compare sectors which comprise the above broad sectors. The tiering of performance across specific asset classes and investment styles is quite apparent in the following table.

Clearly, the Small Cap Equity and Mid Cap Equity sectors rank among the worst performing sectors on the basis of the lowest trading price. Among the Small/Mid Cap group, the Small Cap Blend Equity sector held up by and far the best, with a weighted average lowest trading price 7.04% below the weighted average closing price. Industry-specific sectors suffered some excessive volatility, led by Telecom Sector Equity on relatively low volume. In order to judge the relative performance on most sectors, the Large Cap Blend Equity (which includes ETF's tracking the S&P 500) may be the benchmark. Sectors investing overseas performed relatively better. Among fixed income ETF's, the High Yield Bond sector exhibited the most downside volatility, trading 7.00% below the closing price on a weighted average basis. Relative to most sectors, the remaining bond sectors, as well as Commodity, Currency, and Target Date sectors, suffered limited downside volatility during the Flash Crash and exhibited the least negative WA-LCD figures.

Different ETF's sponsors offer products spanning various sectors. Therefore, when drilling further to the sponsoring firm level, the following tables each compare performance within a single broad sector. The term "ETF Complex" includes the ETF's offered by one firm, or within one family, which fall within the specified broad sector, and each WA-LCD is computed accordingly. The first table below compares ETF Complexes which offer Large Cap Equity portfolios.

At the ETF Complex level, trading volume shows a stronger correlation to the WA-LCD metric. ETF's from FaithShares and Grail have the lowest trading volume among their peers, perhaps to the extent that these ETF's did not trade much during the most volatie period on May 6. The leading ETF Complexes by volume - PowerShares and State Street SPDR's - performed consistently with the broader market primarily because of their high-volume products - QQQQ and SPY, respectively. (Remember, WA-LCD is weighted by trading volume on May 6.) The next most-traded ETF Complex - iShares - suffered a disappointing -44.82% weighted average drop, relative to the closing price on May 6, due to underperformance in their S&P 500 and Russell 1000 offerings. The remaining ETF Complexes which suffered disproportionate price drops also displayed very modest trading volumes relative to peers.

The comparison of ETF Complexes proceeds with the Mid/Small Cap broad sector.

Quite contrary to its performance in the Large Cap Equity broad sector, iShares exhibited the smallest overall price drop during the Flash Crash due to the high trading volume of IWM, which tracks the entire Russell 2000 index. The second-most heavily traded ETF Complex, State Street SPDR's, also held up relatively well against its peers primarily due to the high trading volume of MDY, which tracks the entire S&P MidCap 400 index. Neither of the next two most actively traded complexes - Vanguard and PowerShares - offered a specific Mid/Small Cap ETF with high volume and strong performance, resulting in significantly negative WA-LCD levels.

According the to following table, State Street SPDR's well-known family of sector ETF's, based on the S&P 500 constituents, performed respectably compared to their peers, probably due to their large share of trading volume among sector-focused equity ETF's. Van Eck's Market Vectors ETF's also suffered limited volatility, due to the outperformance of some commodity-sector equity ETF's within that complex. Likewise, the ETF Complex with the least negative WA-LCD among sector-focused products was Jefferies, with its equity portfolios based on Thomson-Reuters/Jefferies CRB indices.

On that note, the next comparison drills down into the Commodity broad sector, consisting of ETF's and ETN's which track physical commodities. Unsurprisingly, this group of ETF Complexes suffered modest declines during the Flash Crash. The PowerShares Deutsche and UBS E-TRACS complexes suffered the largest price declines, mostly due to isolated drops in specific ETF/ETN's over low volume. The most traded complexes within the Commodity broad sector, iShares and United States, exhibited consistent performance across their specific ETF's. For ETF/ETN's which give investors exposure to physical commodities, the volatility on May 6 can hardly be described as a Flash Crash.

In addition, the performance of ETF Complexes which track taxable bond portfolios exceeded the performance of their equity peers, as seen in the table below. The overall performance of PowerShares, State Street SPDR, and iShares complexes suffered from exposure to high-yield bond portfolios, specifically PHB, JNK, and HYG respectively. Otherwise, price declines in taxable bond ETF's throughout all of these complexes were modest. As should be expected, underlying ETF portfolios with longer average durations mostly exhibited greater price declines. Very few signs of a Flash Crash existed for ETF's which track taxable bond indices, and even more so for municipal bond indices.

(Similar tables for other broad sectors are available by popular demand. Interested readers may submit requests in the comments section below.)

The above comparisons clearly show that several ETF's, and some entire sectors, did not suffer from excessive volatility on May 6. Undoubtably, volatility across all market sectors was higher than normal that day. Even within complexes, the worst WA-LCD levels show some outlier downside activity in otherwise sheltered sectors. However, the market should avoid generalizations when assessing liquidity and price discovery of ETF's. Depending on its underlying portfolio, an ETF possesses a unique set of risks which may help or hurt performance during a Flash Crash scenario.

During the Flash Crash, why did some ETF's suffer less downside price risk than others, even among those which track similar indices or hold similar portfolios? The above discussion does not provide many insights which would help answer that question. Based on a yet-to-be-published analysis, no single set of factors would have predicted which ETF's outperformed on May 6. Yet to be released data covering trading activity in May might shed further light. Stay tuned.

Related articles:

How to Trade Large Blocks of ETFs Efficiently (ETF Trends)

Danger: Falling ETFs (Wall Street Journal)

A 'Flash Crash' Lesson (Wall Street Journal)

Fidelity Customers and iShares ETF's: the Impact of Commission-Free Trades

2010-05-28T15:36:00.000-07:00

On February 2, 2010, Fidelity announced that their clients would be able to buy and sell a select group of 25 iShares ETF's commission-free, in addition to Fidelity's NASDAQ Composite Index Tracking Stock. The move follows a decision by Schwab offer commission-free trading on a select group of proprietary ETF's. Fidelity offers a larger number of commission-free ETF's than Schwab, plus the strong reputation of iShares.

In an earlier analysis of Schwab's commission-free arrangement, we studied the quality of trade execution at UBS, a major venue (technically, market center) for executing orders on Schwab ETF's. Even though UBS executes a large volume of orders for Schwab ETF's, other "ETF complexes", such as State Street, Vanguard, and PowerShares, exhibited better trade execution than the Schwab complex. This analysis of the iShares trade execution complex will follow a similar theme.

Morningstar appropriately asks in their commentary who really benefits more from the Fidelity-iShares arrangement: Fidelity or iShares? Their assessment concludes both parties win. Fundometry expands the audience to include investors who enter those commission-free orders. By looking at iShares ETF's which trade commission-free and those which do not (most of the iShares ETF's), we can compare execution quality with respect to the commission as well as time (before and after February 2010).

Fidelity is not a leading venue for trading iShares, or some of the other major ETF complexes. The following two graphs show how much volume was executed at NFS (Fidelity) versus other market centers. ETF complexes are selected from the top five traded ETF's, in terms of share volume over six months, at each market center. (Two graphs are better than one because the market centers range widely in terms of volume, hence the horizontal scales are different to accommodate the higher and lower volumes.) Someone entering an order through Fidelity to buy or sell an ETF (not only those from iShares) may hope the order is routed to another market center which handles a larger order flow, hence sourcing more liquidity and hopefully achieving better price execution. For those iShares which trade commission-free at Fidelity, the graphs show the volume for those respective tickers in a lighter shade, even though the actual trades may have incurred a commission if entered from brokerages other than Fidelity.

Interestingly, if an ETF order is entered from Fidelilty and gets routed to NFS, there appears to be a ~~near certainty~~ strong probability that the order will be executed at NFS. The following two graphs compare how much of the order flow was executed at the specified market center as opposed to being routed to another market center where a better price existed. NFS is not the only brokerage with a tendency to keep orders internal; Knight, Citadel, and Barclays also show no red bars.

Although NFS handles a relatively small volume of orders for iShares ETF's, order flow does not appear to route to other market centers which may offer deeper liquidity. Why? Perhaps because NFS does not need to route an order to another venue if it can execute at a price better than or equal to the National Best Bid and Offer (NBBO). Unfortunately, the following graphs, utilizing the same source data as the above graphs, do not readily support that explanation.

The above two graphs show the breakdown of share volumes according to execution quality during each month (October 2009 through March 2010): improvement (better than the quote), at the quote, and outside the quote (degradation). NFS (Fidelity), Knight, Citadel, and Barclays tend to execute orders themselves rather than route to other market centers (as stated earlier). Over the sampled period, Knight and Citadel exhibited noticeable price improvement for the tickers which Fidelity customers can trade commission-free. Barclays achieved price improvement to a lesser extent but executed a decent amount at the quote. In significant contrast, NFS showed a worsening trend in terms of execution quality. NFS substantially exceeded its competitors in executing outside the quote, a trend which dramatically increased in January and persisted through March. Why would Fidelity offer to not charge commissions for iShares ETF's for which it could not achieve better execution (even when charging commissions)?

Perhaps some more insight can be derived from looking at the other iShares ETF's which Fidelity still charges its customers to trade.

Once again, Knight and Citadel demonstrated a degree of price improvement, outpacing Barclays and NFS. While Fidelity charged a commission for trading the ETF's sampled in the above two graphs, the extent of price degradation was even higher than those ETF's which trade commission-free. In March, trades executed at NFS exhibited a higher rate of price improvement than in preceding months, and trades executed outside the quote subsided yet remained at a high level. Given that trading activity at these market centers are compared for the same group of ETF's and over the same period, the above graphs do not explain how Fidelity's customers (trading iShares ETF's commission-free or not) are winning with respect to execution quality.

Corresponding comparisons for some other major ETF complexes paint a similar picture: Direxion, PowerShares, and ProShares.

While many trades executed outside the quote, to what extent was price impacted? In other words, how far outside the quote did NFS fill orders? Since price impact (in dollars per share) is disclosed separately for trades where price improved and degraded, a weighted average, based on their respective share volumes, can be computed. The following graphs display the share volume in each market center for iShares ETF's which trade commission-free at Fidelity and the corresponding weight-average price impact ("WAPI").

In terms of WAPI, NFS once again stands out from its peers. NFS consistently achieved adverse (negative) overall price impact throughout the six-month period. As indicated above, the trend worsened after December. A peer with comparable volume, Barclays, also exhibited adverse WAPI but to a lesser extent than NFS and without significant trend. Among the largest market centers, BATS and Direct Edge exhibited adverse WAPI but to a much lesser extent than NFS. (Differences in the volume of specific ETF's at each market center explain some of these variations, but in general NFS did exhibit consistently more adverse WAPI versus BATS and Direct Edge among specific ETF's. If interested in more details, please submit a comment below requesting more information.)

The following graphs display the share volume in each market center for iShares ETF's which Fidelity charges its customers to trade and the corresponding weight-average price impact ("WAPI").

The performance across market centers is consistent with the iShares ETF's which trade commission-free at Fidelity. BATS, Direct Edge, Barclays, Knight Equity Markets, and NFS exhibit adverse WAPI. However, BATS, Direct Edge, Barclays, and Knight followed an improving trend over time. In terms of WAPI, only NFS achieved more adverse WAPI from late 2009 to early 2010.

Similar graphs for other ETF complexes similarly show NFS achieving increasingly adverse price impact over time, in contract to most peers. If the above graphs are not sufficient, click on the following ETF complexes to open a few more: Direxion, PowerShares, and ProShares.

In conclusion, while the arrangement between Fidelity and iShares does make tremendous sense from a marketing perspective, the quality of trade execution leaves much more to be desired. For financial advisors or long-term investors who prefer using ETF's, the price impact may not be meaningful enough to diminish the cost savings from zero commissions, as long as trades are not too frequent. Conversely, for day traders, the economics of avoiding a commission may not offset adverse price impact while executing an order at NFS (assuming that a Fidelity customer does not have the option of specifying at which market center to execute). As with any analysis of vast amounts of data, the results have varying relevance depending on individual investor circumstances. Nevertheless, one cannot help but wonder if NFS will improve its trade execution quality for ETF's - regardless of whether a commission is charged or not.

A few important details concerning the data utilized for this analysis:

1. The above graphs compare data gathered from Rule 605 disclosures from approximately a dozen market centers (trading venues). Those venues are selected based on the Rule 606 disclosure from National Financial Services ("NFS", an affiliated broker-dealer of Fidelity) which lists to which market centers Fidelity routed equity orders during the first quarter of 2010.

2. The Rule 605 disclosures for each market center (except for BIDS Trading) are used to analyze trade execution quality over the six month period from October 1, 2009 through March 31, 2010. Data at the individual ETF (ticker) level is aggregated according to the sponsoring firm (e.g. iShares, State Street, PowerShares).

3. The above graphs reflect only market and marketable limit orders. While other types of limit orders are frequently used, Rule 605 disclosures report execution quality for only market and marketable limit orders. To see a table summarizing activity by order type, click here.

4. Aggregated figures may not be ideally comparable across market centers and ETF complexes (sponsoring firms). Each market center has a different share of the trading volume for each ETF which impacts the comparison of execution quality across different market centers and during different months. Furthermore, not every market center has necessarily executed trades in every ticker within an ETF complex.

5. Weighted average price improvement may reflect some biases due to the differing prices of ETF's. Higher priced ETF's may have orders filled with greater deviations from the quote than a lower priced ETF. Conversely, lower priced ETF's may trade in larger share volumes than higher priced ETF's. The weighted average considers both the impact on price, in dollars, weighted by the corresponding share volume. Orders filled at the quote are included in the weighted average with an assumed price impact of zero.

Treasury Temporary Guarantee Program: Update on Potential Costs to the Uninsured Investors

2010-05-06T13:17:00.000-07:00

On September 18, 2009, the Treasury's Temporary Guarantee Program ("TGP") ended. This program provided a form of government insurance on participating money market funds. A few posts in November 2009 reviewed the manner in which payments to the Treasury (insurance "premiums") were calculated and charged by participating money market funds. The primary focus here is who paid for the insurance and who could have received any payments from the coverage.

While premiums were paid directly by the funds, the funds used their fees assessed to all money market fund investors, including any investors who did not qualify to receive a monetary benefit from the insurance. Investors in a participating fund would be covered by the TGP capped at the invested balance as of September 19, 2008 (the "Cut-Off Date"). In other words, even though premiums were charged at the fund level, coverage was defined at the specific account level and based on a specific time. This arrangement is akin to paying premiums for a mandatory employer-sponsored group insurance plan but not being covered if the employment started past a certain date.

Even people who invested in a money market fund as of the Cut-Off Date would not necessarily be fully covered by the TGP. If such an investor held $1,000 on the Cut-Off Date but subsequently invested another $2,000, only the initial $1,000 would be covered. In fact, coverage is not transferable across different funds. If the same investor redeemed the initial $1,000 which was covered and used the proceeds to invest in another money market fund, coverage would not apply to the new investment. An earlier post more clearly explains the details of how coverage was extended with respect to the Cut-Off Date.

The data to track who individually invested in money market funds, including when and by how much, is not publicly available. Even brokers possessing this information would probably require a non-trivial amount of effort and expense to retrieve such data. In order to demonstrate the monetary cost to investors who paid for the TGP but were not covered, we can utilize public data on cash flows for money market funds and derive some useful estimates from a model.

Based on the monthly sales and redemptions for registered money market funds, the model estimates how much of the fund balances originated from investments made before and after the Cut-Off Date. The results below exclude funds which invest primarily in Treasurys and Government debt. Several such funds decided not to participate in the TGP for the full term since, by investing in only Federal government-backed debt, the Treasury guarantee was implicit, hence additional insurance was deemed redundant. Therefore, the sample population of funds in this analysis comprises those which invest in CP, corporate bonds, municipal bonds, RMBS, ABS, and repurchase agreements, in addition to some possible amount of Treasurys and Federal agency debt. In order to expedite the modeling process, only funds with assets over $1 billion are included, and we assume such funds represent a majority of the population.

The results are broken down by three main scenarios. Across all scenarios, investors are assumed to redeem from money market funds in proportion to their balances. For example, if $100 million were invested in a money market fund prior to the Cut-Off Date, out of a total balance of $400 million for the same fund, then the model would assume that 25% of redemptions would be deducted from the $100 million portion. Of course, this "ratio" changes over the modeled time period, from September 19, 2008 through September 18, 2009, depending on how many investors purchase and sell a money market fund after the Cut-Off Date.

In the first scenario, the model assumes that purchases of a money market fund are as likely to come from investors who are covered (insured) by the TGP than those who are not covered (uninsured). Accordingly, in the numerical summary below, the split in Average Monthly Sales (82.6% insured vs 17.4% uninsured) closely matches the split in Average Monthly Balance (81.4% vs 18.6%). As highlighted by the green box, investor balances not covered by the TGP paid 9.6% of the total premiums in this model scenario.

In the subsequent graph, which shows the trend in the insured and uninsured balances over the modeled time period, the monetary cost to investors who are not covered by the TGP is proportional to the size of the red bars versus the green bars.

In the second scenario, the model assumes that purchases of a money market fund are twice as likely to come from investors who are covered by the TGP than those who are not covered. When comparing the split in Average Monthly Sales (45.4% insured vs 54.6% uninsured) against the split in Average Monthly Balance (65.7% insured vs 34.3% uninsured), we observe a greater tendency for sales/purchases to come from investors not covered by the TGP. As highlighted by the green box, investor balances not covered by the TGP paid 28.2% of the total premiums in this model scenario - equivalent to $188 million for the sampled population of $1.9 trillion in fund assets.

Accordingly, the graph corresponding to this second scenario shows a larger proportion of bars colored red than in the first scenario.

In the third and final scenario, the model assumes that purchases of a money market fund are four times as likely to come from investors who are covered by the TGP than those who are not covered. When comparing the split in Average Monthly Sales (25.7% insured vs 74.3% uninsured) against the split in Average Monthly Balance (49.9% insured vs 50.1% uninsured), we observe an even greater tendency for sales/purchases to come from investors not covered by the TGP. Investor balances not covered by the TGP paid a substantial 44.7% of the total premiums in this model scenario - equivalent to $298 million for the sampled population of $1.9 trillion in fund assets.

This scenario may be somewhat extreme but interesting nevertheless. In the corresponding graph, the proportion of red bars versus green bars shows the increasing amount of TGP premiums paid by investors who invested in a money market fund after Cut-Off Date.

While TGP premiums were paid to the Treasury only on specific dates, we assume that money market funds amortized that expense over time and spread the cost out beyond the date when premiums were paid. Regardless of when an investor made initial or subsequent investments, whether before/after Cut-Off Date and before/after premium payment dates, the fund assessed its fees consistently across all investors. Whether investors in a money market fund paid for some portion of TGP premiums after its termination on September 18, 2009 is a question beyond the scope of this analysis and beyond the scope of data available.

We cannot estimate an exact monetary cost to investors who, by definition of the TGP, would not have been eligible to receive payouts from the Treasury in the event a money market fund suffered losses from defaulted securities or otherwise could not fully repay investors. However, from these scenarios, we get a sense of the relative magnitude of this cost. The green box in each of the numerical summaries above indicates how insured and uninsured investors split the modeled cost of TGP insurance. Investors not covered under TGP might have paid $64 million under a reasonable scenario, to as much as almost $300 million in an extreme scenario. Without access to additional data from account-level records, one cannot perform a more accurate and conclusive analysis. (For a money market manager curious on how such an analysis would work, please contact the author at omer@ProteusFinancial.com)

This analysis should lead to further questions and potential ideas for how such a program could have been funded in a more equitable manner. Please feel free to post comments and feedback below. Check back for a future post showing how an actual money market fund (which invests in Treasury securities) participated in the TGP for the full term even though a vast majority of investors purchased the fund after the Cut-Off Date.

In Search of Price Improvement for Schwab ETF's

2010-04-09T14:47:00.000-07:00

When Charles Schwab sold its capital markets affiliate to UBS in October 2004, the industry took notice of the impact: UBS gained significant market share in NASDAQ equities trading as well as the sizable equities order flow from Schwab's customers. Schwab does not receive rebates or other payments from UBS. Nevertheless, part of the consideration Schwab received for the sale was a commitment to route orders through UBS for eight years, starting in 2004.

Normally, Schwab charges a commission for trading equities and options. However, on November 3, 2009, Schwab announced the launch of their proprietary ETF's as well as their unprecedented $0 online commission rate for Schwab clients.

Does this no-commission/no-rebate arrangement have any impact on order flow or order execution? Comprehensive details are not easy to collect, but the following analysis is an attempt at providing some insights from data which is available.

This analysis utilizes Rule 605 disclosures for the following exchanges and ECN's ("market centers"): UBS, NASDAQ, NYSE Arca, BATS, DirectEdge, Instinet, Knight Capital, and Citadel. The sample period covers November 2009 through January 2010. Other market centers executed or routed orders for Schwab ETF's, but this sample accounts for 72% of the trading volume over the sampling period.

Below, we look at various statistics among some of these market centers with a specific focus on UBS.

(Note on the following graphs: Individual ETF's are aggregated according to their sponsoring firm into "ETF complexes". Although the selected ETF complexes have trading volumes larger and smaller than that of the Schwab complex, Schwab's ETF volume should fall roughly in the center of the distribution. Shown next to each ETF complex is the aggregate volume, over the sample period, expressed in millions of shares. The graphs do compare ETF complexes with varying amounts of liquidity, but one can isolate those with similar trading volumes by noting the figures in parenthesis.)

What happens when a broker (assumed to be Schwab) routes a Schwab ETF order through UBS?

We observe how many shares are traded at market centers other than UBS, out of all shares routed and traded through UBS. Few other ETF complexes get such exclusive treatment by UBS, certainly none as large as the Schwab complex. UBS traded fewer shares of First Trust, Rydex, and WisdomTree ETF's (10.4, 9.3, and 11.9 million shares respectively) than Schwab (17.3 million shares) yet routed more of their orders to other market centers.

What happens when a Schwab ETF order is routed through other major market centers?

If a market center cannot fill an order at the best price (national best bid and offer, or "NBBO"), then that center should route the order to another venue where a better price is available. On that basis, other market centers don't seem as capable as UBS in achieving best trade execution for Schwab ETF's. Perhaps this makes sense if the investors trading Schwab ETF's are mostly Schwab clients. As seen above, although NYSE Arca handles orders representing 2.1 million shares of Schwab ETF's, it routes almost 10% of those orders to other market centers where better prices are presumed to have been available.

How well did UBS fill those orders which it chose to handle internally (i.e. not route to other market centers)? Given the data available, we can look at how many shares were executed inside the quote (known as "price improvement"), at the quote, and outside the quote. The following graphs display the percentage of shares which fall into one of these "three tiers" of execution quality. Most orders are either market orders or marketable limit orders. Therefore, we report the two types separately since there may be inherent differences between the manner in which market orders and marketable limit orders are executed. One would especially hope for price improvement on market orders, but no worse than filling at the quote.

Given that UBS executed almost all Schwab ETF's orders internally, it is interesting to see how UBS fills market orders. Among complexes of comparable trading volume, Schwab ranked quite low on price improvement. UBS achieved the best price improvement for iShares ETF's, at about 75% of the total 228 million shares traded with market orders. First Trust ETF's, with a smaller volume of 9.1 million shares traded with market orders, still saw price improvement for 65% of that order flow. UBS achieved price improvement for Schwab ETF's for about half of the 14.7 million shares traded with market orders. The results for at the quote and outside the quote fills are respectable, but nothing special either.

For marketable limit orders, UBS handled Schwab ETF volume of 2.6 million shares. Price improvement ranked somewhat better across the same sample of ETF complexes, but not by much. Approximately 3% of shares traded with marketable limit orders saw price improvement, while almost all others were executed at the quote. UBS achieved the best rate of price improvement for Victoria Bay ETF's with a higher volume (24.6 million shares). ETF's from First Trust and Rydex, with 1.3 and 1.6 shares traded respectively, saw better rates of price improvement even though their volumes are smaller than that of Schwab ETF's.

The same statistics are available at other market centers. Results can be skewed depending on the order volume handled by each market center. Hence, the initial table, showing volume of executed orders by market center, may be a helpful reference.

In comparison to other ETF complexes, NYSE Arca executed market orders and marketable limit orders for Schwab ETF's with modest price improvement, which falls near the lower end of the range. Note that the volume of market orders is very small, only 0.1 million shares of Schwab ETF's executed and no more than a million shares for any other ETF complex. The volumes for marketable limit orders was higher, with 2.0 million shares of Schwab ETF's executed, and Schwab ranked roughly in the middle of the distribution.

Similarly, Schwab ETF's traded on NASDAQ with very little price improvement, but nevertheless ranked near the middle of the range among other ETF complexes with comparable volumes. NASDAQ did not disclose any statistics for market orders.

For orders executed at BATS, Schwab ETF's received the most-frequent price improvement for marketable limit orders in comparison to peers, while price improvement seldom occurred for market orders. Out of 0.8 million shares traded in Schwab ETF's with marketable limit orders, BATS achieved price improvement for approximately 12% of the order flow. While this percentage is much lower than the rate of price improvement achieved by UBS, Schwab ETF's ranked better than other comparable ETF complexes for marketable limit orders executed at BATS.

While digesting these graphs, consider these additional factors.

1. The trading activity spans only three months, although all market centers and ETF complexes are compared over the same period. Schwab launched its proprietary ETF's in November 2009, hence relevant activity does not exist for prior months.

2. Only online orders entered through Schwab are commission free. Orders entered through other channels (e.g. phone) are not advertised as commission free, even though those orders would likely be executed through UBS. The available data does not provide any information on the order entry method. Hence, this analysis assumes that the majority of Schwab ETF's orders routed through UBS were entered online.

3. Other attributes may differ between orders routed through UBS and orders routed through other market centers. No details are available on actual limit prices on limit orders, and the specific execution times and applicable NBBO's are not provided. However, further analysis can be performed on the amount of price improvement at the ticker level.

Execution quality is challenging to track and measure. The summarized data used in this analysis attempts to compare execution quality among major market centers at the level of ETF complexes. While not digging into finer levels of comparison (ticker level or specific month), this analysis demonstrates that Schwab ETF orders routed through UBS almost exclusively get executed at UBS and generally with less frequent price improvement than orders executed at UBS for other ETF complexes. Nevertheless, the other market centers in the sampled data exhibited even less price improvement.

UBS probably has an advantage versus other market centers due to its sizable portion of order flow for Schwab ETF's, even though other ETF complexes with smaller volumes (such as Claymore, WisdomTree, and Rydex) achieved more frequent price improvement at UBS. On the other hand, UBS achieved price improvement for other ETF complexes (such as iShares, Vanguard, PowerShares) to a greater extent than for Schwab ETF's even though a larger portion of orders were routed to other market centers.

Would paying a commission in order to trade an iShares ETF have resulted in more cost-effective execution than paying no commission to trade a Schwab ETF? The answer may depend primarily on order size and the frequency of trading. This analysis is not exhaustive and cannot be conclusive, but the sampled data demonstrates that paying no commission when trading an ETF might not always result in the lowest overall execution cost.

Can Money Market Funds Have Too Much Liquidity?

2010-02-27T08:10:00.000-08:00

On Jan 27, 2010, the SEC adopted a new set of rules governing how registered money market funds operate. A number of these rules were adjustments to certain requirements already existing under Rule 2a-7. While the new rules are material changes relative to current requirements, they might not sufficiently address the root causes of some challenges still facing the money market fund industry.

After the Reserve Fund broke a buck shortly after Lehman Brothers filed for bankruptcy, the SEC and investors have been concerned about the perceived safety of money market funds. The Treasury was concerned enough to create the Temporary Guarantee Program, highlighted in an earlier [post]. The SEC has debated how to change the rules governing these funds. Floating the $1.00 NAV price has been a consideration, but the fund industry's resistance and concerns from that idea have prompted the SEC to focus on other adjustments to the rules.

There are some interesting restrictions on credit quality and maturity of underlying investments. Highlights from the SEC's [summary] of the new rules:

1. Restrict the maximum weighted average maturity ("WAM") of the total portfolio to no more than 60 days, versus the previous limit of 90 days.

2. Limit illiquid securities (those which cannot be liquidated or disposed of within seven days at carrying value) to no more than 5% of total fund assets, versus the previous limit of 10%.

3. Highly liquid securities, which must be convertible into cash within one day and one week, must be no less than respectively 10% and 30% of total fund assets.

4. Limit investments in Second-Tier Securities (generally rated A-2/P-2) to no more than 3% of total fund assets, versus the previous limit of 5%.

5. Limit the maturity of any Second Tier Securities to 45 days, versus the previous limit of 397 days.

6. "Know Your Investor" procedures require money market funds to anticipate potential redemption requests based on the type of investors and past investor behavior.

In essence, all of these requirements seek to ensure that money market funds will be able to accommodate a spike in redemption requests. The Reserve Fund scenario seems to be the case study to which these new requirements are targeted. New monthly disclosures to the SEC, made public with a 60 day lag, will put more pressure on portfolio managers to follow these new rules with a high degree of certainty. As a
result, a portfolio manager might target a WAM which is comfortably under 60 days.

However, one consequence of these rules could be a drop in demand for commercial paper maturing beyond 60 days and other short-tenor bonds which otherwise would cause the fund's WAM to exceed 60 days. This penalty for tenor comes at a time when many corporations, including banks, rely heavily on investors to restructure and rollover maturing debt. More frequent maturing of commercial paper will result in reduced flexibility for corporations to manage their interest expenses. As interest rates rise, as most likely they will over time, corporations and government agencies might need to re-issue short-term debt more frequently and at higher rates.

Should a crisis in confidence or some other major credit event occur, bond issuers will have less time to address their refinancing needs. During a major market crisis, an additional 30 days (difference between a 90-day to 60-day maximum WAM) can be instrumental to survival. Ironically, one firm's inability to survive a liquidity crisis prompted the debate which resulted in these new requirements.

How do money market funds currently manage the WAM in their portfolios? Should we expect a major change in behavior as a result of the new rule?

Specifically with respect to the new WAM requirement, the SEC appears to have implemented a minor tweak in reality. The graph below shows historical WAM as reported to the SEC by registered money market funds, which generally invest in short-term bonds (those maturing within one year). Since past disclosures to the SEC are semi-annual (future disclosures to the SEC will be monthly), the graph shows a six-month historical rolling average which tries to capture the WAM for the entire population. However, there are still variations in WAM during the interim months of the semi-annual reporting periods which may cause these averages to lag reality somewhat.

According to this data, a large majority of money market funds appear to manage their portfolios to a WAM under 60 days. At least 75% of the population (red line), measured in terms of assets, would appear to not be impacted by the new restrictions on maximum WAM. Even the 10% tail of the population (yellow line) with the highest WAM appears to require only some minor adjustments to their future investments. The median (green line) consistently maintains a WAM of under 50 days.

Will these historical norms of managing investments in money market funds be sufficient to meet the financing needs of corporations and government agencies? When markets were more favorable, many issuers found better pricing in the intermediate-term and long-term bond markets. Since the latter half of 2007, issuers facing financial difficulties have been forced to rely more on the short-term bond market than desired. Hopefully, those issuers are not relying too much on registered money market funds to rollover their debts, or else they may want to revise upwards their interest expense projections.

Securities Lending by Index Funds Still Recovering

2010-02-26T21:11:00.000-08:00

Last month's post highlighted a preliminary indicator of a bottoming out and turnaround in the lending of securities held by registered mutual funds. A recent update of data from the SEC indicates that lending capacity, or a willingness to lend, among bond mutual funds remains well below the peak in 2007. (From the previous post, remember that the data collected for this analysis indicates only whether a fund has lent securities but not specifically how many of them.)

Given the illiquid markets for many types of fixed income securities, one might expect a much lower supply (demand) for lending (borrowing) bonds. Even short-sellers who identify profitable dislocations in the market may hesitate to risk a short squeeze, should lenders decide to call back the bonds. The uncertainty in prices for many bonds, as a result of a dearth of trading activity, might make the calculation of margin requirements too challenging and risky to justify the revenues from lending.

The drop in securities lending capacity among bond funds is equally prevalent in index funds and actively-managed funds. Historically, index funds focused on reducing their net expenses to shareholders by collecting additional fees from short-sellers (borrowers). Thanks to counterparty risks and pricing concerns, further aggravated by Lehman's bankruptcy, the benefit of reducing an expense ratio, by collecting extra lending revenue, does not justify the risks. Bond index funds cannot afford the reputational risk either.

(Note concerning the graphs: In the previous post, the date axis reflected the earliest date within a six-month semi-annual reporting period. In order to more clearly reflect the latest date, graphs will reflect the latest date of a six-month semi-annual reporting period. For example, data points as of 11/30/2009 are based on data from 6/30/2009 through 11/30/2009.)

For actively-managed bond funds (excluding index funds), the capacity for lending securities has been steadily declining since 2008, both for funds which choose to lend and funds which prohibit lending securities.

For index bond funds, the trend is similarly downward trending. The population of such funds, in terms of number and AUM, is very small - on the order to only $200 billion compared to over $5 trillion for actively managed bond funds. Therefore, the trend can shift abruptly if a few large index funds change their policy. Furthermore, note the very small sample size for September 2009.

The drop in lending capacity has been less severe for both equity index and actively-managed funds. For the most part, equities did not suffer the degree of illiquidity which hit the bond markets in 2008 and 2009. Given sufficient trading activity, lenders appear comfortable with loaning out shares. If a lender or custodian wants to call back the securities, a borrower can most likely repurchase the securities through an exchange or ATS, venues which outside of the Treasurys are quite non-existent for bonds.

For actively-managed equity funds, the portion of the population actively lending shares has decline steadily since late-2007. However, during 2009 this trend appeared to be reaching a turning point. This stabilization has yet to prove a turnaround.

In terms of assets (AUM), the vast majority of equity index funds lend shares, but in terms of number of funds, they appear to utilize securities lending similarly to their actively-managed counterparts. Of course, this implies that the largest equity index funds are dominating the supply of lendable shares. Overall, the decline in securities lending also appears to be muted. One reason should be that equity index funds should be inclined to loan securities given the additional revenue has a relatively material impact on reducing their expense ratios.

Furthermore, actively-managed equity funds are more likely than their indexed counterparts to not allow securities lending (represented by the red bars). By number of funds, the portion which utilize securities lending (green bars) are consistent between the active and passive groups.

The next update will include funds reporting through December 2009, which constitutes a large portion of the entire sample. Then we should be able to better determine the near-term course of these trends.

Don't Overlook the Recent Rally in Securities Lending

2010-01-08T16:47:00.000-08:00

Securities lending programs became increasingly popular during the last decade among many investment companies (mutual funds, ETF's, pensions, endowments). Long-only funds, pensions, and endowments were attracted to the incremental income from lending securities which otherwise remained invested? The demand from certain hedge funds and other speculators with shorting selling strategies made such programs all the more worthwhile. In addition, custodians of the securities also got a chance to earn additional fees for providing the service.

As with many other investment trends in the last decade, the music for securities lending abruptly stopped playing around the bankruptcy of Lehman Brothers. Interestingly, the reason for losses was not specifically related to the securities which were lent. Except in the case of securities held in accounts at Lehman Brothers, securities generally could be recalled by the lender. Instead, investment companies which lent their securities suffered losses from supposedly safe short-term securities using the cash collateral from borrowers. Regrettably, the securities lending transaction added counterparty risk and collateral price risk in exchange for a fee which probably did not fully compensate the lender for these risks.

The web is filled with ample explanations of how these lending programs worked, and the media diligently covered the losses on reinvested collateral (and also popularized the SIV acronym). The immediate purpose of this posting is to provide a simple graphical perspective on securities lending activity among registered investment companies.

Using SEC filings from primarily mutual funds and ETF's, we can get a 50,000-foot view of securities lending activity. Starting in 2007, funds pulled back their level of lending activity in order to re-assess the risks and protect against further unforeseen losses on reinvested collateral. The interesting observation from the following graphs is the apparent recovery in lending activity.

Please note two important caveats concerning the data behind these graphs.

1. Investment companies disclosed their policy to lend securities, specifically whether lending was allowed and whether it occurred during each reporting period. However, the data does not reflect exactly how much of the securities were actually loaned out. Of course, if an investment company had a policy to not allow securities lending or did not engage in the practice, then we assume no securities were lent.

2. The data has a few months lag. The graphs cover a period through June 2009, but the sample during the second quarter of 2009 gets thin and leaves open the possibility of material restatement as more data arrives. To gauge the sample size, refer to the dollar amount of sampled portfolios is displayed underneath the months along the horizontal axis.

Starting from December 2003, the following graphs (separately for equity-dominated portfolios and debt-dominated portfolios) show the overall mix of securities across the following three categories:

1. Fund policy did not permit lending out securities during the reporting period.

2. Fund policy did permit lending out securities but was not engaged in lending any during the reporting period.

3. Fund policy did permit lending out securities and was engaged in lending some amount of them during the reporting period.

Here are three graphs for equity funds, looking at the trend according to total assets (AUM), average AUM per fund, and number of funds.

The green bars are intentionally placed underneath the yellow bars. The height of the combined green and yellow bars corresponds roughly to the capacity for lending activity, so hence the green bars are a gauge for how much this capacity was utilized.

The return of securities lending does not appear to be consistent across the population. While lending utilization based on total AUM has been trending upwards since January 2009 (first graph), the number of funds participating in this rebound has been trending downwards (second graph), albeit the pace of decline in 2009 is slower than in 2008. This divergent trends implies that funds lending securities are larger on average than previously. Furthermore, the third graphs shows that smaller funds which can lend securities do not exercise the privilege (yellow), when compared to larger funds (green).

Here are the same three graphs for bond funds.

For bond funds, the trends are consistent across the board. In terms of total AUM and number of funds, securities lending utilization has trended upwards during 2009 to historic highs. When looking at the population based on the average AUM, those funds lending securities appear to be increasingly smaller than those which do not authorize the practice. Given the challenges in managing and monitoring a securities lending program, one might have expected larger bond funds to be more engaged in the practice. However, smaller bond funds may be relying on their custodian banks or third parties to manage the programs, hence not impacted by internal resource constraints.

Finally, one obvious and expected outcome from this analysis: equity funds have consistently engaged in securities lending much more than bond funds. Hedge funds which borrow securities probably engage in long/short trading strategies, but if trading in bonds becomes more electronic or ~~hedge funds~~ investors seek more opportunities in less liquid markets, bond funds might gain a performance edge on their competitors by supplementing returns with lending revenues.

Check back in the future for updates of these graphs and possibly other breakdowns, such as passive vs active strategy, custodian specific, and fund complex. Feel free to post or email other suggestions for assessing these trends.

Further reading:

Wall Street Journal: Is Your Fund Pawning Shares at Your Expense?

Wall Street Journal: Getting a Fair Share from ETFs

The Motley Fool: The Risky Business of Securities Lending

Cost Basis Reporting: impact on TA systems, if any

2009-11-30T13:30:00.000-08:00

The Emergency Economic Stabilizaton Act of 2008 consists of a tax provision which requires brokers to report extensive cost basis information to the IRS and investors. Investors who received past gain/loss statements from their brokers should realize that these statements were not reported directly to the IRS. As a result, this provision brings cost basis reporting for tax purposes to an entirely new level.

In just over two years, starting on January 1, 2012, brokers and their transfer agents will be required to track and report gains/losses for mutual fund transactions in much greater detail than currently required. The deadline for stock transactions is actually one year earlier: January 1, 2011. Experts in the industry are saying that any brokerage which has not started planning for the systems impact is already behind schedule.

There is no shortage of articles highlighting the need to take action and vendors touting potential solutions. One major class of solutions aims to import data from existing transaction processing systems and compute the required cost basis information for investors and the IRS. Approaching the problem as only a reporting challenge has its appeals, probably foremost for cost and expediency. Recoding a transfer agency system during a recessionary period (or anytime, for that matter) ranks up there with persuading Americans to agree on healthcare reform.

With respect to mutual funds, transfer agency shareholder recording systems ("TA systems"), from vendors such as DST, PNC, SunGard, and Envision, generally follow fundamental rules when tracking sharelots. (For those wondering, assume a sharelot to represent the most basic level of tracking an investment in a stock, ETF, mutual fund, bond, etc. on a TA system.) The following such rules apply to a single account ("account-level processing").

1. When an investor purchases shares in a mutual fund, a sharelot is created on the trade date of the investment. If for some reason multiple purchase orders for the same fund occur on the same trade date, the TA system might consolidate those orders under one sharelot.

2. When an investor sells/redeems shares in a mutual fund, the balance of an existing sharelot is reduced by the size of the sell order as of trade date. The choice of which existing sharelot to reduce depends on various rules, usually chosen by the mutual fund (hopefully disclosed in the corresponding Prospectus or Statement of Additional Information.)

3. When an investor reinvests a dividend into a mutual fund (not necessarily the same fund which paid the dividend), the balance of a sharelot created specifically for reinvested dividends is increased. Generally, every reinvestment trade does not create a new sharelot (but, as with everything related to transaction processing, exceptions may exist).

4. The purchase or sale of mutual fund shares as part of an exchange (selling one fund and using proceeds to purchase another) is handled no differently. Sharelots are reduced and created accordingly. However, other rules may take into account special redemption fees, front-end loads, or commissions, for which additional sharelots might be required in order to facilitate computations.

5. Moving shares from one account to another (i.e. changing brokers) generally has no impact on the sharelots, should not result in a taxable event, and should not change the cost basis. However, brokers need to be certain to transfer the sharelot data properly across systems. A surprising portion of account transfers require human intervention, such as sending/receiving faxes and manual data entry.

What might the new cost basis reporting requirements mean for mutual fund transactions and share balances recorded on TA systems, and their users? Here are a few potential repercussions to consider.

1. Systems which follow certain rules for processing sell orders might need to accommodate other rules required under the tax provision.
Simple FIFO or LIFO no longer suffices. Investors will have the option to select which rules to
follow, even change selections over time.

2. The reinvestment of dividends may require the creation of more sharelots in order to track each individual reinvestment transaction. The IRS will require brokers to compute the gain/loss for each set of shares purchased, regardless of whether an investor used cash or dividend distributions to fund the purchase.

3. Wash sales occur when identical shares (the true meaning of which may be pending clarification) are purchased within 30 days before or after being sold. If a taxable loss is realized when shares are sold, the corresponding purchase would require an adjustment to the sharelot cost basis. Not all TA systems may handle this adjustment automatically.

These are only a few considerations, and future postings on this blog may introduce more. Keep in mind that the impact on TA systems might be minimal if adopting the new cost basis reporting requirements is defined as a reporting challenge. As brokers plan various solutions in coordination with technology vendors, will the underlying data architecture and trade processing code in TA systems remain largely insulated?

Check future postings for an ongoing assessment of operational considerations and hopefully some quantitative simulations of the potential tax impact.

Treasury TGP = Sticky Situation

2009-11-20T22:54:00.000-08:00

In the last two postings, I discussed how the Treasury's Temporary Guarantee Program (TGP) for supporting money market funds favored shareholders who held positions on or prior to September 19, 2008 (the "Cut-Off Date"). The TGP served to preserve the perceived safety of participating money market funds for existing covered investors and possibly new uninsured investors.

More specifically, the TGP offered money market funds a few key competitive advantages.

1. Normally, FDIC-insured bank deposits are the dominant means for saving cash under a federal guarantee program. The TGP placed money market funds on a more even playing field, which probably was merited given the alternative of having investors en masse pull out of money markets funds. In response, the American Bankers Association warned that investors would be encouraged to withdraw bank deposits to take advantage of the TGP. As a result of this concern, the Treasury decided to limit the guarantee to shares held as of the Cut-Off Date.

2. If investors wanted to transfer funds out of a money market fund after the Cut-Off Date, the guarantee would cease to apply to the transferred funds. However, it seems that an investor could transfer funds back into the same money market fund and continue to benefit from TGP coverage up to the eligible balance as of Cut-Off Date. Therefore, money market funds became a convenient place to park funds (a sort of "safe haven") any time investors became worried about systemic risk in other assets or markets.

3. The "safe haven" status might have also encouraged investors to stay in a specific fund family. In other words, an investor could hop around different mutual funds within the same fund family where the money market fund provided coverage under the TGP. A decision to switch to another fund family would most likely involve a choice to close an account and abandon the insurance, unless an investor was a shareholder in money market funds at multiple fund families. As long as an investor never closed an account which provided coverage under the TGP ("Covered Account"), he/she could have transferred funds in and out of that account without losing the applicable insurance.

Given the last item, fund sponsors probably benefited from the "non-portability" of TGP insurance. (Remember when cell phone numbers in the U.S. could not be transferred between different carriers?) In contrast, FDIC insurance was always "portable", because the FDIC would insure bank deposits regardless of when and from where the money arrived. Although the Treasury specified a Cut-Off Date to prevent investors from moving their funds out of seemingly weaker institutions into safer money market funds, the Cut-Off Date ~~and customer service representatives~~ probably encouraged investors to think twice before leaving a fund family.

Combined with numerous press releases announcing participation in the TGP, marketing departments at fund families were handed a powerful tool for mitigating customer attrition - and possibly attracting new customers and assets (see "Exposed Assets" in the November 13 posting).

Covered and Exposed, but by how much?

2009-11-13T13:34:00.000-08:00

Did money market fund investors fully appreciate how the Treasury's Temporary Guarantee Program ("TGP") impacted the benefits and cost of coverage?

Firstly, the overall cost was not substantial as a percentage of assets in the money market funds. For the initial period of the program and its subsequent two extensions, participating funds paid 0.01%, 0.015%, and 0.015% of the "Covered Assets" (i.e. value of shares outstanding as of September 18, 2008) for each period. Hence, the total cost of participating for the full term was 0.04% of Covered Assets. Remember that Covered Assets never adjusted for any investments and redemptions which occurred after September 18, 2008 ("Cut-Off Date").

(We need to define one more term before pressing ahead: Shares purchased in excess of the account balance as of the Cut-Off Date would not be covered under the TGP; we shall refer to them as "Exposed Assets".)

Given that every share of a mutual fund (technically, within one class of shares) must be charged the same fees, every share must pay its share (no pun intended) of the cost of participating in TGP (for lack of a better term, "TGP Premiums"). Therefore, Exposed Assets paid part of the TGP Premiums even though the Treasury excluded coverage for losses on Exposed Assets if the fund NAV price dropped below $1.00 (or $0.995 to be accurate).

Was the amount of TGP Premiums paid a material sum to make it worthwhile to even further this discussion? That depends on whether you consider $1.2 billion to be "material". The Treasury indicated that they would keep the TGP Premiums in its Exchange Stabilization Fund. (By the way, $1.2 billion is approximately 0.04% of $3 trillion.)

A small study, including a simple simulation, can shed some light on how much investors might have paid for insurance with respect to Covered Assets and Exposed Assets.

All registered mutual funds publish their monthly sales and redemptions via the SEC's web site. Each fund must report individually for each month, albeit with a few months lag. By looking at sales and redemptions for every month and every fund, one can try to estimate how much Covered Assets stayed in the funds, with the remainder comprising Exposed Assets. At this point, these estimates cannot be validated with account-level data, which is not trivial to gather and otherwise non-public.

Given that the TGP ended on September 17, 2009, we don't have enough data to carry the analysis through the end of the program. However, as of October 2009, a sizable minority of funds have reported sales and redemptions through June 2009. Total assets range from approximately $550 billion to $700 billion. With time, as funds disclose their historical figures, more assets can be included in this analysis.

For now, the selected sample reflects data for 10 of the largest fund families, excluding money market funds which invested primarily in Treasurys. Midway through the TGP, a number of fund families chose for certain Treasury and Government funds to stop paying for the TGP given that their primary investments were in securities issued by the government. This exclusion simplifies the analysis and keeps samples consistent with each other.

Let's jump into the results. For now, we can assume that investors in Covered Assets end Exposed Assets purchased and redeemed shares in the same proportion as their corresponding balances. For example, given a balance of $400 for Covered Assets and $200 for Exposed Assets (i.e. split 67%/33%), one-month activity of $120 in purchases and $150 in redemptions would be allocated accordingly ($80/$40 for purchases and $100/$50 for redemptions).

(Note: Since the analysis covers the period ending June 30, the entire TGP Premiums are less than would be expected given the 0.04% rate. In other words, through June 30, the Treasury probably collected $1.2 billion of TGP Premiums, but not all of the premiums were fully earned. The resulting adjustment assumes that, as of June 30, the TGP had another 2.5 months until expiration on September 17.)

In the table above, the split between Covered Assets and Exposed Assets is shown in the orange cells. The respective split in TGP Premiums is shown in the green cells. In this case, investors owning Exposed Assets paid only $19 million of premiums. On an average asset base of $159.3 billion, that comes to a rate of only 0.0117% (1.17 bps). Another way to measure their contribution: Exposed Assets covered 10.6% of the total TGP Premium. Not so bad considering that Exposed Assets represented 24% of average total balances. (Remember, all of these results are estimates based on a cash flow model.)

Here is a graph showing the trend in Covered Assets and Exposed Assets during the simulated period.

So what happens if, after September 18, 2008, purchases into these money market funds came more so from investors who had very few, if any, shares in money market funds before the Cut-Off Date? Let's assume that, all else equal, purchases only are split 50/50, where as above the split was 67/33. This might be a more realistic scenario because few investors expected Lehman to go bankrupt, starting a cascade of events which resulted in the TGP, and resulting in a rapid growth rate of Exposed Assets.

Now Exposed Assets comprise, on average, 32% of the total assets. Investors owning Exposed Assets paid only $48 million of premium. On an average asset base of $211.5 billion, that is a rate of only 0.0228% (2.28 bps). Another way to measure their contribution: Exposed Assets covered 27% of the total TGP Premium. Exposed Assets and Covered Assets are charged more comparable rates (2.28 bps versus 2.91 bps, respectively) for insurance, even though only investors in Covered Assets can receive a payout if a money market fund's NAV price falls below $1.00.

Here is the graph corresponding to this second scenario.

Finally, we can take this analysis one notch further (for academic purposes). Let's assume that, all else equal, purchases only are split 33/67 (inverse of the split used in the first scenario).

The trend should be self-evident. Exposed Assets comprise, on average, 47% of the total assets. Investors owning Exposed Assets paid only $77 million of premium, resulting in a rate of 0.0250% (2.5 bps). Although the rate is only marginally higher than in the previous scenario, Exposed Assets covered almost 44% of the total TGP Premium. Interesting, for an academic example.

Here is the graph corresponding to this third scenario.

The critical question: how much did investors add to their existing money market fund balances after September 18, 2008? The money market funds, with help from their transfer agents, have access to data to answer this question. The accurate way to quantify Covered Assets and Exposed Assets would be to look at account level activity, while not complicated would be extensive given the number of accounts held in retail money market funds. Although an industry-wide analysis may be burdensome, specific fund sponsors can perform the analysis individually.

In the meantime, the above scenarios simulated by a cash flow model can only demonstrate the general impact. Check back later for future updates of the numbers and further commentary.

Money Market Guarantee Program: the Finer Print

2009-11-06T12:45:00.000-08:00

The Treasury Temporary Guarantee Program ("TGP") for money market funds has received ample publicity since its inception after the Lehman Brothers bankruptcy. According to a FAQ from the Treasury's web site, the guarantee is subject to certain limitations.

1) If an investor owned 100 shares in a specific fund as of close of business September 19, 2008, subsequently sold the 100 shares, and then repurchased 100 shares in the same specific fund prior to a Guarantee Event, the investor would be covered for 100 shares.

2) If an investor owned 100 shares in a specific fund as of close of business September 19, 2008, subsequently sold the 100 shares, and then repurchased 125 shares in the same specific fund prior to a Guarantee Event, the investor would be covered for only 100 shares.

3) If an investor owned 100 shares in a specific fund as of close of business September 19, 2008, subsequently sold the 100 shares, and then repurchased 100 shares in another fund that is participating in the program, the investor would not be covered.

Let's run through a slightly different example (not from the Treasury's web site) which essentially reflects what the FAQ explains.

On September 12, 2008, an investor owns 100 shares in a money market fund which later participates in the TGP. Lehman Brothers files for bankruptcy on September 15. Fearing that stocks will plummet and bonds will become riskier, the investor reacts over the next few days by liquidating risky assets and purchases another 400 shares in the money market fund. Now the he/she has 500 shares of the money market fund. On September 29, the Treasury opens the TGP for money market funds. However, he/she has no luck in getting the guarantee to cover any of the proceeds generated from selling securities after the Lehman bankruptcy. The investor would have had to foreseen the meltdown in the credit markets (i.e. Lehman bankruptcy) in order to get full coverage (500 shares) under the TGP. Out of the 500 shares of the money market fund, only 100 are covered under the TGP.

This example does not discredit the mission or benefits of the TGP. The money market fund does participate in the TGP in order to stabilize its investor base, thus mitigating the changes of a "run on the fund" which would subsequently hurt all investors.

Rather, only the "finer" print is being highlighted here. After the Lehman bankruptcy, many investors reallocated their investments into money market funds and other relatively safer investments. Hence, it was important for money market funds to receive some form of government support to comfort investors. However, did investors fully appreciate the impact of the September 18, 2008 cut-off?

More posts on this topic should be forthcoming.