Commodity Trading Advisors: Risk, Performance Analysis, and Selection Chapter 4 ppsx

CHAPTER 4 CTA Performance, Survivorship Bias, and Dissolution FrequenciesDaniel Capocci Using a database containing 1,892 funds including 1,350 dissolved funds, we investigate CTA perfo

CHAPTER 4 CTA Performance, Survivorship Bias, and Dissolution Frequencies

Daniel Capocci

Using a database containing 1,892 funds (including 1,350 dissolved funds),

we investigate CTA performance and performance persistence to mine if some CTAs consistently and significantly outperform their peers overvarious time periods To test the persistence hypothesis, we use a methodol-ogy based on Carhart’s (1997) decile classification We examine performanceacross deciles and across CTA strategies to determine if some deciles aremore exposed to certain strategies over time We also analyze survivorshipbias and its evolution over time We conclude the study by analyzing the dis-solution frequencies across deciles and their evolution over time

deter-INTRODUCTION AND LITERATURE REVIEW

Unlike hedge funds, which appeared in the first academic journal in 1997,commodity trading advisors (CTAs) have been studied for a longer time.Many studies were published in the late 1980s and in the early 1990s (see, e.g., Elton, Gruber, and Rentzler 1987, 1989, 1990; Edwards and Ma1988) More recently, Billingsley and Chance (1996) and Edwards and Park(1996) showed that CTA funds can add diversification to stocks and bonds

in a mean-variance framework According to Schneeweis, Savanayana, andMcCarthy (1991) and Schneeweis (1996), the benefits of CTAs are similar

to those of hedge funds, in that they improve and can offer a superior adjusted return trade-off to stock and bond indices while acting as diversi-fiers in investment portfolios

risk-Fung and Hsieh (1997b) showed that a constructed CTA style factorpersistently has a positive return when the Standard & Poor’s (S&P) has a

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negative return According to Schneeweis, Spurgin, and Georgiev (2001),CTAs are known to short stock markets regularly Fung and Hsieh (2001a)analyzed CTAs and concluded that their impact on portfolios is similar to

examined whether CTA percent changes in net asset values (NAVs) followrandom walks They found all classifications (except the diversifiedsubindex) to behave as random walks The effectiveness of CTAs in enhanc-ing risk-return characteristics of portfolios could be compromised whenpure random walk behavior is identified Kat (2002) found that allocating

to managed futures allows investors to achieve a very substantial degree ofoverall risk reduction at limited costs Managed futures appear to be moreeffective diversifiers than hedge funds

Regarding performance, Edwards and Caglayan (2001) concluded thatduring bear markets, CTAs provide greater downside protection than hedgefunds and have higher returns along with an inverse correlation with stocksreturns in bear markets Schneeweis and Georgiev (2002) concluded thatcareful inclusion of CTA managers into investment portfolios can enhancetheir return characteristics, especially during severe bear markets Schneeweis,Spurgin, and McCarthy (1996) observed that performance persistence wasvirtually inexistent between 1987 and 1995 There is little information onthe long-term diligence of these funds (Edwards and Ma 1998; Irwin, Kruke-meyer, and Zulauf 1992; Kazemi 1996) Schwager (1996) reviews the litera-ture on CTA performance persistence and conducts his own analysis Hefound little evidence that the top-performing funds can be predicted.According to Worthington (2001), between 1990 and 1998 the correlation

of managed futures to the S&P 500 during its best 30 months was 0.33 and

−0.25 during its worst 30 months According to Georgiev (2001), one of thedrawbacks of CTAs is that during bull markets, their performance is gener-ally inferior to those of hedge funds

Brorsen and Townsend (2002) show that a minimal amount of formance persistence is found in CTAs, and there could exist some advan-tages in selecting CTAs based on past performance when a long time series

per-of data is available and accurate methods are used

This chapter aims to detect performance persistence of CTAs We want

to determine if some CTAs consistently outperform their peers over time In

stock price reached during the life of the option A lookback put is a normal put option, but the strike depends on the maximum stock price reached during the life

of the option

the next section, we describe the database, reporting the descriptive tics of the funds and analyzing the correlation between the various strate-gies reported The following section focuses on survivorship bias Weanalyze the presence of this bias over the whole period studied but also overdifferent time periods, including a bull and a bear market period Further,

statis-we report the methodology used to analyze CTA performance and formance persistence before reporting the results of the performance analy-sis in the next section The next section reports the results of the persistenceanalysis and analyzes the exposure of the deciles constructed on previousyear’s performance to the individual strategies Then we report the completeanalysis of monthly and yearly dissolution frequencies

per-DATABASE

In this section, we present our database and analyze the descriptive statistics

of the data before reporting the correlation between the various strategies

Descriptive Statistics

There are several CTA data providers The providers most commonly used

in academic studies are Managed Account Repots, TASS Management, andthe Barclay Trading Group, Ltd The latter represents one of the most (ifnot the most) comprehensive managed future databases

For our analysis we use the Barclay Trading Group database, whichcontains 1,892 individual funds (including 1,350 dissolved funds) over theJanuary 1985 to December 2002 period The Barclay Trading Group clas-sifies these funds in 7 categories that are subdivided in 17 strategies plus theno-strategy category We grouped some strategies because they contain toofew funds to give interesting results As shown in Table 4.1, we obtained atotal of 11 strategies Note that we combined only those strategies that are

in the same category

To perform our performance analysis, we will use the whole databaseand the classifications reported in Table 4.1 This will allow us to determinewhether results differ across strategies and whether funds in particularstrategies significantly outperform others

Previous studies often focused on fewer funds For example, Schneeweis,Spurgin, and McCarthy (1996) studied 56 CTA funds from 1985 to 1991.Irwin, Zulauf, and Ward (1994) used a database containing 363 CTAsfrom 1979 to 1989 Other studies were larger For example, Edwards andPark (1996) found 596 CTAs from 1983 to 1992 by supplementing theMAR/LaPorte CTA database with private sources Diz (1996) and Fung andHsieh (1997b) had 925 and 901 managed future programs from 1975 to

CTA Performance, Survivorship Bias, and Dissolution Frequencies 51

1995, and from 1986 to 1996 respectively They were both based on theBarclay Trading Group database.

Funds in the Barclay Trading Group database can be classified intomore than one strategy This can lead to a bias when we compare differentstrategies since they can contain the same funds In order to deal with this

Before entering the body of the study, we analyze the composition ofthe database Table 4.2 reports the descriptive statistics of the database.Funds are classified according to strategy The last line reports the statisticsfor the whole database

TABLE 4.1 Grouping of Barclay Trading Group Strategies

Technical Diversified Technical Diversified Technical Financial/Metals Technical Financial/Metals Technical Currency Technical Currency Other Technical Technical Interest Rate

Technical Energy Technical Agricultural Fundamental Fundamental Diversified

Fundamental Interest Rate Fundamental

Financial/Metals Fundamental Energy Fundamental Currency Fundamental

Agricultural Discretionary Discretionary Systematic Systematic Stock Index Stock Index

Option Strategies Option Strategies

No Category No Category Note: The left-hand side of the table reports the strategy classifica- tion used throughout the study; the right-hand side contains the original classification of the Barclay Trading Group.

2 Any fund that is reported in two strategies is classified into the one that contains the most funds.

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Table 4.2 indicates that the systematic strategy is the most representedstrategy (with 897 funds) followed by total technical funds (416 funds) anddiscretionary funds (299 funds) Other technical funds, option strategyfunds, and fundamental funds count only 8, 9, and 19 funds respectively.The database contains 611 dissolved funds as a whole, 350 of which followthe systematic strategy Note that all the other technical funds and optionstrategy funds are dissolved over the period studied The median returnsindicate the same patterns.

Regarding the statistics, the highest mean monthly return is achieved

by the other technical funds (with 3.18 percent per month) followed by the option strategy funds and discretionary funds (with 2.62 percent and2.03 percent per month) Many strategies offer a monthly return of between1.6 percent and 1.9 percent per month The lowest returns are those of thearbitrage funds (with 1.25 percent) followed by the technical currencyfunds (with a monthly return of 1.58 percent) All the monthly returns aresignificantly different from zero over the period studied

The fundamental funds and the other technical funds are the morevolatile funds with a standard deviation of 7.60 and 7.25 percent Becausethere are few funds applying these strategies, there is no diversificationeffect, which can explain why the returns of these strategies are so volatile.The strategies that offer the most stable returns are the discretionary funds(with a standard deviation of 3.01 percent) and the arbitrage funds (with astandard deviation of 3.19 percent)

As one could expect, the strategies that are the most volatile also havethe lowest minimum return and the highest maximum return The monthly

whereas the maximum of this strategy is 57.4 percent The returns are usually positively skewed (the only exception is the arbitrage strategy) and their distributions tend to have fat tails, as evidenced by the large valuesfor kurtosis

When risk and returns are considered together through the Sharpe

followed by other technical funds (with 0.38) Fundamental funds offer aSharpe ratio of only 0.19

Correlation Analysis

Table 4.3 reports the correlation coefficients between the various strategiesfor the January 1985 to December 2002 period It indicates that the CTA

use a risk-free rate of 5 percent for this calculation.

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global index is almost exactly correlated with the systematic funds This can

be partly explained by the fact that this strategy contains the greatest ber of funds Forty-four coefficients out of sixty-six (66 percent of the co-efficients) are under 0.5, indicating that most of the strategies are notcorrelated The lowest coefficient is the one between arbitrage and system-

represent-ing 14 percent of the coefficients

SURVIVORSHIP BIAS

Performance figures are subject to various biases One of the most tant is the survivorship bias that appears when only surviving funds aretaken into account in a performance analysis study The common practiceamong suppliers of CTA databases is to provide data on investable funds

data suffer from survivorship bias because dissolved funds tend to haveworse performance than surviving funds

Survivorship bias has already been studied Fung and Hsieh (1997b)precisely analyzed this bias and estimated it at 3.4 percent per year Theyalso concluded that survivorship bias had little impact on the investmentstyles of CTA funds Returns of both surviving and dissolved CTA fundshave low correlation to the standard asset classes

Survivorship Bias over Various Time Periods

Here we analyze the presence of survivorship bias in CTAs returns over ious long-term time periods We first study the whole period covered beforedividing it into subperiods

var-Table 4.4 reports the survivorship bias obtained from our database.Survivorship bias is calculated as the performance difference between sur-viving funds and all funds All returns are monthly and net of all fees Thefirst part of the table indicates a survivorship bias of 5.4 percent per yearfor the entire period This figure is higher than the one obtained in previousstudies Table 4.4 shows the bias was higher during the 1990 to 1994period (7.3 percent) and during the 1995 to 1999 period (6.2 percent) butlower during the 2000 to 2003 period (4.4 percent)

CTA Performance, Survivorship Bias, and Dissolution Frequencies 57

Survivorship Bias over Time

Figure 4.1 reports the evolution of the survivorship bias calculated on athree-year rolling period starting January 1985 to December 1987 and end-ing January 2000 to December 2002 It allows us to analyze more preciselyhow the survivorship evolves over time

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 on the vertical axis are monthly percentages.

TABLE 4.4 Survivorship Bias Analysis over Different Periods Bias 1985–2003 0.5 per Month

5.4 per Year Bias 1985–1989 0.5 per Month

5.5 per Year Bias 1990–1994 0.6 per Month

7.3 per Year Bias 1995–1999 0.5 per Month

6.2 per Year Bias 2000–2003 0.4 per Month

4.4 per Year Our database contains 1,899 CTAs (611 survived funds and 1,288 dissolved funds as of December 2002).

5 We take a month as a positive month if the whole database has a positive formance We consider a month as negative if the whole database does not reach positive returns.

per-The figure indicates that the monthly bias ending January 1985increases from around 0.7 percent at the beginning of the year to 0.85 per-cent after summer before reaching the bottom of 0.9 percent at the begin-ning of 1989 Afterward, it increases until January 1993 (0.9 percent) andthen decreases to a mean around 0.55 percent for the periods ending betweenJanuary 1994 and January 2000 Because the three-year periods end Janu-ary 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, the sharp decrease in the January 1989 results (and the slowincrease that follows) can be explained by the fact that the surviving fundsunderperformed the whole database in 1988 and 1989 The first underper-formance was in December 1988 (1.87 percent for the surviving fundsagainst 2.94 percent for the whole database) Moreover, this was the firstmajor underperformance, which has been followed by others during the

−2.54 percent against −0.91 percent in April) On the other hand, the sharpincrease in survivorship bias over the period ending November and Decem-ber 1992 can be explained mainly by high overperformance in June, July,and August 1992 with an average of 3 percent monthly outperformance Tosummarize, this figure identifies epochs during which surviving funds out-performed the whole database, and during which the difference betweensurviving funds and dissolved funds was less important

We also analyze the survivorship bias calculated over the positive

indi-cates that the mean survivorship bias is the same over the three periodsstudied at 0.48 percent The standard deviation and the median of the survivorship are also almost equal The only significant difference is in the minimum three-year rolling period, which is much higher for the nega-tive months at 0.13 percent versus 0.06 percent for the whole period and the positive months The maximum is also almost equal between 0.87percent and 0.90 percent

METHODOLOGY

The aim of this study is to determine if some CTAs consistently and sistently outperform their peers To achieve this objective, we construct aCTA Global Index that contains all the funds present in our database and

per-one index per CTA strategy To test if some funds significantly outperformthe indices, we use the following regression.

We run this analysis for each fund compared to the whole CTA base index but also for each fund compared to its strategy index Once weobtain results, we want to determine if momentum is present in CTAreturns Active CTA selection strategies could increase the expected return

data-on a portfolio if CTA performance is really predictable We define thehypothesis that a CTA with an above-average return in this period also willhave an above-average return in the next period as the hypothesis of per-sistence in performance Sirri and Tufano (1998) and Zheng (1999) stressedthe importance of persistence analysis in mutual funds They documentlarge inflows of money into last year’s best performers and withdrawalsfrom last year’s losers Capocci and Hübner (2004) have stressed this forhedge funds They find that newly invested money in these best-performingmutual funds is a predictor of future fund performance

We apply the methodology of Carhart (1997) to our simple model Allfunds are ranked based on their previous year’s return Every January weplace all funds into 10 equally weighted portfolios, ranked from highest tolowest past returns Portfolios 1 (High) and 10 (Low) are then furthersubdivided on the same measure The portfolios are held until the followingJanuary and then rebalanced This yields a time series of monthly returns oneach decile portfolio from January 1985 to December 2002 Funds that dis-appear during the course of the year are included in the equal-weighted aver-age until they disappear, then portfolio weights are readjusted appropriately

CTA Performance, Survivorship Bias, and Dissolution Frequencies 59

TABLE 4.5 Descriptive Statistics of the 3-Year Rolling-Period Survivorship Bias

Whole period 0.48 0.18 0.51 0.06 0.90 Positive months 0.48 0.18 0.51 0.06 0.90 Negative months 0.48 0.18 0.52 0.13 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).

Finally, in the last part of the study we want to determine empirically ifsome strategies are consistently better than others To achieve this objective

we use the next regression

(4.2)

currency, technically diversified, technically financial/metals,technically others, stock index, options, systematic, arbitrage,

discretionary, fundamental, no category) at period t

We regress each decile against the CTA Global Index and each strategyindex Doing so, we determine if some deciles are exposed to some strate-gies, which indicates that that strategy is particularly present in the corre-sponding decile

PERFORMANCE ANALYSIS

Here we apply the model just discussed to our database to determine ifsome strategies significantly outperform the CTA Global Index over differ-ent time periods In the next section we investigate whether momentumexists in CTA performance

Table 4.6 indicates some interesting results First, we see that results aredifferent across strategies, indicating that the classification in substrategiesseems to be relevant Second, the first column of the table reports the alpha

of the different strategies once the performance of the CTA database sidered as a whole is taken into account through the CTA Global Index.This is the performance not explained by the global CTA index Seven out

con-of the 11 strategies are significantly positive at the 5 or 1 percent cance level (technically financial/metals, technically currency, technicallyother, discretionary, stock index, arbitrage, and option strategies); two arenot significantly different from zero (fundamental and no category); andtwo are significantly negative (technically diversified and systematic) Theseresults indicate that all but two strategies produce returns significantly dif-ferent from zero, which means that the individual strategies produce returns

6 The CTA Global Index is composed of all the individual funds classified in the ious strategies It is the same funds classified differently.

var-The positive alphas range from a monthly percentage difference of 0.65percent for technically financial/metals to 2.03 percent for option strategies;

−0.58 percent for systematic funds

Third, most betas are significantly positive at the 1 percent significancelevel For four strategies (fundamental, stock index, arbitrage, and option)the beta is either significant at the 10 percent level or not significant Thesestrategies all contain 52 funds or less, which means that they represent only

a small part of the index This fact partly explains their limited exposure tothe CTA Global Index

from 0.00 for stock funds to 0.95 for systematic funds As we could have

particularly low when the beta is not significant

Table 4.7 reports the same results over different subperiods We dividethe analysis in three six-year periods (January 1985 to December 1990, Jan-

CTA Performance, Survivorship Bias, and Dissolution Frequencies 61

TABLE 4.6 Relative Performance Analysis of Strategy Indices

Technically diversified −0.28 *** 1.14 *** 0.92 Technically financial and metals 0.65 ** 0.64 *** 0.38 Technically currency 0.92 *** 0.38 *** 0.18 Technically other 2.56 *** 0.33 ** 0.04

t-stat are heteroskedasticity consistent.

***Significant at the 1 percent level.

**Significant at the 5 percent level.

*Significant at the 10 percent level.

Numbers in the table are monthly percentages.

***Significant at the 1 percent level **Significant at the 5 percent level

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