Figure 3.1 Net Flows into Equity Mutual Funds (Right) versus S&P Annual Returns (Left)
Data source: S&P returns: Standard & Poor’s; mutual fund flows: 2011 Investment Company Fact Book (Washington, DC: Investment Company Institute).
How do people decide when to invest? How do investors select among different alternatives? In virtually all investment decisions, the key driver is past returns. The investor calculus is simple: High returns are good; low returns or losses are bad. When the stock market has been rising, investor buying interest will increase. Conversely, after a period of market decline, investors will be more prone to liquidate than to invest.
The strong relationship between market returns and investor net flows into equity mutual funds is clearly evident in Figure 3.1. When Standard & Poor’s (S&P) 500 index returns turn significantly negative, the normal inflows into equity mutual funds are reversed. Net outflows from equity mutual funds occurred in 2002 and 2008 following large declines in equity prices. In each case, equity prices surged in the following year (2003 and 2009, respectively).
Figure 3.1 Net Flows into Equity Mutual Funds (Right) versus S&P Annual Returns (Left)
Data source: S&P returns: Standard & Poor’s; mutual fund flows: 2011 Investment Company Fact Book (Washington, DC: Investment Company Institute).
Returns determine not only when people invest but also what they invest in. Investments that have registered strong two-, three-, and five-year average returns will draw buying interest, while those with low, let alone negative, returns will be shunned. This investor behavior is quite understandable and influenced by numerous factors. To begin, it seems entirely logical to select investments that have demonstrated an ability to provide good returns. In addition, those investments that have done the best in recent years will also be the ones scored most highly by rating services. Not surprisingly, return-based advertising will feature funds that have done well, providing another spur to investor activity. Financial articles in newspapers and magazines will also focus on funds that have performed well. Investors who use software to select funds from a database will invariably select investment criteria that will generate a list of funds with strong recent returns, automatically filtering out lower-return funds. Portfolio optimization software, which is heavily dependent on returns, will also tend to select investments that have generated high past returns, albeit subject to volatility and correlation constraints. All of these factors will reinforce the natural investor tendency to select funds with high recent returns and to exclude laggards.
Clearly, people tend to invest in markets following periods of good performance and also tend to select investments that have demonstrated the best recent returns. The key question is: How well does this near-automatic reliance on past returns in making investment decisions serve investors? In quest of an answer, in subsequent sections we provide the analysis to answer the following four specific questions:
Clarifying Note: The following studies draw inferences from past market, sector, and strategy style performance following periods of high and low returns. There is, of course, no certainty that future results would show similar patterns. In all cases, however, the underlying assumption is that past performance patterns are indicative of the more likely patterns for the future. Readers should bear in mind that since the conclusions are based on empirical studies, they should be viewed as indications rather than absolute truths. Still, it seems more reasonable to invest in accordance with the empirical evidence than in opposition to it.
We segmented annual S&P returns for the 1871 to 2011 period into four quartiles and then compared the average returns in years following highest-quartile and lowest-quartile years.1 Returns following lowest-quartile years averaged 12.4 percent versus 10.8 percent following highest-quartile years and 10.5 percent in all years. We repeated an analogous process using three-year returns. The results were similar, but more pronounced, with average returns in years following lowest-quartile three-year returns outpacing returns following highest-quartile periods: 12.0 percent versus 9.9 percent. Finally, we repeated the test using past five-year periods. Here the difference was truly striking. The average return in years following lowest-quartile five-year returns was almost exactly double that of years following highest-quartile five-year returns (18.7 percent versus 9.4 percent). These results are summarized in Figure 3.2. The consistent superior performance of years following low-quartile return periods versus years following high-quartile return periods is clearly evident.
Figure 3.2 S&P Returns, Including Dividends: Comparison of Years Following Highest- and Lowest-Quartile Performance, 1872–2011
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!. Prior to 1926 (first year of S&P index), data is based on Cowles stock index data.
There is always a trade-off between more data and more relevant data. It can reasonably be argued that by going back as far as the 1870s, we included a period of history that is not representative of the current market. We therefore repeated the exact same analysis for the years 1950 forward. The results are summarized in Figure 3.3. Once again, years following low-quartile return periods significantly outperformed years following high-quartile periods, with the difference being 6 percent for the one-year period and nearly 4 percent for the three-year period.
Figure 3.3 S&P Returns, Including Dividends: Comparison of Years Following Highest- and Lowest-Quartile Performance, 1950–2011
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!.
The lesson is that the best prospective years for realizing above-average equity returns are those that follow low-return periods. Years following high-return periods, which are the times most people are inclined to invest, tend to do slightly worse than average on balance.
In the prior section, we examined the performance of the S&P in the single years following highest-return periods. Although the historical evidence suggests that these years performed significantly worse than years following low-return periods, an even more important question is: How do longer-term investments launched after high-return periods fare versus those started after low-return periods?
We segmented annual S&P 10-year returns for the period beginning in 1880 and ending between 1991 and 2011 into four quartiles. (The exact ending year depends on the length of the forward holding period tested.) Figure 3.4 shows the average annual return in the 5, 10, 15, and 20 years following both high- and low-quartile 10-year returns. There was little difference between the two for the 5-year forward period, but for the 10-, 15-, and 20-year forward periods, returns were about 2 percent per year higher following low-quartile 10-year returns than following high-quartile 10-year returns.
Figure 3.4 S&P Forward Period Average Annual Compounded Returns, Including Dividends, 1880–2011: Comparison of Years When Past 10-Year Returns Were in Lowest and Highest Quartiles
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!. Prior to 1926 (first year of S&P index), data is based on Cowles stock index data.
We then repeated an analogous experiment segmenting the data based on past 20-year returns. These results are shown in Figures 3.5. Returns were consistently higher in the forward periods follow low-quartile past returns by amounts ranging between 1.4 percent and 5.4 percent per year. On average across the four forward periods, returns were a substantive 3.5 percent per year higher following low-quartile periods than following high-quartile periods.
Figure 3.5 S&P Forward-Period Average Annual Compounded Returns, Including Dividends, 1890–2011: Comparison of Years When Past 20-Year Returns Were in Lowest and Highest Quartiles
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!. Prior to 1926 (first year of S&P index), data is based on Cowles stock index data.
Although, generally speaking, there is a benefit in using more data, perhaps going back as far as the late 1800s introduces data that is unrepresentative of the modern era and serves to distort the results. To address this possibility, we also repeated the same analysis for years 1950 forward. Restricting the analysis to this more recent data, the outperformance of post-lowest-quartile periods vis-à-vis post-highest-quartile periods was even more imposing. As shown in Figure 3.6, returns were higher following lowest-quartile 10-year returns in each of the four forward periods by amounts ranging from 1.1 percent to 6.4 percent.
Figure 3.6 S&P Forward-Period Average Annual Compounded Returns, Including Dividends, 1950–2011: Comparison of Years When Past 10-Year Returns Were in Lowest and Highest Quartiles
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!.
Based on past 20-year returns, the results were particularly striking. Returns in the periods following lowest-quartile 20-year returns exceeded returns following highest-quartile 20-year returns by amounts ranging between 6.6 percent and 11.0 percent!
The message is clear. The best time to start a long-term investment in equities is after an extended period of low returns—not surprisingly, the periods when investors are most likely to be disenchanted with stocks as an investment—and the worst time is after extended high-return periods (e.g., the late 1990s) when investors tend to most enthusiastic about stocks.
Readers might well wonder what the implications of past returns are for the current long-term investment horizon. As of the end of 2011 (the most recent year-end as of this writing), the past 10-year return was 2.9 percent per annum and the past 20-year return was 7.8 percent per annum (see Figure 3.7). These are relatively low return levels that correspond to the 14th and 11th percentiles, respectively, for the 10-year and 20-year average per annum returns for all year-ends since 1950. The only other year-ends when both these percentiles were below the 25th percentile were 1974, 1975, 1976, 1977, 1978, 1979, 1981, 1982, 2008, 2009, and 2010. Excluding the last three of these years, for which 10-year forward returns are not yet available, the forward average 10-year and 20-year returns for these years were both just under 16 percent per annum. Both the 10-year and 20-year return percentiles will remain below the 25th percentile as long as the 2012 return is 28 percent or less. In short, at this juncture (2012), barring a plus 28 percent return in 2012, the relatively poor performance of the stock market during the past 10-year and 20-year periods has constructive implications for stocks as a long-term investment.
Figure 3.7 S&P Forward-Period Average Annual Compounded Returns, Including Dividends, 1950–2011: Comparison of Years When Past 20-Year Returns Were in Lowest and Highest Quartiles
Data source: Moneychimp.com, which is based on Robert Shiller’s data and Yahoo!.
Searches for the highest-return mutual funds will invariably generate lists that are replete with sector focus funds because some sectors will always outperform broad market funds. Investors who select mutual funds based on highest past returns—a common approach—will end up indirectly investing in the sector or sectors that have realized the highest past returns in recent years. The obvious question is: Does the best-performing sector in recent years (and by implication most funds with the same sector focus) continue to perform better in the current year? To provide an answer, we utilize the 10 S&P sector indexes (see Table 3.1).
Table 3.1 S&P Sector Indexes
| Number | Index |
| 1 | Consumer Discretionary |
| 2 | Consumer Staples |
| 3 | Energy |
| 4 | Financials |
| 5 | Health Care |
| 6 | Industrials |
| 7 | Information Technology |
| 8 | Materials |
| 9 | Telecommunication Services |
| 10 | Utilities |
To evaluate the relative performance of the past best sector, we compare the outcome of three investment strategies:
In the first test, we use past one-year returns to define the best and worst sectors. Since 1990 is the first full year for which S&P sector index data is available, 1991 is the first year in the comparison analysis. Figure 3.8 illustrates the net asset value2 (NAV) graphs that result from each of the three investment strategies. Selecting the best past year sector at the start of each year results in a dramatically lower ending NAV than the equal allocation annual rebalancing implied by the average and does only modestly better than picking the prior year’s worst-performing strategy.
Figure 3.8 NAV Comparison: Prior One-Year Best S&P Sector versus Prior Worst and Average
Data source: S&P Dow Jones Indices.
Next, we conduct an analogous test using past three-year returns to define the best and worst sectors. Here, the first test year is 1993 because three prior years of data are needed to define the best and worst sectors. The NAV graphs for each of the three strategies are shown in Figure 3.9. In this instance, selecting the past best sector not only underperforms the average, but also lags picking the worst past sector.
Figure 3.9 NAV Comparison: Prior Three-Year Best S&P Sector versus Prior Worst and Average
Data source: S&P Dow Jones Indices.
We repeat the process a third time using the past five-year period to define the best and worst sectors. Since five years of data are needed to define the best and worst sectors, the first year for which a comparison can be made is 1995. The results are shown in Figure 3.10. Finally, in this third test, choosing the best past sector generates the highest NAV, significantly outdistancing both the average and the worst sector NAVs. Note, however, that the outperformance is achieved in a roller coaster ride—an important point to which we will soon return.
Figure 3.10 NAV Comparison: Prior Five-Year Best S&P Sector versus Prior Worst and Average
Data source: S&P Dow Jones Indices.
In two of the three test periods, choosing the best sector did worse than average and in one it did better. How can we combine these disparate results to yield an answer as to whether, based on past data, selecting the best past sector improves or hurts future performance? Since there is no a priori reason to favor one length of past return period over another, we assume that money is divided equally among all three. Thus, the best sector approach will allocate one-third of assets to the best-performing sector in the past year, one-third to the best-performing sector during the past three years, and the final third to the best-performing sector of the past five years. (Sometimes two or all three of these may be the same sector.) The worst sector approach will use an analogous allocation methodology. The average allocation will be the same as before. The results for the three-period combined analysis are shown in Figure 3.11. Selecting the best sector does slightly worse than the average but at least it does better than selecting the past worst sector. Based on these results, it might seem that although choosing the past best-performing sector doesn’t help, at least it doesn’t seem to hurt much, either. But the story does not end there.
Figure 3.11 NAV Comparison: Three-Period Prior Best S&P Sector versus Prior Worst and Average
Data source: S&P Dow Jones Indices.
So far, the analysis has only considered returns and has shown that choosing the best past sector would have yielded slightly lower returns than an equal-allocation approach (that is, the average). Return, however, is an incomplete performance metric. Any meaningful performance comparison must also consider risk (a concept we will elaborate on in Chapter 4). We use two measures of risk here:
Figure 3.12 compares the best sector, worst sector, and average results in terms of these two risk measures. The worst sector and average have similar risk levels in terms of both statistics. The best sector, however, has a significantly higher standard deviation and a far larger maximum drawdown. Calculating risk is not merely an academic exercise. Higher risk can dramatically alter the outcome of an investment. Although the best sector approach delivered only a slightly lower cumulative return than the average (Figure 3.11), any investors who followed this strategy would have been much more likely to abandon the investment in midstream because of its proclivity to huge drawdowns. These investors might likely never have realized an outcome near equal to the average. After all, in real time, investors don’t know that an investment will recover. In other words, the greater the risk, the more likely the investment would be liquidated at a loss.
Figure 3.12 Standard Deviation and Maximum Drawdown: Prior Best Sector (Three-Period Average) versus Prior Worst Sector and Average of All Sectors, 1995–2011
Data source: S&P Dow Jones Indices.
Figure 3.13 combines return and risk into two return/risk ratios. Both ratios show similar results: In return/risk terms, the best sector not only does much worse than the average, but it even underperforms the worst sector. The implications are that investors would be better off diversifying to achieve average returns than to concentrate their investment in the past best-performing sector. It follows that selecting the highest-return mutual funds of the past would also lead to subpar return/risk performance because these funds are likely to have an investment focus on the past best-performing sectors.
Figure 3.13 Return/Standard Deviation and Return/Maximum Drawdown Ratios: Prior Best Sector (Three-Period Average) versus Prior Worst and Average of All Sectors, 1995–2011
Data source: S&P Dow Jones Indices.
If your knowledge of hedge funds doesn’t extend much beyond the latest episode of your favorite TV drama series—if it’s a mystery, hint: The hedge fund manager did it—the only key fact you need to know here is that hedge funds encompass a broad range of strategies. In contrast to mutual funds, which primarily consist of long equity or long bond investments (or a combination of the two), hedge funds include a wide range of strategies, which differ in markets traded (equities, fixed income, foreign exchange [FX], credit, commodities); geographic focus (developed countries, emerging markets, single country, specific region); net exposure (net long, market neutral, net short, dynamically ranging); and directional versus relative value market orientation. A complete overview of hedge funds is provided in Chapter 11.
Not surprisingly, there is a strong tendency for many hedge fund investors to allocate to funds that have generated high returns in recent years and to redeem from those that have experienced significant losses. Although hedge fund managers are far more idiosyncratic than long-only managers, in many cases, the level of returns will be strongly influenced by the investment environment for the specific strategy style—particularly for some hedge fund categories. In this sense, although investors who move assets from the worst-return hedge funds in recent years to the best will be responding to individual manager performance, they will also be reflecting an indirect bias of shifting funds from the past weakest-performing hedge fund categories to the strongest. This implicit investor behavior raises the critical question: Does the best-performing hedge fund strategy category in recent years (and by implication most funds within that category) continue to perform better in the current year? To provide an answer, we utilize the 23 hedge fund sector indexes calculated by Hedge Fund Research, Inc. (HFRI), which are listed in Table 3.2.
Table 3.2 HFRI Hedge Fund Strategy Indexes*
| Index | |
| 1 | HFRI Equity Hedge (Total) Index |
| 2 | HFRI Equity Hedge: Equity Market Neutral Index |
| 3 | HFRI Equity Hedge: Quantitative Directional |
| 4 | HFRI Equity Hedge: Sector—Energy/Basic Materials Index |
| 5 | HFRI Equity Hedge: Sector—Technology/Health Care Index |
| 6 | HFRI Equity Hedge: Short Bias Index |
| 7 | HFRI Event-Driven (Total) Index |
| 8 | HFRI Event-Driven: Distressed/Restructuring Index |
| 9 | HFRI Event-Driven: Merger Arbitrage Index |
| 10 | HFRI Event-Driven: Private Issue/Regulation D Index |
| 11 | HFRI Macro (Total) Index |
| 12 | HFRI Macro: Systematic Diversified Index |
| 13 | HFRI Relative Value (Total) Index |
| 14 | HFRI Relative Value: Fixed Income—Asset Backed |
| 15 | HFRI Relative Value: Fixed Income—Convertible Arbitrage |
| 16 | HFRI Relative Value: Fixed Income—Corporate Index |
| 17 | HFRI Relative Value: Multi-Strategy Index |
| 18 | HFRI Relative Value: Yield Alternatives Index |
| 19 | HFRI Emerging Markets (Total) Index |
| 20 | HFRI Emerging Markets: Asia ex-Japan Index |
| 21 | HFRI Emerging Markets: Global Index |
| 22 | HFRI Emerging Markets: Latin America Index |
| 23 | HFRI Emerging Markets: Russia/Eastern Europe Index |
*Excludes fund of fund indexes, which combine multiple strategies.
The hedge fund strategy category indexes are not investable. So, it is not possible to replicate the returns of the indexes. The assumption for our test is that by randomly selecting a subset portfolio of one or more funds in the hedge fund category, the category index would serve as a proxy estimate for the expected return of the portfolio. Although the returns of any selected subset within a strategy could vary significantly from the index, there would be no bias to the direction of variation, and the strategy index return would serve as the best estimate of the single-strategy portfolio return. We compare the outcomes of three investment strategies.
In the first test, we use past one-year returns to define the best and worst hedge fund strategy categories. Figure 3.14 illustrates the net asset value (NAV) graphs that result for each of the three investment strategies. Selecting the hedge fund strategy with the highest return in the prior year ends up with a much lower final NAV than an investment that replicates the average of all strategies, and it even results in a slightly lower cumulative return than selecting the past year’s lowest-return strategy.
Figure 3.14 NAV Comparison: Prior One-Year Best HFRI Strategy Style versus Prior Worst and Average
Source: Data from HFR (www.hedgefundresearch.com).
Next, we conduct an analogous test using past three-year returns to define the best and worst hedge fund strategy categories. The NAV graphs for each of the three strategies are shown in Figure 3.15. In this instance, the underperformance of the past best sector is particularly striking. The final NAV based on an annual return equal to the average of all sectors is more than double the best sector’s ending NAV, while the worst sector’s ending NAV is nearly four times as high as the best.
Figure 3.15 NAV Comparison: Prior Three-Year Best HFRI Strategy Style versus Prior Worst and Average
Source: Data from HFR (www.hedgefundresearch.com).
We repeat the process a third time using the past five-year period to define the best and worst hedge fund strategies. The results are shown in Figure 3.16. Here again, the best sector substantially underperforms both the average and the worst strategy approaches, each of which ends up with an NAV nearly double the final NAV of the best sector.
Figure 3.16 NAV Comparison: Prior Five-Year Best HFRI Strategy Style versus Prior Worst and Average
Source: Data from HFR (www.hedgefundresearch.com).
To derive a single composite result that combines all three period results, we assume the best strategy method will allocate one-third of assets to the best-performing strategy in the past year, one-third to the best-performing strategy during the past three years, and the final third to the best-performing strategy of the past five years. (Sometimes two or all three of these may be the same strategy.) The worst strategy allocation will be analogous. The average allocation will be the same as before. The results for the three-period combined analysis are shown in Figure 3.17. As might have been expected from the individual period results, the best strategy provides by far the poorest return, with a final NAV that is less than half that of both the average and the worst strategy approaches.
Figure 3.17 NAV Comparison: Three-Period Prior Best HFRI Strategy Style versus Prior Worst and Average
Source: Data from HFR (www.hedgefundresearch.com).
As poorly as the best strategy performed in relative return terms, this is not the full story. Investing in the best strategy not only resulted in delivering much lower returns, but it did so with much greater risk. Figure 3.18 compares the relative risk of the three-period composite approaches using the standard deviation and maximum drawdown. The best strategy approach has a much higher standard deviation and a massively larger drawdown than both the average and worst strategy approaches. One aspect of Figure 3.18 is particularly notable: the enormous chasm between the maximum drawdown using the best strategy approach versus the worst strategy method, with the former being very high and the latter very low. This empirical evidence indicates that the past best-return strategy is particularly prone to a large drawdown, while the past worst-return strategy seems to have a below-average likelihood of realizing another very poor return year.
Figure 3.18 Standard Deviation and Maximum Drawdown: Prior Best Strategy (Three-Period Average) versus Prior Worst Strategy and Average of All Sectors, 1995–2011
Data source: S&P Dow Jones Indices.
Since the past best strategy was the worst future performer in terms of both return and risk measures, the outcome of a return/risk-based comparison is a foregone conclusion. The results of such a comparison are shown in Figure 3.19. For both return/risk metrics, the best strategy’s return/risk level is a fraction of the corresponding levels for the worst strategy and average of all strategies.
Figure 3.19 Return/Standard Deviation and Return/Maximum Drawdown Ratios: Prior Best Strategy (Three-Period Average) versus Prior Worst and Average of All Strategies, 1995–2011
Data source: Standard & Poor’s.
The lesson seems quite clear: In regard to hedge fund investing, favoring the best-return strategies of the past represents a highly misguided approach. As a general principle, an investor would be much better off doing the exact opposite—investing in the worst-return strategies of the past. To the extent that the performance of many hedge funds will be heavily influenced by the strategy category, the foregoing analysis implies that shifting assets from low-return to high-return managers may well degrade rather than improve future performance. This general conclusion, however, would not apply to hedge funds whose performance is relatively uncorrelated to their hedge fund category. Also, to avoid any confusion, our analysis showed only that past high-return strategies tend to underperform in the future. We did not at all address the question of whether the past best-performing funds in a given hedge fund category do better than the worst-performing funds in the same category.
We have seen that for equities, selecting the best sector provides only average returns, but with much greater risk. And for hedge funds, where the dispersion of returns across alternative investment strategies is far greater, choosing the prior best strategy drastically underperformed both the average and prior worst strategy. These observations raise the question of why the best-return investments of the past perform so poorly going forward. There are four plausible explanations:
Some readers may be uncomfortable about where this chapter seems to be heading. In the preceding sections, we showed that investing after high-return periods yielded poorer results than investing after low-return periods and that selecting the past best-performing equity sector or hedge fund strategy resulted in subpar future performance. Do these results imply that using past returns to select funds is a waste of time and possibly even counterproductive? Do we mean to imply that investors might be better off choosing funds with poor past performance than those with superior returns? The answer is yes and no—it depends on the degree to which a fund’s performance is determined by the market or sector.
If a fund is highly correlated to the market (or a sector)—as is true for virtually all long-only funds—its performance will be far more a reflective of the market than the fund’s investment process and skill. For example, a so-called closet index fund—a fund that is managed so that its performance does not deviate much from the selected index—would by design be highly correlated to the market. For a closet index fund, high returns would simply mean that the market had witnessed similar high returns and would provide no additional information about the fund’s relative merits. Although closet index funds may represent an extreme case, most long-only mutual funds are still highly correlated to whichever index most closely resembles the types of stocks in their portfolios (an index representing similar capitalization companies, sector, and country or region) and could be described as quasi-closet index funds. In contrast, a market neutral fund—a fund in which long and short positions are equally balanced—would likely have a low correlation to the market. In this case, performance results would reflect the fund manager’s stock-picking skill rather than the market direction.
Imagine you are watching cable news and see an ad for an energy sector fund with striking two-, three-, and five-year returns. Does this performance record imply that the fund manager is particularly skilled or that the fund is a good investment prospect? If this same fund is very highly correlated to the energy sector index, as is typically the case, then the fund’s performance would be nothing more than a reflection of the sector’s performance. And, as we have seen, superior sector performance is a poor, if not inverse, indicator of future performance.
Most long-only funds are far more influenced by a specific benchmark index than by any idiosyncratic management skill. In the world of long-only funds, even when skill plays a contributing role in achieving past performance, it is typically dwarfed by the market or sector influence. The lesson is that when you are looking at the past performance of long-only funds, you are primarily seeing the past performance of the market or sector—and that information, as the research studies in this chapter have shown, will be unhelpful, if not detrimental, in selecting investments for future performance.
The relevance of past returns in selecting hedge funds and CTA funds3 is a far more complex issue. Although for these types of managers past performance may reflect manager skill, there are still a number of major limitations to drawing conclusions about potential future performance based on past performance.
Similar to the situation for merger arbitrage and convertible arbitrage, the investment environment for the specific strategy plays a critical role in determining returns for managers in many other hedge fund categories as well. Therefore, for many hedge funds, past returns may be more an indication of the prior environment for a given strategy than a refection of the manager’s relative merits. And, insofar as the best-performing strategies in recent past years tend to dramatically underperform the worst-performing strategies going forward (as was demonstrated earlier in this chapter), it follows that for strategy-dependent managers, high return levels in recent years may be a negative rather than positive indicator.
For all these reasons, past returns may also be a useless, or even misleading, indicator in selecting hedge fund investments. Still, there is no intention to draw a broad-based conclusion that past returns are always irrelevant in selecting hedge funds and CTAs. Clearly, there are fund managers that achieve superior performance through skill. The Renaissance Medallion fund, which we detailed in Chapter 2, provides a prime example. So it would be at least theoretically possible to select potential future superior hedge fund managers on the basis of past returns. The bottom line for hedge funds is that past returns are often a poor indicator, but sometimes they may be useful; discerning between the two, however, is difficult. One guideline is that the more dependent a manager’s returns are on the market or strategy category, the less relevant past returns are.
Investor decisions are often driven by past returns. But there are good reasons, both theoretical and empirical, to question the relevance of past performance in timing or selecting investments. In fact, insofar as past returns have any relevance as an investment decision input, their significance is more often the exact reverse of what most investors believe it to be—that is, past high returns are more likely to be a negative rather than positive indicator.
Past superior performance is relevant only if the same conditions are expected to prevail—an expectation that is often unfounded and sometimes starkly contradicted by evolving events. As essayist George J. Church once wrote, “Every generation has its characteristic folly, but the basic cause is the same: People persist in believing what happened in the recent past will go on happening into the indefinite future, even when the ground is shifting under their feet.”
1S&P data based on series compiled by Robert Shiller, which uses Cowles stock index data prior to 1926.
2The net asset value (NAV) indicates the equity at each point of time (typically month-end) based on an assumed beginning value of 1,000 (sometimes 100). For example, an NAV of 2,000 implies that the original investment has doubled as of the indicated time. The NAV at any point is equal to the chain of compounded returns. For example, if the first three monthly returns were +10 percent, +5 percent, and −8 percent, the NAV would be equal to 1,062.6 (1,000 × 1.10 × 1.05 × 0.92 = 1,062.6).
3The term CTA stands for commodity trading adviser, the official designation for managers registered with the Commodity Futures Trading Commission (CFTC) and members of the National Futures Association (NFA), and is a misnomer on at least two counts: (1) A CTA is a fund or account manager with direct investment responsibility, and not an adviser as the name appears to suggest. (2) CTAs do not necessarily trade only commodities as the name implies. The vast majority of CTAs also trade futures contracts in one or more financial sectors, including stock indexes, fixed income, and FX. And, ironically, many CTAs do not trade any commodities at all, but trade only financial futures.