Triumph of the Optimists: 101 Years of Global Investment Returns
by Elroy Dimson, Paul Marsh and Mike Staunton · 3 Feb 2002 · 353pp · 148,895 words
success bias. This is because the back-histories are too often based on shares that were index members at the date the index went live. Survival bias occurs because the back-history has an almost total absence of companies that had disappeared by the time the index was launched. Success bias can
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us to look at value effects across the entire population of UK listed stocks over nearly half a century. The source data is free of survivorship bias, and covers some one hundred thousand firm-years of accounting data. Nagel’s database is comparable, and in some ways superior, to the US Compustat
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which we saw in section 3.2 is material in relation to the UK figure—the finger of suspicion has pointed mainly at success and survivorship bias among countries. The concern over success bias is that inferences about risk premia worldwide were being heavily influenced by the US experience, yet the United
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States has been an unusually successful economy. The closely related worry over survivorship bias is that previous attempts to place the experience of other countries like the United Kingdom alongside that of the United States may still have overstated
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need to focus on the experience of all countries, not just the United States and the United Kingdom. If we look at all markets, then survivorship bias ceases to be an issue. Our sample of sixteen countries is by no means comprehensive. However, it does represent a large proportion by value of
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ranked only sixth out of sixteen countries. The United Kingdom is near the middle of the distribution of worldwide equity premia. Concerns about success and survivorship bias, while legitimate, may therefore have been somewhat overstated. In this sense, investors may not have been materially misled by a focus on the United States
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returns can currently be estimated. Our own work, too, thus suffers from easy-data bias. Recently, there has been much concern in the literature about survivorship bias in markets. The concern is that long-run return studies, such as our own, document returns for surviving markets, and leave out the record for
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,000: Strategies for Profiting from the Greatest Bull Market in History. NY: McGraw Hill Elton, E.J., M.J. Gruber, and C.R. Blake, 1996, Survivorship bias and mutual fund performance, Review of Financial Studies 9: 1097–1120 Fama, E.F., 1975, Short-term interest rates as predictors of inflation. American Economic
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proportions in senior securities and equities under alternative holding periods. Journal of Portfolio Management 19(4): 30–36 Li, H., and Y. Xu, 2000, Can survival bias explain the equity premium puzzle? Working paper, Cornell University Graduate School of Management Litzenberger, R.H., and K. Ramaswamy, 1979, The effect of personal taxes
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, 189, 192, 195, 199, 204 Beta, 180, 214, 215 Bianchi, B., 264 Bias, 34–36 see also easy data bias, look-ahead bias, success bias, survivorship bias Bill markets, 19, 68 Bills, 68–73 Biscaini Cotula, A.M., 264 Bittlingmayer, G., xii, 254 Black, F., 141, 208 Blake, C.R., 35 Bodie
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bias, 6, 34, 36–8, 42–4, 174, 197 Sui, J.A., 211 Sullivan, T., xii Summer effect, 135, 138 Surveys, 179, 185–7, 188 Survivorship bias, 34–8, 41, 142, 173–5, 202, 222, 299 Sweden, 289–93 see also cross-country comparisons Switzer, L., xii, 239 Switzerland, 294–8 see
The New Science of Asset Allocation: Risk Management in a Multi-Asset World
by Thomas Schneeweis, Garry B. Crowder and Hossein Kazemi · 8 Mar 2010 · 317pp · 106,130 words
returns to databases, the returns before their database entry date may be biased upward relative to all those funds that do not report) and (2) survivorship bias: Funds that used to exist historically in 192 THE NEW SCIENCE OF ASSET ALLOCATION the database are removed from it when they stop reporting. Often
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is removed from a fund reporting to a database, the impact of backfill bias is removed dramatically. Similarly, most hedge fund indices do not contain survivorship bias or backfill bias, as managers reporting to the database at any one time are used. Historical index returns are not changed when these managers are
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removed from the database and therefore do not reflect survivorship bias. Likewise, as new managers are added to the database, the historical index returns are not changed in order to reflect those new managers and corresponding
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historical index returns. Hence, no backfill bias is contained in the many indices.3 The impact of survivorship bias and backfill bias, as well as the impact of the use of hedge fund indices to reflect the performance of individual hedge funds, is shown
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a higher return (7.8%) than that of the CISDM ELS index (4.9%) over the same period. This is consistent with both backfill and survivorship bias. (Note that the correlations with the S&P 500, BarCap US Government and Corporate High-Yield indices and the CISDM ELS index are similar at
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benchmarks may overestimate actual historical returns due to failure of the indices/benchmarks to correct for backfill bias (historical benchmark data includes current reporting managers); survival bias (managers who leave, generally due to poor performance, leave the database and the index is recalculated). Most indices, including most hedge fund and managed futures
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an index may contain survivorship and backfill bias. For instance, if an index was started in 2002, returns pre-2002 would contain backfill bias and survivorship bias. CHAPTER 9 Risk Budgeting and Asset Allocation sset allocation and risk management are about finding the right balance of risk and return. In this chapter
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, 91, 99–100, 113 Strategic asset management, 2 Style purity, 126–132 Subprime market, 228–229 Surplus-at-Risk (SAR)/Liability Driven Investment (LDI), 34 Survivorship bias, 192, 194 Swaps, 15 Tactical asset allocation (TAA), 2, 12, 91–92, 101–106 Taxability, 62 Tax codes, 197 Term premium, 103 Term spread, 52
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by Ernie Chan · 17 Nov 2008
Are Its Returns? 18 How Deep and Long Is the Drawdown? 21 How Will Transaction Costs Affect the Strategy? 22 Does the Data Suffer from Survivorship Bias? 24 How Did the Performance of the Strategy Change over the Years? 24 vii P1: JYS fm JWBK321-Chan September 24, 2008 13:43 Printer
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32 TradeStation 35 High-End Backtesting Platforms 35 Finding and Using Historical Databases 36 Are the Data Split and Dividend Adjusted? 36 Are the Data Survivorship Bias Free? 40 Does Your Strategy Use High and Low Data? 42 Performance Measurement 43 Common Backtesting Pitfalls to Avoid 50 Look-Ahead Bias 51 Data
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quality that may be even more important than their frequencies: whether the data are free of survivorship bias. I will define survivorship bias in the following section. Here, we just need to know that historical stock data without survivorship bias are much more expensive than those that have such a bias. Yet if your data have
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survivorship bias, the backtest result can be unreliable. The same consideration applies to news—whether you can afford a high-coverage, real
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liquidity. See “Choosing a Brokerage or Proprietary Trading Firm” in Chapter 4.) Despite my frequent admonitions here and elsewhere to beware of historical data with survivorship bias, when I first started I downloaded only the split-and-dividend-adjusted Yahoo! Finance data TABLE 2.2 How Capital Availability Affects Your Many Choices
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Low Capital High Capital Proprietary trading firm’s membership Futures, currencies, options Intraday Directional Small stock universe for intraday trading Daily historical data with survivorship bias Low-coverage or delayed news source No historical news database Retail brokerage account Everything, including stocks Both intra- and interday (overnight) Directional or market neutral
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Large stock universe for intraday trading High-frequency historical data, survivorship bias–free High-coverage, real-time news source Survivorship bias–free historical news database Survivorship bias–free historical fundamental data on stocks No historical fundamental data on stocks P1: JYS c02 JWBK321-Chan 16 September 24
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to come QUANTITATIVE TRADING using the download program from HQuotes.com (more on the different databases and tools in Chapter 3). This database is not survivorship bias–free—but more than two years later, I am still using it for most of my backtesting! In fact, a trader I know, who each
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can this be possible? Probably because these are intraday strategies. It seems that the only people I know who are willing and able to afford survivorship bias–free data are those who work in money management firms trading tens of millions of dollars or more (that includes my former self). So, you
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Example 3.7. P1: JYS c02 JWBK321-Chan September 24, 2008 13:47 Printer: Yet to come 24 QUANTITATIVE TRADING Does the Data Suffer from Survivorship Bias? A historical database of stock prices that does not include stocks that have disappeared due to bankruptcies, delistings, mergers, or acquisitions suffer from the so
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-called survivorship bias, because only “survivors” of those often unpleasant events remain in the database. (The same term can be applied to mutual fund or hedge fund databases
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that do not include funds that went out of business.) Backtesting a strategy using data with survivorship bias can be dangerous because it may inflate the historical performance of the strategy. This is especially true if the strategy has a “value” bent; that
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when you read about a “buy on the cheap” strategy that has great performance, ask the author of that strategy whether it was tested on survivorship bias–free (sometimes called “point-in-time”) data. If not, be skeptical of its results. (A toy strategy that illustrates this can be found in Example
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transaction cost today was applicable throughout the backtest, the earlier period would have unrealistically high returns. Survivorship bias in the data might also contribute to the good performance in the early period. The reason that survivorship bias mainly inflates the performance of an earlier period is that the further back we go in our
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have a high enough Sharpe ratio? r Does it have a small enough drawdown and short enough drawdown duration? r Does the backtest suffer from survivorship bias? r Does the strategy lose steam in recent years compared to its earlier years? P1: JYS c02 JWBK321-Chan September 24, 2008 Fishing for Ideas
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multiple symbols. Low cost. Split/dividend adjusted. Software enables download of multiple symbols. Fundamental data available. Survivorship bias free. Has survivorship bias. Can download only one symbol at a time. Has survivorship bias. Split but not dividend adjusted. Has survivorship bias. Has survivorship bias. Expensive. Updated only once a month. Daily Futures Data Quotes-plus.com CSIdata.com Oanda.com
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and splits that result in the same adjusted prices as your current Yahoo! table. Are the Data Survivorship Bias Free? We already covered this issue in Chapter 2. Unfortunately, databases that are free from survivorship bias are quite expensive and may not be affordable for a start-up business. One way to overcome this
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all the stocks in your universe to a file, then you will have a point-in-time or survivorship-bias-free database to use in the future. Another way to lessen the impact of survivorship bias is to backtest your strategies on more recent data so that the results are not distorted by too
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many missing stocks. P1: JYS c03 JWBK321-Chan September 24, 2008 13:52 Printer: Yet to come Example 3.3: An Example of How Survivorship Bias Can Artificially Inflate a Strategy’s Performance Here is a toy “buy low-price stocks” strategy (Warning: This toy strategy is hazardous to your financial
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year and hold them (with equal initial capital) for one year. Let’s look at what we would have picked if we had a good, survivorship-bias-free database: SYMBOL ETYS MDM INTW FDHG OGNC MPLX GTS BUYX PSIX Closing Price on 1/2/2001 Closing Price on 1/2/2002 0
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total return on this portfolio in that year was –42 percent. Now, let’s look at what we would have picked if our database had survivorship bias and actually missed all those stocks that were delisted that year. We would then have picked the following list instead: SYMBOL MDM ENGA NEOF ENP
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example, –42 percent was the actual return a trader would experience following this strategy, whereas 388 percent is a fictitious return that was due to survivorship bias in our database. Does Your Strategy Use High and Low Data? For almost all daily stock data, the high and low prices are far noisier
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, an erroneous backtest would produce a historical performance that is better than what we would have obtained in actual trading. We have already seen how survivorship bias in the data used for backtesting can result in inflated performance. There are, P1: JYS c03 JWBK321-Chan September 24, 2008 Backtesting 13:52 Printer
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still the question of how to retrieve historical data for hundreds of symbols, especially survivorship-biasfree data. Here, we will put aside the question of survivorship bias because of the expensive nature of such data and just bear in mind that whatever performance estimates we obtained are upper bounds on the actual
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come 67 performance of the strategy from its backtest performance. Issues discussed here include: r Data: Split/dividend adjustments, noise in daily high/low, and survivorship bias. r Performance measurement: Annualized Sharpe ratio and maximum drawdown. r Look-ahead bias: Using unobtainable future information for past trading decisions. r Data-snooping bias
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trading model is wrong. It could be wrong for a large number of reasons, some of which were detailed in Chapter 3: data-snooping bias, survivorship bias, and so on. To eliminate all these different biases and errors in the backtest programs, it is extremely helpful to have a collaborator or consultant
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must make sure the data is thoroughly cleansed of such fictitious quotes before one can completely trust your backtesting performance on a mean-reverting strategy. Survivorship bias also affects the backtesting of mean-reverting strategies disproportionately, as I discussed in Chapter 3. Stocks that went through extreme price actions are likely to
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. However, these stocks may not appear at all in your historical database if it has survivorship bias, thus artificially inflating your backtest performance. You can look up Table 3.1 to find out which database has survivorship bias. Momentum can be generated by the slow diffusion of information—as more people become aware of
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source code can be downloaded from epchan.com/ book/example7 7.m. The data is also available at that site.) Note that the data contains survivorship bias, as it is based on the S&P 500 index on November 23, 2007. clear; load(’SPX 20071123’, ’tday’, ’stocks’, ’cl’); P1: JYS c07 JWBK321
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Mean-reverting regimes are more prevalent than trending regimes. r There are some tricky data issues involved with backtesting mean-reversion strategies: Outlier quotes and survivorship bias are among them. r Trending regimes are usually triggered by the diffusion of new information, the execution of a large institutional order, or “herding” behavior
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MATLAB TradeStation, 35 historical databases, finding and using, 36–43 high and low data, use of, 42–43 split and dividend-adjusted data, 36–40 survivorship bias, 40–42 January effect, 144–146 performance measurement, 43–50 strategy refinement, 65–66 transaction costs, 61–65 year-on-year seasonal trending strategy, 146
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Historical databases errors in, 117 finding and using, 36–43 high and low data, use of, 42–43 split and dividend-adjusted data, 36–40 survivorship bias, 40–42 HQuotes.com, 37, 81 Hulbert, Mark (New York Times), 10 I Information ratio. See Sharpe ratio Information, slow diffusion of, 117–118 Interactive
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, 2008 14:7 Index data-snooping bias, 25–27 drawdown, 21–22 strategies unnoticed by institutional money managers, 27 survivorship bias, 24 transaction costs, effect on strategy, 22–23 Strategy refinement, 65–66 Survivorship bias, 14, 24, 40–42 and artificial inflation of a strategy’s performance, 41–42 effect on backtesting of meanreverting
Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant and Fabrice Douglas Rouah · 23 Sep 2004
CHAPTER 3 Performance of Managed Futures: Persistence and the Source of Returns 31 B. Wade Brorsen and John P. Townsend CHAPTER 4 CTA Performance, Survivorship Bias, and Dissolution Frequencies 49 Daniel Capocci CHAPTER 5 CTA Performance Evaluation with Data Envelopment Analysis 79 Gwenevere Darling, Kankana Mukherjee, and Kathryn Wilkens v
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the biggest databases ever employed in performance analysis studies to determine if some funds consistently and significantly over- or underperform. The chapter also analyzes the survivorship bias present in CTAs as well as the dissolution frequencies of these funds. Chapter 5 applies data envelopment analysis (DEA) to a performance evaluation framework
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funds, the database at our disposal as of May 2001 contained monthly netof-fee returns on 1,195 live and 526 dead funds. To avoid survivorship bias, we created 455 seven-year monthly return series by, beginning with the 455 8 PERFORMANCE funds that were alive in June 1994, replacing
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determined quarterly based on assets under management. When a trading program closes down, the index does not get adjusted backward, which takes care of survivorship bias issues. All 300 of the CTAs in the index are classified by their trading approach and market category. Currently the index contains 248 systematic
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(CTAs) than for hedge funds (3.30 percent versus 2.23 percent). Liang (2003), perhaps surprisingly, drew the same conclusion with respect to survivorship bias, which turns out to be significantly higher in the case of CTAs (5.85 percent versus 2.32 percent). Table 2.1 illustrates the consequences
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not a major concern here, because the comparison is among CTAs, not between CTAs and some other investment. Faff and Hallahan (2001) argue that survivorship bias is more likely to cause performance reversals than performance persistence. The data used show considerable kurtosis (see Table 3.1). However, this kurtosis may
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are now charging higher fees. CTA returns decreased over time and more recent funds have lower returns. At least part of this trend is likely survivorship bias. As dollars under management increased, CTA returns decreased. The finding of fund returns decreasing over time (and as dollars invested increase) suggests that
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paint a consistent picture. To adequately select CTAs or funds based on past returns, several years of data are needed. CHAPTER 4 CTA Performance, Survivorship Bias, and Dissolution Frequencies Daniel Capocci sing a database containing 1,892 funds (including 1,350 dissolved funds), we investigate CTA performance and performance persistence to
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We examine performance across deciles and across CTA strategies to determine if some deciles are more exposed to certain strategies over time. We also analyze survivorship bias and its evolution over time. We conclude the study by analyzing the dissolution frequencies across deciles and their evolution over time. U INTRODUCTION AND
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put is a normal put option, but the strike depends on the maximum stock price reached during the life of the option. CTA Performance, Survivorship Bias, and Dissolution Frequencies 51 the next section, we describe the database, reporting the descriptive statistics of the funds and analyzing the correlation between the various
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strategies reported. The following section focuses on survivorship bias. We analyze the presence of this bias over the whole period studied but also over different time periods, including a bull and a bear market
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funds at −0.21. There are nine negative coefficients in total representing 14 percent of the coefficients. SURVIVORSHIP BIAS Performance figures are subject to various biases. One of the most important is the survivorship bias that appears when only surviving funds are taken into account in a performance analysis study. The common
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data on investable funds that are currently in operation. When only living funds4 are considered, the data suffer from survivorship bias because dissolved funds tend to have worse performance than surviving funds. Survivorship bias has already been studied. Fung and Hsieh (1997b) precisely analyzed this bias and estimated it at 3.4
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percent per year. They also concluded that survivorship bias had little impact on the investment styles of CTA funds. Returns of both surviving and dissolved CTA funds have low correlation to the standard asset
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classes. Survivorship Bias over Various Time Periods Here we analyze the presence of survivorship bias in CTAs returns over various long-term time periods. We first study the whole period covered before dividing it
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into subperiods. Table 4.4 reports the survivorship bias obtained from our database. Survivorship bias is calculated as the performance difference between surviving funds and all funds. All returns are monthly and net of all fees. The
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first part of the table indicates a survivorship bias of 5.4 percent per year for the entire period. This figure is higher than the one obtained in previous studies. Table 4.4
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4.4 percent). 4By “living funds” we mean funds still in operation at the moment of the analysis. CTA Performance, Survivorship Bias, and Dissolution Frequencies 57 TABLE 4.4 Survivorship Bias Analysis over Different Periods Bias 1985–2003 Bias 1985–1989 Bias 1990–1994 Bias 1995–1999 Bias 2000–2003 0.5 5
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Year Our database contains 1,899 CTAs (611 survived funds and 1,288 dissolved funds as of December 2002). Survivorship Bias over Time Figure 4.1 reports the evolution of the survivorship bias calculated on a three-year rolling period starting January 1985 to December 1987 and ending January 2000 to December
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93 94 94 95 95 96 96 97 97 98 98 99 99 00 00 01 01 02 02 FIGURE 4.1 Evolution of the Survivorship Bias (3-year Rolling Period) Our database contains 1,899 CTAs (611 survived funds and 1,288 dissolved funds as of December 2002). Numbers
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around 0.55 percent for the periods ending between January 1994 and January 2000. Because the three-year periods end January 2000, the monthly survivorship bias decreases almost constantly to 0.12 percent in December 2002. We analyze these results to determine how such variations are possible. On one hand,
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against −1.85 percent in March, −2.54 percent against −0.91 percent in April). On the other hand, the sharp increase in survivorship bias over the period ending November and December 1992 can be explained mainly by high overperformance in June, July, and August 1992 with an average of
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the difference between surviving funds and dissolved funds was less important. We also analyze the survivorship bias calculated over the positive and negative months5 for the whole database. Interestingly, Table 4.5 indicates that the mean survivorship bias is the same over the three periods studied at 0.48 percent. The standard
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consider a month as negative if the whole database does not reach positive returns. 59 CTA Performance, Survivorship Bias, and Dissolution Frequencies TABLE 4.5 Descriptive Statistics of the 3-Year Rolling-Period Survivorship Bias Whole period Positive months Negative months Mean Std. Dev. Median Min Max 0.48 0.48 0.48
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0.13 0.90 0.90 0.87 Std. dev. = standard deviation; Min = minimum; and Max = maximum of the 3-year rolling-period survivorship bias calculated over the whole period studied (January 1985–December 2002). one index per CTA strategy. To test if some funds significantly outperform the indices, we
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Global Index is composed of all the individual funds classified in the various strategies. It is the same funds classified differently. 61 CTA Performance, Survivorship Bias, and Dissolution Frequencies TABLE 4.6 Relative Performance Analysis of Strategy Indices Technically diversified Technically financial and metals Technically currency Technically other Fundamental Discretionary Systematic
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period, many strategies significantly outperform the CTA Global Index. Astonishingly, technically diversified and technically financial/metals that respectively significantly under- and out- CTA Performance, Survivorship Bias, and Dissolution Frequencies 65 perform during the whole January 1985 to December 2002 period do not significantly deviate from the index over the last 10
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0.87 Min = minimum; Max = maximum. Std. Dev. = standard deviation; t-stat are heteroskedasticity consistent. Numbers in the table are monthly percentages. CTA Performance, Survivorship Bias, and Dissolution Frequencies 67 ation of 1.84 percent. The average beta (in our case the beta is measured relative to our CTA Global Index
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means that these subdeciles do not contain a lot of funds, which leads to less stable returns compared to whole deciles. 69 CTA Performance, Survivorship Bias, and Dissolution Frequencies TABLE 4.11 CTA Persistence in Performance, January 1986 to December 2002 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10
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D1b D1c D10b D10c D10c Index Bull Market Period Mar 2000 Alpha Jan 1998– TABLE 4.12 Persistence in Performance Subperiod Analysis CTA Performance, Survivorship Bias, and Dissolution Frequencies 71 analyses are less significant.9 The table also indicates that each decile is significantly exposed to the CTA Global Index.
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of the funds were dissolved in the year after their classification in the top- or worst-performing decile. As we noted in analyzing the survivorship bias, this bias is important in CTA data and we have to take this factor into account. 10We have analyzed the relationship between the various
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D8 D9 D10 D10a D10b D10c D1a D1b D1c Arb Alpha TABLE 4.13 Decile Performance Analysis, January 1986 to December 2002 CTA Performance, Survivorship Bias, and Dissolution Frequencies 73 and D10c are significantly negative. All these figures are different from the ones obtained in the performance or performance persistence analysis
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analyze the dissolution frequencies in our database, defined as the number of funds that stopped reporting to the database. This measure is similar to survivorship bias, the difference being that we analyze it per decile each year based on the previous year’s performance. This analysis is interesting because it
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for D7, 11 percent for D8, and 8.8 percent for D9. D9 has the lowest dissolution frequencies of all the 75 CTA Performance, Survivorship Bias, and Dissolution Frequencies 45% 30% 15% Mean D10 D9 D8 D7 D6 D5 D4 D3 D2 D1 0% FIGURE 4.2 Average Dissolution Frequencies
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FIGURE 4.3 88 90 92 94 96 98 00 02 Evolution of the Yearly Dissolution Frequencies across Deciles between 1986 and 2002 CTA Performance, Survivorship Bias, and Dissolution Frequencies 77 60% 50% 40% 30% 20% 10% 0% −10% 86 87 88 89 90 91 92 93 94 95 96
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and then vary over time, but they are higher between 1999 and 2001 depending on the deciles considered. CONCLUSION In this study, we investigate CTA survivorship bias, performance, and performance persistence. After having made a literature review and analyzing the descriptive statistics, we have analyzed the correlation between the various CTA
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strategies. Our results indicate that most of the strategies defined are weakly correlated, indicating a need to separate the funds into investment strategies. The survivorship bias analysis indicates that our CTA database contains a bias of 5.4 percent per year over the whole January 1985 to 78 PERFORMANCE December
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, which is barely higher that the estimated risk-free rate (4.2 percent). 2. For technical reasons, we have not accounted for possible survivorship bias, which may be expected to have a substantial impact on the overall performance. Figure 16.2 displays the excess replicating returns for the nine-moment
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ranking properties of the moment-based replicating efficiency measures on a sample of CTA managed funds. Summing up, we found that (neglecting any possible survivorship bias) using these measures, the majority of the funds investigated had a performance superior to the S&P 500. We also found that the moment
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proxy the same industry). In practice, it is rarely the case. Indices are constructed using different methodologies (each methodology defines rebalancing dates, index component selections, survivorship bias correction, etc.) and, even more important, different data sources. It generates, most of the time, significant patterns dissimilarity between them. In the case of
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2001; Kothari and Warner 2001). Unfortunately, CTA database vendors only provide monthly data. 1The Zurich indices during the investigation period do not suffer from survivorship bias. 329 Random Walk Behavior of CTA Returns TABLE 18.1 Zurich Advisor Qualified Universe Indices as of December 2000 Trading Style Subindex Number of Advisors
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Vol. 50, No. 6, pp. 32–45. Faff, R. W., and T. A. Hallahan. (2001) “Induced Persistence or Reversals in Fund Performance? The Effect of Survivorship Bias.” Applied Financial Economics, Vol. 11, No. 2, pp. 119–126. Fama, E. F., and K. R. French. (1993) “Common Risk Factors in the Returns
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: The Case of Hedge Funds.” Review of Financial Studies, Vol. 10, No. 2, pp. 275–302. Fung, W., and D. A. Hsieh. (1997b) “Survivorship Bias and Investment Style in the Returns of CTAs: The Information Content of Performance Track Records.” Journal of Portfolio Management, Vol. 24, No. 1, pp. 30
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providers for, 208–209 INDEX methodologies employed by, 242–244 quantitative description of, 308–312 strategies for, see Strategies, CTA styles of, 80, 287–288 survivorship bias for hedge funds vs., 19 systematic, 80, 243–244, 387 technical vs. fundamental analysis by, 242 trend-following, 7, 80, 244 Compensation of CTAs
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Market volatility managed futures as subset of, 80 managed futures combined with, 11 risk and dependence characteristics of, 5–6 short-volatility strategies for, 198 survivorship bias for CTAs vs., 19 Tremont TASS database for, 7 Hedgers, 241 Herding behavior, 152–153 IMAs, see Individually managed accounts Incentive fees, 40–44
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of, 47 measures of, 79–80 persistence of, see Performance persistence previous studies on, 49–50 risk measures and evaluation of, 82–87 and survivorship bias, 56–58 from 1990 to 2003, see Market conditions (1990-2003) Performance persistence, 31–48 Barclay Trading Group data study, 51–77 and characteristics
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observations, 341–351 literature review of, 337–338 methodology for, 340 sources of data for, 340 Styles, CTA, 80, 88, 90, 101, 242–244 Survivorship bias, 19, 56–58 Sydney Futures Exchange (SFE), 261, 262, 270–271 Systematic CTAs, 80, 243–244, 387 INDEX TASS Management, 51 Technical analysis, 242
Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen · 4 Apr 2011 · 1,088pp · 228,743 words
, the historical performance data that investors get to see are often upward biased. This bias is due to the voluntary nature of performance reporting and survivorship bias (so that poor performers are left out of databases or are not marketed by the fund manager). A similar caveat applies to simulated “paper” portfolios
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. For active asset managers with voluntary reporting, published returns are almost certainly upward biased. Section 11.4 reviews a host of selection biases such as survivorship bias and backfill bias in the context of hedge fund return databases, but similar caveats apply to the reported performance of other managers. Backtested results of
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winners to use in marketing. Biases are larger in industries with voluntary reporting: databases purportedly describing the whole industry can overstate industry returns due to survivorship bias, backfill bias, and other biases (see Section 11.3). These caveats are more relevant to forming a skeptical assessment of high average returns than they
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idiosyncratic risk), non-standard utility functions (habit formation, recursive utility), modified consumption data (durable goods, luxury goods, long-term consumption risk), and biased sample explanations (survivorship bias among countries studied, absence of negative rare events in the sample, unexpected repricing of equities or bonds) as rational explanations for high observed equity outperformance
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success of the U.S. economy. Even multi-country studies involve various biases which suggest that realized market returns exceed the returns that were anticipated:• Survivorship bias raises the odds that we examine countries that have had good or at least continuous capital market performance (say, the G5 as opposed to Russia
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) equity returns and premia, 1900–2009 Source: Dimson–Marsh–Staunton (2010). Lower premium if realized returns are adjusted for unexpected windfall gains Despite concerns about survivorship bias, peso problems, and time-varying expected returns, many investment textbooks still use historical excess returns as a proxy for the ex ante risk premium. Historical
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all funds that were live at the end of the sample they get a 14.3% annual return; • including both “live” and “dead” funds removes survivorship bias: net return falls to 11.1%; • adjusting for backfill bias (by only including returns starting from the date each fund first reports to the database
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a combination of them, covers only a subset of funds—and likely a flattering subset. The main quantifiable biases in published hedge fund returns are survivorship bias and backfill bias. Underlying most biases is the voluntary nature of reporting to such databases and the flexibility that database providers give to reporting funds
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academic papers discuss and quantify these biases, they perhaps are not fully appreciated by HF investors. The list of biases is long and partly overlapping:• Survivorship bias. Funds leave the database when they die. There is strong evidence that extinct funds in the “graveyard” module of HF databases earned lower average returns
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months. Short-lived funds that are ignored tend to have lower returns. Only the first two biases have been extensively quantified. Several studies indicate that survivorship bias is between 2% and 3%. That is, using only “live” funds at the end of the sample overstates industry returns by 2% to 3% (because
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. Bhardwaj–Gorton–Rouwenhorst (2008) present a scathing analysis of CTA performance, titled “Fooling some of the people all the time”. They find that adjusting for survivorship bias and backfill bias (using the first reporting day filter), reported CTA annualized return (1994–2007) drops from 12.6% to 4.9%, barely above the
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bias after that point. The HFR index goes further back (published data start in 1990) but at least in the early years without adjusting for survivorship bias. One could also expect the value-weighted DJ CS index to exhibit milder biases than equally weighted fund indices such as the HFR, so it
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measures survey-based returns consensus forecasts criticisms data problems multiple asset classes rational/irrational explanations stale data subjective returns timeliness of data use of averages survivorship bias swap—Treasury spreads swaps CDSs interest rate variance Swensen, David systematic managers systematic risk tactical forecasting assets BRP combining models corporate bonds cross-asset selection
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
by Nassim Nicholas Taleb · 1 Jan 2001 · 111pp · 1 words
seriously. Soros promotes Popper. That bookstore on Eighteenth Street and Fifth Avenue. Pascal’s wager. EIGHT: TOO MANY MILLIONAIRES NEXT DOOR Three illustrations of the survivorship bias. Why very few people should live on Park Avenue. The millionaire next door has very flimsy clothes. An overcrowding of experts. NINE: IT IS EASIER
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TO BUY AND SELL THAN FRY AN EGG Some technical extensions of the survivorship bias. On the distribution of “coincidences” in life. It is preferable to be lucky than competent (but you can be caught). The birthday paradox. More charlatans
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who enter it, not the sample of those who have succeeded in it. We will examine the point later from the vantage point of the survivorship bias, but here, in Part I, we will look at it with respect to resistance to randomness. Consider two neighbors, John Doe A, a janitor who
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the bull market for European bonds of 1993, whom I openly considered nothing better than random gunslingers. I tried presenting him with the notion of survivorship bias (Part II of this book) in vain. His traders have all exited the business since then “to pursue other interests” (including him). But he gave
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like flies, with, for instance, “legendary”(rather, lucky) investor Julian Robertson closing shop in 2000 after having been a star until then. Our discussion of survivorship bias will enlighten us further, but, clearly, there is nothing less rigorous than their seemingly rigorous use of economic analyses to trade. A tendency to get
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traders who made every single mistake in the book become so successful? Because of a simple principle concerning randomness. This is one manifestation of the survivorship bias. We tend to think that traders were successful because they are good. Perhaps we have turned the causality on its head; we consider them good
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) the biological handicap of our inability to understand probability (Chapter 11, “Randomness and Our Brain”). Eight • TOO MANY MILLIONAIRES NEXT DOOR Three illustrations of the survivorship bias. Why very few people should live on Park Avenue. The millionaire next door has very flimsy clothes. An overcrowding of experts. HOW TO STOP THE
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in the Chekhovian dilemmas in the private lives of Marc and Janet, but their case provides a very common illustration of the emotional effect of survivorship bias. Janet feels that her husband is a failure, by comparison, but she is miscomputing the probabilities in a gross manner—she is using the wrong
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). But the benefits promised in the book seem grossly overstated. A finer read of their thesis reveals that their sample includes a double dose of survivorship bias. In other words, it has two compounding flaws. Visibility Winners The first bias comes from the fact that the rich people selected for their sample
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further by cashing them from the Soviet government, or Argentine real estate in the 1930s (as my great-grandfather did). The mistake of ignoring the survivorship bias is chronic, even (or perhaps especially) among professionals. How? Because we are trained to take advantage of the information that is lying in front of
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tend to mistake one realization among all possible random histories as the most representative one, forgetting that there may be others. In a nutshell, the survivorship bias implies that the highest performing realization will be the most visible. Why? Because the losers do not show up. A GURU’S OPINION The fund
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, others fail and disappear from the analyses. Sadly. Nine • IT IS EASIER TO BUY AND SELL THAN FRY AN EGG Some technical extensions of the survivorship bias. On the distribution of “coincidences” in life. It is preferable to be lucky than competent (but you can be caught). The birthday paradox. More charlatans
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-known counterintuitive properties of performance records and historical time series. The concept presented here is well-known for some of its variations under the names survivorship bias, data mining, data snooping, over-fitting, regression to the mean, etc., basically situations where the performance is exaggerated by the observer, owing to a misperception
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inferred, that of the best performers. We call the difference between the average of such distribution and the unconditional distribution of winners and losers the survivorship bias—here the fact that about 3% of the initial cohort discussed earlier will make money five years in a row. In addition, this example illustrates
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hike by Alan Greenspan). The interesting part is that several years later I can hardly find any of them still trading (ergodicity). Recall that the survivorship bias depends on the size of the initial population. The information that a person derived some profits in the past, just by itself, is neither meaningful
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has the odds markedly stacked in her favor, but who still ends up going to the cemetery. This effect is the exact opposite of the survivorship bias. Consider that all one needs is two bad years in the investment industry to terminate a risk-taking career and that, even with great odds
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by Sullivan, Timmerman, and White goes further and considers that the rules that may be in use successfully today may be the result of a survivorship bias. Suppose that, over time, investors have experimented with technical trading rules drawn from a very wide universe—in principle thousands of parameterizations of a variety
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was that while I suspected that he was fooled by randomness, the extent had to be far greater than one could imagine, particularly with the survivorship bias. A back of the envelope calculation showed that at least 97% of what he was discussing was just noise. The fact that he was comparing
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start soliciting the patients. The Dog That Did Not Bark: On Biases in Scientific Knowledge By the same argument, science is marred by a pernicious survivorship bias, affecting the way research gets published. In a way that is similar to journalism, research that yields no result does not make it to print
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is clearly small, and they are the ones generally observed by the public as representative of the profession, as we saw in our discussion on survivorship bias. The winners would move into Bel Air, feel pressure to acquire some basic training in the consumption of luxury goods, and, perhaps owing to the
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and looking for possible explanations. Very little consideration was given to the possibility that the premium may have been an optical illusion owing to the survivorship bias—or that the process may include the occurrence of black swans. The discussion seems to have calmed a bit after the declines in the equity
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of the normative theory of asset pricing to compute the expected portfolio returns given some risk profile, not as a statistical device.) Not counting the survivorship bias, over a given twelve-month period, assuming (very generously) the Gaussian distribution, the “Sharpe ratio” differences for two uncorrelated managers would exceed 1.8 with
The Power of Passive Investing: More Wealth With Less Work
by Richard A. Ferri · 4 Nov 2010 · 345pp · 87,745 words
random sampling of stocks by 0.2 percent over the period. However, there was likely a strong survivorship bias in the data that resulted in a deceptively high average return for the remaining entities. Survivorship bias occurs in performance data when the entire return histories of non-surviving entities are deleted from the database
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to closing or merging. Including terminated fund performance in the data up to each fund’s termination date would eliminate the survivorship bias and change the outcome in Figure 3.1. Without survivorship bias, the Vanguard 500 Index Fund beat over 85 percent of actively managed funds during the 25 year period. The second
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about twice their reported investment costs.16 One added benefit from Carhart’s exhaustive study on mutual fund performance was the creation of the first survivorship-bias-free mutual fund database. The database was initially funded by Eugene Fama and compiled by Carhart. Unlike other databases at the time, the CRSP Survivor
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expanded over the past decade to correct mutual fund database biases universally. The three major mutual fund database providers, CRSP, Morningstar, and Lipper, are now survivorship bias free. cSource: Barras, Laurent, Scaillet, O., and Wermers, Russ R., False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas (April 20, 2009). Journal
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Active (SPIVA) report. This study tracks one-, three-, and five-year active mutual fund performance across several different asset classes and is derived from a survivorship-bias-free database. The semiannual SPIVA report includes U.S. equity, U.S. real estate, U.S. fixed income, international equity, emerging markets equity, and international
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Index Fund** Large Cap U.S. Stock 50 30% Diversified International Stock 35 51% U.S. Investment Grade Bond 38 34% *This data has a survivorship bias because closed and merged fund performance was not included. Using S&P survivorship data, it’s estimated that U.S. large cap and diversified international
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began in the 1960s and grew in popularity during the 1990s as more mutual fund data became available. This coincided with a period when the survivorship-bias in mutual fund databases was being addressed and corrected.a “Hot Hands in Mutual Funds; Short-Run Persistence of Relative Performance 1974–1988” was published
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fund managers to earn returns,” according to Mark Grinblatt and Sheridan Titman in their 1992 Journal of Finance article.3 “Our sample, largely free of survivorship bias, indicates that relative risk-adjusted performance of mutual funds persists; however, persistence is mostly due to funds that lag the S&P 500,” according to
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many had gone out of existence over that period. The closed and merged fund performance was included until the termination month to make the database survivorship bias free. Each year, mutual funds were ranked in equal numbered groups from 1 to 10 based on annual performance. The first group held the top
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&P Persistence Scorecard.”b The Scorecard tracks the consistency of mutual funds over three- and five-year time periods. The University of Chicago’s CRSP Survivorship-Bias-Free U.S. Mutual Fund Database provides the data for this analysis. The ongoing S&P study separates U.S. equity mutual funds into groups
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comes to passively managed index fund portfolios. Passive investing is the most efficient investment solution for today’s investor. aMutual fund databases were plagued by survivorship bias through the mid-1990s. As funds closed or merged, their entire historic performance was purged from the database. Most of these funds had poor performance
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than the managers who were terminated for poor performance.7 In a more recent study, Jeffrey Busse, Amit Goyal, and Sunil Wahalu used a new, survivorship bias-free database to examine the performance and persistence in performance of 4,617 active domestic equity institutional products managed by 1,448 investment management firms
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portfolio and tactical asset allocation and Strategy, consistency of Strategy indexes Style bias, actively-managed funds and Style indices Styles within categories Subprime mortgage meltdown Survivorship bias: Cowles Commission report and Vanguard 500 Index Fund and Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel Swendon, David F. Swensen, David
The Data Detective: Ten Easy Rules to Make Sense of Statistics
by Tim Harford · 2 Feb 2021 · 428pp · 103,544 words
’ll completely misunderstand where the real vulnerabilities are.5 The rabbit hole goes deeper. Even the story about survivorship bias is an example of survivorship bias; it bears little resemblance to what Abraham Wald actually did, which was to produce a research document full of complex technical analysis. That is largely
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rigorous, whose abstract read: “We tested several hundred undergraduates to see if they could see into the future. They couldn’t.” This, then, is a survivorship bias as strong as press coverage of Kickstarter projects or trying to deduce the vulnerabilities of planes by examining only the ones whose vulnerabilities weren’t
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Journal of Personality and Social Psychology, you might well conclude that people can indeed see into the future. For obvious reasons, this particular flavor of survivorship bias is called “publication bias.” Interesting findings are published; non-findings, or failures to replicate previous findings, face a higher publication hurdle. Bem’s finding was
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too absurd, but not too predictable) that makes them so fascinating. The “interestingness” filter is enormously powerful. * * * — Little harm is done if publication bias (and survivorship bias) merely produces cute distortions in our view of the world, leading people to prepare for a job interview by finding a secluded spot to strike
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consequences for both our money and our health. Money first. Business writing—a field in which I confess to dabbling—is dripping with examples of survivorship bias. In my book Adapt, I had a little chuckle about the Tom Peters and Robert Waterman book In Search of Excellence, a blockbusting business bestseller
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for the fact that any fund still in existence is a survivor—and that introduces a survivorship bias. Burton Malkiel, economist and author of A Random Walk down Wall Street, once tried to estimate how much survivorship bias flattered the performance of the surviving funds. His estimate—an astonishing 1.5 percent per year
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reflect all the experiments that had been conducted.31 This matters. Billions of dollars are misspent and hundreds of thousands of lives lost because of survivorship bias, when we make decisions without seeing the whole story—the investment funds that folded, the Silicon Valley entrepreneurs who never got beyond the “junk in
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–16, 118–23, 125–27 racial bias in criminal justice, 176–79 in sampling, 135–38, 142–45, 147–51 selection bias, 2, 245–46 survivorship bias, 109–10, 112–13, 122–26 systematic bias in algorithms, 166 and value of statistical knowledge, 17 big data and certification of researchers, 182 and
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Second World War, 4, 262 secrecy, 174–75 Seehofer, Horst, 191 Seeing Like a State (Scott), 201, 203 selection bias, 2, 245–46. See also survivorship bias self-awareness, 40 self-harm data, 73–76, 85 self-reported data, 2 senses and perception, 247 sex scandals, 185–86 sexual activity data, 93
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, 169 Super PACs, 275 superforecasters, 252–55, 260 surveillance, 202 surveys, 28, 49, 50, 143, 164–65. See also face-to-face surveys; telephone surveys survivorship bias, 109–10, 112–13, 122–26 Sustainable Development Goals, 142–43 Sutherland, John, 214n Swarthmore College, 138 Sweden, 209 Taber, Charles, 33, 38 Taft, William
Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing
by Vijay Singal · 15 Jun 2004 · 369pp · 128,349 words
subperiods as well as for the whole period unless there is a valid reason for a change in the observed relationship. SURVIVORSHIP BIAS Another source of unreliability of an anomaly is survivorship bias, which exists whenever results are based on existing entities. For example, a simple study of existing mutual funds will find that
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and alive, are included in the sample, then the funds, on average, do not outperform their benchmarks. The sample of existing mutual funds has a survivorship bias and will result in an overestimation of fund performance. Survivorship is important in market timing studies, as market timing newsletters or services use many strategies
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Random Walk timer shows only the successful strategies and not the unsuccessful strategies, giving readers the false impression of market timing prowess where none exists. Survivorship bias is widespread in many spheres of the investment world. People with a good investment record are retained, while others are dumped. It seems as if
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be viewed with caution and skepticism, as spurious mispricings can surface for a variety of reasons, such as errors in defining normal return, data mining, survivorship bias, small sample bias, selection bias, nonsynchronous trading, and misestimation of risk. • Though anomalies should disappear in an efficient market, they may persist because they are
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. It rates the timing newsletters based on the actual return that could have been earned based on the recommendations. However, HFD also suffers from a survivorship bias, though less severe—only newsletters’ portfolios that have continued to do well will be retained by the newsletters and, therefore, only the winning portfolios, by
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as compensation, 137 trading costs, 181 weekend effect, 43, 48, 51–52, 53 Outlook, 174 overconfidence, 286, 292, 293. See also behavioral finance overestimation and survivorship bias, 11 overreaction, 287–88, 290 Ownership Reporting System, 138 Pacific Asia funds, 119, 121, 122, 124, 133n3 Pacific markets, 112. See also Asia Palm, 16
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trading strategies 347 348 Index structural efficiency. See market efficiency structural uncertainty model, 290 substitutability among financial assets, 169, 170–71, 174 supplementary indexes, 172 survivorship bias, 11–12 synergistic gains, 197 T. Rowe Price New Asia Fund (PRASX), 113 Takenaka, Heizo, 22n2 takeovers. See mergers and acquisitions taxes and taxation capital
Portfolio Design: A Modern Approach to Asset Allocation
by R. Marston · 29 Mar 2011 · 363pp · 28,546 words
then will attempt to measure how large they are. There are two major biases affecting hedge fund returns. One bias plagues all asset return data—survivorship bias—although usually it’s relatively easy to correct for it if there is a full universe of data available. The other bias—backfill bias—is
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: a/b c09 P2: c/d QC: e/f JWBT412-Marston Hedge Funds T1: g December 20, 2010 17:1 Printer: Courier Westford 179 2. Survivorship bias. This bias arises when a database keeps track of only the live funds. The reason why you would like to keep track of all funds
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August 1998 did not report returns of –100 percent in that month. Instead, the returns ended in July 1998.17 Any attempts to adjust for survivorship bias will miss the liquidation bias when the fund closes down. Quantitative estimates of backfill bias range widely from one study to another. That’s because
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the methodology for determining the bias varies as well. Malkiel and Saha (2005) estimate backfill and survivorship bias using the TASS database from 1994 to 2003. The TASS database distinguishes between returns that have been backfilled into the TASS database from returns subsequently
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% 7.3% 7.3% Backfill Bias All Funds Exclude 1st 14 Months Bias 12.0% 10.5% 1.5% Survivorship Bias Live Funds∗ Live and Defunct∗ Bias 13.7% 9.3% 4.4% Survivorship Bias Live Funds Live and Defunct Bias 14.4% 12.0% 2.4% ∗ The returns for the live and defunct
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funds exclude backfilled returns. The estimates of survivorship bias are for 1996 to 2003. examining the dropout rates for hedge funds in three databases, TASS, HFR, and CISDM. Using a data set of hedge
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that are not truly backfilled, but merely previously missing from that database, why are those returns so much lower than the non back-filled returns? Survivorship bias is potentially quite large given the high rates of exit from the industry. Consider first how many funds survive over time. Malkiel and Saha (2005
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of the data set, December 2003, from the defunct firms that dropped out of the data set prior to that date. To determine the resulting survivorship bias, it’s necessary to compare the returns of the live firms with the live and defunct firms together. In Table 9.6, Malkiel and Saha
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estimate survivorship bias to be 4.4 percent. Fung and Hsieh (2006) measure survivorship bias using their three databases from 1994 to 2004. Unlike Malkeil and Saha, Fung and Hsieh include all returns in their
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estimate of this bias, including backfilled returns. Table 9.6 reports a survivorship bias of 2.4 percent using P1: a/b c09 P2: c/d QC: e/f JWBT412-Marston T1: g December 20, 2010 Hedge Funds 17
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lower set of estimates from Fung and Hsieh (2006) are used, 1.5 percent for backfill bias and 1.8 to 2.4 percent for survivorship bias, the effects on hedge fund returns are enormous. If you reduce the returns in Table 9.4 by about 3.5 percent to 4 percent
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). The authors eliminate funds that give only gross returns or quarterly, not monthly, returns. 9. As discussed below, those that do, then don’t cause survivorship bias. Those that don’t, then backfill cause backfill bias. 10. These databases are provided by Lipper TASS, Hedge Fund Research (HFR), and the Center for
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a net basis. This results in a 7.5 percent gap between the CA and SHE indexes.6 Metrick argues that the CA index has survivorship bias due to the fact that many VC firms attract institutional money only after they have had successful VC funds. He suggests treating the CA index
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spot price and the current futures price backwardation as opposed to the normal backwardation that Keynes described. 3. Gorton and Rouwenhorst (2006) argue that the survivorship bias from using backdated commodity futures returns is not clearly in one direction or another. Stocks fail because of bankruptcy while commodity futures may be delisted
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versus, 24–25 capital gains, 33 correlation with venture capital, 202 in portfolios, 290–291, 309–310 returns, 30–32 strategic asset allocation, 3–4 survivorship bias, 179–180 Svenson, David, 14, 158, 204, 213, 258–260, 269–276 INDEX symmetrical investment fees, 167 systematic risk, 47 T TASS, 173 term structure
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