From Largecaps to Microcaps: Stock Herding within ETFs

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

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

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

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?

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.