Performance Attribution for Passive Strategies By: State Street Global Services - Performance Services February 2016 STATE STREET CORPORATION 1 SSGSNA-0091
KEY TAKEAWAYS Although the debate over the merits of active versus passive management rages on, passive management through index-tracking mutual funds and ETFs in the U.S. equity markets has become the mainstream strategy. Despite the prevalence of passive investing, performance attribution methods remain geared toward the dynamics of active investing; many managers employ manual spreadsheet reporting to augment active-focused performance solutions. Single-factor attribution models don t address the primary performance drivers that occur within a passive strategy. An effective performance attribution solution can explain excess returns and give greater insight into passive portfolio performance by incorporating several factors to explain excess returns, including: cash drag and futures, trade costs, fair valuation, tax advantage, income and expenses, benchmark impact, NAV rounding, and post-date versus trade-date performance. Introduction As passive index investing has gained momentum since the first index fund was launched nearly 40 years ago, the debate on the merits of passive investing versus active investing has simmered in the background. Yet despite the emergence of passive investing as investors mainstream strategy, performance attribution solutions have remained focused on active strategies. As a result, in order to address passive-specific performance, managers have adopted relatively low-tech reporting methods, such as manual spreadsheet reporting, as their primary source of attribution, tracking error and reporting. In this white paper, we ll review the evolution of passive and active investing, the trends driving each approach in the marketplace, and the factors critical to performance attribution in a passive strategy. The goal is to help managers identify an effective performance solution that provides the appropriate level of transparency and insight when it comes to their key passive performance drivers. STATE STREET CORPORATION 2
Active and Passive Management: A Brief History After the Great Depression, the active management of funds began as analysts selected undervalued securities based on research into companies, markets and valuations. Following Vanguard Group founder John Bogle s Princeton University 1951 thesis, which posited that active-fund managers couldn t beat general market averages, modern portfolio theory (MPT) took root. MPT s premise, that a portfolio s diversification can offset risk, continued to grow alongside the concept of indexing. In the 1970s, these ideas began to win the imprimatur of academics, and general investors took notice. The idea that active stock selection is often outwon by general market performance became more widely accepted, and the first index fund was born in 1976. In the 24 years between 1976 and 2000, the popularity of index funds remained relatively stagnant. In the early 2000s, however, with a variety of global economic forces as tailwinds, investors began to turn to indexed funds in droves with few signs of relenting. In 2004, investors had approximately 150 ETFs to choose from; 10 years later, in 2014, 1,300 funds peppered the market with nearly $1.5 trillion in total assets (Barron s). Today, the tension between active and passive investing can be found across the investment industry, stirring passionate viewpoints and debate. Although active investment has had its notable successes periods where active management beat index fund performance in general, its supporters have had weaker evidence to present in making its case to justify the higher management fees associated with active strategies. Today s Challenge: Attributing Passive Management Performance Even as the debate rages on, there s little doubt that passive investing through index-tracking mutual funds and ETFs in the U.S. equity markets has become the mainstream strategy. According to the ICI Factbook, from 2007 through 2014, index domestic equity mutual funds and ETFs received $1 trillion in net new cash and reinvested dividends, while actively managed domestic equity mutual funds experienced a net outflow of $659 billion. One reason for this trend toward passive strategies is the increased focus on management fees; investors can gain exposure to U.S. broad index strategies for single-digit basis point fees, less than 1/10 th of many actively managed funds. Other factors driving investors toward ETFs are trading frequency, tax efficiency and greater transparency relative to comparable mutual fund products. What s more, the trend shows no signs of relenting; a new survey from Charles Schwab revealed that ETFs are becoming increasingly important to investors, especially millennials, with 60 percent of respondents age 20-35 reporting plans to increase their investments in ETFs next year. The demand for passive management has also STATE STREET CORPORATION 3
spurred innovation in the $2.7 trillion market, with smart beta strategies emerging at a rapid clip to complement traditional broad beta strategies. These smart strategies are passive and tracked by several ETFs and funds, but focused on different weighting methodologies, such as a factor-based approach. Even as the ETF market grows and innovates, however, the performance attribution methods of passively managed products has remained relatively unchanged. Despite the application of new technology to many facets of investment and performance, manual spreadsheets remain the primary source of attribution, tracking error and reporting when it comes to passive management performance. This approach, while flexible, is often exposed to operational risk and is not as scalable as a systematic solution due to data management requirements. Performance Attribution in a Passive Strategy Key Factors The industry has long accepted the Brinson Fachler (or similar variation), single-factor attribution model for equity performance attribution. This approach fulfills most active strategy requirements by attributing excess returns in terms of security selection and sector or country allocation decisions. For passive strategies, however, the usefulness of a traditional attribution model is limited in a number of ways. Most importantly, an active manager s performance often deviates from the benchmark significantly more than a passive manager s portfolio. It is impossible to perfectly replicate a benchmark without any tracking error, which is generated from portfolio construction through such factors as security transactions, cash flows and expenses to name a few. While these factors may only explain a portion of a basis point, they often make up the entire excess return, and therefore are critical to analyzing passive strategies. A single-factor model doesn t explain this excess return generated through passive investing. As an example, a traditional attribution model may explain 90-95% of an actively managed portfolio s return, leaving a residual of three basis points. A passively managed portfolio may have an excess return of three basis points, which is not explained in terms of allocation and selection leaving three basis points in residual. While the active manager may not be concerned with the residual, those three basis points are critically important to the passive manager to explain given they have been hired to replicate an index, while the active manager clients have different expectations. STATE STREET CORPORATION 4
In light of the above-mentioned issues presented by single-factor attribution models, it is important to consider what specific factors should be included in an effective passive management performance solution to overcome them. These factors are highlighted in the chart below, and explained further in the discussion that follows. Cash Drag Cash and cash equivalents Accruals and open trades Other components not invested in replicated securities Futures Impact of futures usually used to offset cash drag Mismatch between futures contract or basket and benchmark Tax Advantage Effect of differing tax rates between fund and benchmark Explicit Trade Costs Commissions, fees, stamp duties, other costs attributable to a specific security Trade Variance Trade execution price vs closing price Fair Value Impact Difference between NAV based pricing policy and index based pricing Other Income Securities lending, litigation Currency Impact Gains/losses due to foreign exchange rate shifts NAV Rounding Rounding precision of official NAV versus extended NAV Expense Impact Management fees, administrative fees Other fees not attributable to a specific security Benchmark Impact Difference between published benchmark return and the return generated by rolling up the individual securities within the benchmark Post Date vs. Trade Date Difference between the NAV return (post date) and the daily performance return (trade date) Cash Drag and Futures Cash drag is generally the largest driver of tracking error. While the index assumes full investment in securities that make up the benchmark, in practice this is not feasible. The overall base of uninvested assets is the sum of cash balances, short-term or sweep balances, payables and receivables due to open trades, and income receivables referenced as the implicit cash balance. This amount, which is not exposed to the market, will benefit the portfolio in a down market and hurt the portfolio in an upmarket. The breakdown of the implicit cash STATE STREET CORPORATION 5
balance is also important, since the portfolio manager can choose to spend the cash and short-term investments, but cannot spend the receivables without leveraging the portfolio. Therefore, the proper breakdown of spendable and unspendable effects in reporting is important. Also critical is to break out the impact due to cash returns, which includes the interest on any spendable balances, and the impact due to currency fluctuations. At any level, portfolio managers may also want to see specific currency drivers and drill down into security specific accruals that make up the unspendable balances. The sum of the cash and currency impacts result in a gross cash drag impact. Many portfolio managers choose to overlay the implicit cash balance with futures to minimize cash drag in the portfolio. The contribution of the futures can then be netted against the gross cash drag to identify the impact of cash drag after the equitization was applied. When using futures to equitize the cash balance, portfolio managers often have to optimize the benchmark exposure with a basket of futures contracts. This can result in another variance: the futures basket blended return compared to the benchmark. STATE STREET CORPORATION 6
Trade Costs Transaction execution and costs also play a significant role in excess returns of passive strategies. While commissions have continued to decrease over recent years, transactions explicit trade costs, which may also include other market specific fees, can also have a noticeable impact. For example, the U.K. market has a 50 basis point stamp duty on all security purchases, based on the transaction value. More recently, asset managers have introduced swing pricing to shield long-term mutual fund investors from the impact of NAV dilution from transaction costs related to large subscriptions or redemptions. In this pricing model, a factor is typically applied to the NAV price investors buy or sell at if the transaction amount triggers a defined tolerance. By adjusting the NAV price for the subscription or redemption, the shares of the transaction are impacted, which should offset the trade costs associated with that cash flow. In addition to the explicit trade costs described above, the timing when trades are executed also impacts return variance. Portfolio managers will often target their trades at market close to minimize tracking error, but when that is not possible, the result can be either beneficial or harmful, depending on market movements. The impact of this variance can be measured in different ways based on how the portfolio is managed, but either way, capturing the impact of transaction timing is an important component of a performance solution. Both explicit trade costs and trade execution impacts are important to isolate from the security selection effect. A full trade cost analysis can be helpful in highlighting how each broker executes transactions on the investor s behalf, as measured by a volume weighted average price or implementation shortfall approach. Fair Valuation Registered mutual funds are required to determine fair value prices for securities when market quotations are not readily available or reliable. This use of fair valuation typically takes place with international portfolios, when information that may impact the price of securities in the fund becomes available following local market close but before the U.S. market close. The impact of fair valuation is important, since benchmarks typically are not adjusted for fair valuation. That s why the difference between a fair valuation NAV and the market close NAV should be isolated as a separate impact in reporting. Tax Advantage Each fund has specific tax rates based on domicile and exemptions. Since tax rates vary by fund, index vendors typically use the most conservative tax rates, which in many cases is the maximum rate in the company s country of incorporation. Due to varying fund domiciles and potential exemptions in place, the fund, in turn, often has a STATE STREET CORPORATION 7
tax advantage over the benchmark. The tax advantage results in a higher income return in the portfolio than a net benchmark; this is typically captured in the security level return, which would drive a selection effect. Tax advantage can often lead to pressures on cash drag, as many portfolios will hold receivables for long periods of time due to the complexities of international tax laws and the reclamation process. Given that this is not an active decision, it is important that a performance solution segregates this effect as its own factor, removing it from security selection. Income and Expenses Security-specific dividends and interest are part of the security returns, and are included in the allocation and selection calculations. Dividend and interest income typically doesn t vary between the portfolio and benchmark at the security level, with the exception of tax rates as described above. Separately, income and expense amounts that are not attributable to an individual security aren t captured in allocation or selection. Fund-level income, such as securities lending or, in some cases, litigation income that is not captured in security-level returns needs to be accounted for as a separate impact. Given the ever changing litigation environment, we have seen an upward trend in litigation distributions which will potentially increase the importance of this factor over time. Expenses such as management fees also need to be accounted for as a separate impact in reporting. Benchmark Impact Total level benchmark returns, calculated from index levels, are official returns that have been published and widely used through different distribution channels for years. Transparency into security-level index returns is a more recent offering from vendors. There is less standardization in how each vendor calculates security-level returns, especially around tax rates on dividend income in foreign markets. In some cases, adjustments for dividend taxes are done at a country or index level. This is one way that discrepancies occur between the assetweighted, security-level return rollup and the total published index return. Although in most cases the index vendors or a data aggregator can obtain the accurate security-level performance on all benchmark holdings, there can be small variations due to lack of standardization. Added transparency into these variations is an important aspect of a performance solution. NAV Rounding Official, published net asset values (NAVs) are typically based on a two-digit price, and the rate of return on a mutual fund or ETF for any specified period will be calculated from that two-digit NAV. The rounding of a NAV from a higher precision to two digits can have a significant effect on funds performance. While over time these differences often net out, there can be a larger-than-expected difference within shorter periods or around STATE STREET CORPORATION 8
significant cash flows. In the extreme example below, we extended the NAV to six digits and calculated the daily rate of return at 40.3 basis points a more accurate reflection of the portfolio s return for the one-day period. Column1 Prior Day Ending NAV Current Day Ending NAV Current Day Return Official NAV 12.270000 12.310000 0.326% Extended NAV 12.265148 12.314581 0.403% Impact of NAV Rounding -0.077% The official performance in this example, based on the two-digit rounded NAV, is 32.6 basis points; the difference of nearly 8 basis points is purely due to NAV precision. The difference between a two-digit industry standard NAV return and a more precise calculation is essential to isolate given its potential magnitude. Post-date vs. Trade-date Performance The market standard for trade processing is to post all trades on the day following trade date, or T+1. Many performance and attribution systems calculate a true trade-date performance, which requires the rate of return to be recalculated based on restated trade-date positions. For the most part, the difference between the two approaches is minimal. The overall profit and loss on the transactions is accurate, but the day it is captured in the numerator of the return calculation can vary. The difference in return is typically offset by a similar difference in the opposite direction on the next day, and this only becomes a noticeable impact if there are a large number of transactions on a day of significant performance. In the example below, a three-stock portfolio has a cash contribution of $70,000. This entire contribution was used to purchase more shares of the largest holding, Stock B. On trade date, the market dipped mid-morning but rebounded to close higher for the day, up about 3.5%. The purchase of 1,000 additional shares of Stock B was executed mid-morning, and the gain on the shares purchased between execution and close was $4,030. This contributed 1.21% to the portfolio, based on beginning market value, plus the $70,000 cash flow contribution. The same $4,030 gain over the ending market value, which would also be the T+1 beginning market value, is 1.17%. The NAV-based post date return calculation would be lower by 1.21% on trade date, and higher by 1.17% on T+1. In this scenario, the difference of nearly five basis points would be the variance of the two returns and needs to be separated from other factors. STATE STREET CORPORATION 9
Shares Pricing Market Values Beginning Transaction Prior Day Close Execution Market Close Beginning Beginning + Flow Ending Transaction P&L Stock A 500-123.58 127.28 61,790 63,640 - Stock B 2,000 1,000 71.38 70.00 74.03 142,760 222,090 4,030 Stock C 1,350-42.61 44.38 57,524 59,913 - Portfolio 262,074 332,074 345,643 4,030 Impact of Transaction 1.214% 1.166% Conclusion As passive index investing solidifies its stronghold and expands to include new strategies, the market has lagged in adapting attribution approaches that address passive-specific tracking and reporting issues. Passive strategy investors and portfolio managers typically expect the portfolio to track to the benchmark, but since passive strategies are, practically speaking, not perfect mirrors of the benchmark due to the impact of transactions and constraints around portfolio construction, variation is not zero. Although they are small, these deviations are important to track and the ability to understand them is a common challenge for managers in the industry. Enabling this understanding is more critical than ever within the current regulatory environment, with managers under increased scrutiny to provide more transparency into performance drivers than ever before. Traditional attribution models provide some, but not all, of this transparency. By adding several factors to further explain excess returns in passive investing, the difference between the fund and benchmark return can be attributed with greater precision, giving portfolio managers and asset owners the most granular level of detail into their portfolio performance. Ultimately, an effective enhanced attribution offering should explain what traditional attribution models describe as residual, pairing these additional factors with benchmark relative allocation and selection effects. We re ready to help you access these capabilities through our enhanced attribution solution today, and we continually monitor the market and regulations to address additional attribution needs tomorrow so the true story of strategy performance can be told. STATE STREET CORPORATION 10
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