Prior performance and risk taking ammann


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Prior performance and risk taking ammann

  1. 1. The Journal of Behavioral Finance Copyright C 2007 by2007, Vol. 8, No. 1, 20–34 The Institute of Behavioral Finance Prior Performance and Risk-Taking of Mutual Fund Managers: A Dynamic Bayesian Network Approach Manuel Ammann and Michael Verhofen We analyze the behavior of mutual fund managers with a special focus on the impact of prior performance. In contrast to previous studies, we do not focus solely on volatility as a risk measure, but also consider alternative definitions of risk and style. Using a dynamic Bayesian network, we are able to capture non-linear effects and to assign exact probabilities to the mutual fund managers’ adjustment of behavior. In contrast to theoretical predictions and some existing studies, we find that prior performance has a positive impact on the choice of risk level, i.e., successful fund managers take on more risk in the following calendar year. In particular, they increase volatility, beta, and tracking error, and assign a higher proportion of their portfolio to value stocks, small firms, and momentum stocks. Overall, poor-performing fund managers switch to passive strategies. Introduction option-like function of compensation schemes. Fund managers may unnecessarily shift a fund’s risk in re- The behavior of mutual fund managers has been sponse to its relative performance. This behavior issubject to considerable academic research. As rational linked to compensation and investor reactions.agents, they are supposed to adjust their behavior in Carpenter [2000] solves the dynamic investmentaccordance with the incentives they face. We divide problem of a risk-averse manager compensated using athese incentives into two categories: the structure of call option on the assets he controls, i.e., a convex com-their compensation schemes, and investor behavior. pensation scheme. She shows that under the manager’s Most compensation schemes are constructed like optimal policy, the option is likely to end up deep ina call option, i.e., fund managers have a higher up- or deep out of the money, because managers generallyside than downside potential. In a multiperiod con- take on more risk than the investors would choose.text, a positive relationship between past performance Berk and Green [2004] propose a model that in-and new fund flows has been observed. Thus a high- corporates two important points: Performance is notperforming fund manager is rewarded with new cap- persistent, and fund flows respond rationally to pastital, but a poor-performing manager does not suffer performance. In particular, they assume investors be-the same amount of cash outflows. If compensation have as Bayesians would, i.e., they update their beliefsis linked to fund size, this provides an incentive for about a fund manager’s skill based on observed returnsmanagers to increase a portfolio’s riskiness to a subop- and prior beliefs. They show that a rational model fortimal point from an investor’s perspective. In short, in active portfolio management can explain many empiri-so-called mutual fund tournaments, portfolio managers cal observations without relying on investor irrational-compete for better performance, greater fund inflows, ity and asymmetric information.and higher compensation. Similar models relying on Bayesian updating have Many authors have focused on the theoretical basis also been proposed (see Schmidt [2003] and Dangl,of agency conflicts in the mutual fund industry, i.e., on Wu, and Zechner [2004]). Lynch and Musto [2003]asymmetric information, hidden activity between mu- focus on how fund managers change their strategy overtual fund managers and their investors, and on the call time. In their model, strategy changes occur only after periods of poor performance. Manuel Ammann is a professor of finance at the University of From an empirical point of view, different authorsSt. Gallen and director of the Swiss Institute of Banking and finance. have analyzed the actual behavior of mutual fund man- Michael Verhofen is a research assistant at the Swiss Institute agers. Deli [2002] investigates marginal compensationof Banking and Finance at the University of St. Gallen. rates in mutual fund advisory contracts. He finds that Requests for reprints should be sent to: Michael Verhofen,University of St. Gallen, Swiss Institute of Banking and Finance, marginal compensation depends positively on turnoverRosenbergstrasse 52, CH-9000 St. Gallen, Switzerland. Email: and fund type (e.g., equity, closed-end), and is tively related to fund size and size of the fund family. 20
  2. 2. PRIOR PERFORMANCE AND RISK-TAKINGTherefore, incentives to take risk may differ across of their mono-causal dependency structure (Jordan,fund managers. Ghahramani, and Saul [1997]). Chevalier and Ellison [1997] estimate the shape of Bayesian networks work as follows. Suppose therethe relationship between performance and new fund are three variables, the tracking error in period T , theflows because it creates incentives for fund managers return in period T , and the tracking error in periodto increase or decrease fund riskiness. They find that T + 1. Suppose next that all variables are conditionallyfunds tend to change their volatility depending on their dependent, i.e., the tracking error in period T affectsrelative performance by the end of September. the return in period T and the tracking error in period Similarly, Brown, Harlow, and Starks [1996] fo- T + 1, and the return in period T affects the trackingcus on mid-year effects. In particular, they test the error in T + 1. In classical econometrics, this prob-hypothesis that mutual managers showing an under- lem is referred to as multicollinearity, and can lead toperformance by mid-year change the fund’s risk dif- identification problems. Bayesian networks can dealferently than those showing an outperformance at the with such complex settings and help overcome iden-same time. Their empirical analysis shows that mid- tification problems. For a discussion of a wide rangeyear losers tend to increase fund volatility to a greater of different settings of Bayesian networks, see Pearlextent than their successful counterparts. [2000]. Busse [2001], however, has suggested that some Besides being a modern tool for identifying theprior findings may be spurious. Using daily data as well impact and magnitude of different causal sources,as Brown, Harlow, and Starks’s [1996] methodology, Bayesian networks have many advantages over stan-he finds no mid-year effect. dard econometric methods. For example, they allow the In sum, existing empirical analyses have failed to computation of exact conditional probabilities to assessdeliver clear evidence about the behavior of mutual a factor’s magnitude. Thus we can analyze whetherfund managers, and there are doubts about the robust- probabilities change from 50:50 to 60:40 or to 90:10.ness of many findings. Moreover, Bayesian networks capture non-linear and In this paper, we contribute to the literature in a asymmetric patterns.number of ways. In contrast to existing studies, we do Using Bayesian networks on a set of U.S. equitynot focus solely on volatility as a risk measure. We funds over about twenty years of data, we find thatconsider other measures as well, such as beta, track- prior performance has a positive impact on the choiceing error, and style measures like the high-minus-low of risk level (in other words, successful fund man-(HML) factor, the small-minus-big (SMB) factor, and agers take on more risk in the following time pe-the momentum (UMD) factor. Furthermore, in contrast riod). In particular, they increase volatility, beta, andto previous studies, we use a robust, non-parametric ap- tracking error, and assign a higher proportion of theirproach. Because we do not impose any distributional portfolio to value stocks, small firms, and momentumassumptions, we are able to capture a wide range of stocks.non-linear and asymmetric patterns. Moreover, rather This article is structured as follows: In the sec-than using a subgroup of mutual funds, we use a com- ond section, we outline the econometric approach. Theplete set of all U.S. equity funds to obtain a more third section presents our empirical results. The finallengthy time frame of data. section is the conclusion. We compute conditional transition matrices, andcompare whether they differ for successful and un-successful mutual funds. For the empirical analysis, Modelwe use a Bayesian network, a model for represent-ing conditional dependencies between a set of random Introduction to Bayesian Networksvariables. Until now, research on Bayesian networks(BN) has mainly concentrated in statistics and com- A Bayesian network (BN) is a graphical model forputer science, especially artificial intelligence (Korb representing conditional dependencies between a setand Nicholson [2004], Neapolitan [2004]), pattern of random variables. This includes learning about con-recognition (Duda, Hart, and Stork [2001]), and ex- ditional distributions and updating beliefs about prob-pert systems (Jensen [2001], Cowell et al. [2003]). The ability distributions for a target node given some ob-term “Bayesian network” is not yet widespread, but servation for at least one variable.special cases of Bayesian networks are already widely Figure 1 shows a dynamic Bayesian network. Theused in economics and finance. Many state space mod- structure is similar to those proposed by Pearl [2000]els such as the Kalman filter and hidden Markov mod- for causal discovery. The circles denote the nodes,els are Bayesian networks with a simple dependency i.e., the variables, and the arcs denote the conditionalstructure. Moreover, many classical econometric ap- dependencies between two nodes, which can be ofproaches such as discrete choice models and regres- any type. The most common conditional probabilitysions are so-called naive Bayesian networks because distributions (CPD) are Gaussian distributions and 21
  3. 3. AMMANN & VERHOFEN FIGURE 1 Illustration of a Dynamic Bayesian NetworkThe three main issues for Bayesian networks are 1) representation, i.e., what is a Bayesian network, 2) probabilistic inference, i.e., the updatingof probability distributions for a query node given evidence for a particular node, and 3) the estimation of parameters for conditional probabilitydistributions for a Bayesian network based on sample data.Representation: A Bayesian network consists of a set of variables, usually denoted as nodes (illustrated as circles). The arcs represent conditionaldependencies in the network between nodes.Inference: Probabilistic inference denotes the updating of probability distributions for a query node given some evidence (posterior distribution).The graph illustrates the probability updating for the query node TRT , i.e., the tracking error in period T + 1, conditional on the evidence thatRT , return in period T , was high.Learning: Maximum likelihood methods are appropriate to learn the parameters for conditional probability distributions in a Bayesian network.multinomial (or tabular) distributions. Figure 1 shows error leads to a 90% chance of a low return and a 10%a binomial distribution. chance of a high return. We use a dynamic Bayesian network to analyze the The tracking error in period T + 1 is the result ofrevision of behavior in the mutual fund industry. We two variables, the tracking error and the return in periodanalyze how past performance affects a set of vari- T . These conditional dependencies have a straightfor-ables describing the risk-taking behavior of mutual ward interpretation. On the one hand, there may befund managers. For example, suppose a fund manager some degree of persistency in the behavior, i.e., a fundcan to some degree choose the tracking error of his manager who has had a high tracking error in periodportfolio, i.e., how close he mimics the relevant index T will tend to maintain a high tracking error. On theor whether he chooses a more active investing style. By other hand, there may be some degree of learning orassumption, there is a fifty-fifty chance he will choose revision of behavior.a high or low tracking error. Suppose next that the Figure 1 shows how the dynamic Bayesian networkchoice of magnitude of the tracking error affects the can be used to update beliefs about probability distri-subsequent return. Thus a low tracking error leads to butions. Suppose the investor knows a fund had su-a 50% chance of a high return and a 50% chance of a perior performance in period T . The question is howlow return. In contrast, we assume that a high tracking this knowledge affects the belief about the distribution22
  4. 4. PRIOR PERFORMANCE AND RISK-TAKINGof the tracking error in period T + 1. As shown in previous factorization, we show that:Figure 1, the straightforward application of Bayes’stheorem leads to a probability of 91.50% for a high P (A, B, C, D) P (A, D|B, C) =tracking error and 8.5% for a low tracking error. P (B, C) Bayesian networks are also frequently referred P (A) · P (B) · P (C|A) · P (D|B, C)to as probabilistic networks (Cowell et al. [2003]), = . P (A) · P (B) · P (C|A) · P (D|B, C)dAdDBayesian artificial intelligence (Korb and Nicholson[2004]), and Bayesian belief networks (Duda, Hart,and Stork [2001]). As noted by Borgelt and Kruse Basically, there are three elementary theorems of prob-[2002], Bayesian networks rely on the achievements ability, the building blocks for BN: Bayes’s theorem,of many other concepts, especially classification and the chain rule, and conditional dependency.regression trees, naive Bayes classifiers, artificial neu-ral networks, and graph theory. Learning As mentioned earlier, other econometric ap-proaches, such as the Kalman filter, hidden Markov In the previous subsection, we assumed that the pa-models, and state space models, can be regarded rameters or conditional probability distributions wereas special cases of Bayesian networks and particu- known. However, in most cases, it is necessary to learnlarly of dynamic Bayesian networks (DBN) (Jordan, about the parameters of a BN based on a data set. ForGhahramani, and Saul [1997]). Even regressions and parameter learning, maximum likelihood (ML) can bediscrete choice models can be incorporated into a used. As noted by Ghahramani [2001], the likelihoodBayesian network structure in what are called naive decouples into local terms involving each node andBayes nets. its parents. This simplifies the maximum likelihood BNs are very powerful tools for dealing with un- estimation by reducing it to a number of local maxi-certainty, incomplete information, and complex proba- mization problems.bilistic structures. They enable the extraction of proba- Suppose a data set consists of M cases for each n (h)bilistic structures from data, as well as decision making node. Let d (h) = (d1 , . . . , dn ) denote the vector of (h)in these structures. BNs are thus well-suited for finan- observations for a single case for all nodes in the net-cial applications. work. Therefore, the training data set d is given by This section addresses the three main issues asso- d = {d (1) , d (2) , . . . , d (M) }. Neapolitan [2004] showsciated with BNs: 1) the question of representation, that the likelihood function is given byor what is a Bayesian network? 2) the question oflearning, or how can the parameters of a BN be es- n Mtimated? and 3) the question of inference, or how can L(d | θ ) = P (di(h) | pai(h) , θi )BNs be used to answer probabilistic questions? For ex- i=1 h=1tended coverage of this topic and of decision makingwithin BNs, we refer to Jensen [2001], Cowell et al. where pai(h) contains the values of the parents of node[2003], Korb and Nicholson [2004], and Neapolitan Xi in the hth case, and θ is the parameter set.[2004]. For this paper, we use a multinomial distribution. Thus the maximization problem simplifies to a closed- form solution if the data are complete. The likelihood function is given byRepresentation   n qi rij sijk As defined by Jensen [2001], a Bayesian network L(d) = E Fijk  ,consists of a set of variables with directed arcs be- i=1 j =1 k=1tween them. These variables form a directed acyclicgraph (DAG), and, for each arc that connects two vari- where qi denotes the parents of node Xi , and ri is theables, a potential table (i.e., a conditional distribution) number of different classes of the multinomial distribu-is defined. tion. Fijk denotes the distribution of node i conditional Suppose there are four random variables, A, B, C, on the parent node j where the value of node xi is equaland D. Applying the chain rule, the joint probability to k. The exponent sijk denotes the number of cases incan be written as a product of conditional probabil- which xi is equal to k. See Neapolitan [2004] for theities P (A, B, C, D) = P (D|A, B, C) · P (C|A, B) · proof of these results.P (B|A) · P (A). Suppose next that A and D are Suppose next that the conditional distributionsconditionally independent, i.e., P (A, D|B, C) = Fijk have a Dirichlet distribution, i.e., a gen-P (A|B, C) · P (D|B, C), and B and C are condi- eralized beta distribution with (prior) parameterstionally independent. Using Bayes’s theorem and the aij1 , aij2 , . . . , aijri , Nij = k aijk , and Mij = k sijk 23
  5. 5. AMMANN & VERHOFENThe likelihood is given by 1) The current strength of the predictive support π contributed by each incoming link Ui → X, i.e., n qi (Nij ) ri (aijk + sijk ) πX (Ui ) = P (Ui | EUi X ), where EUi X is all evi- L(d) = dence connected to Ui except via X. i=1 j =1 (Nij + Mij ) k=1 (aijk ) 2) The current strength of the diagnostic support λ con- tributed by each outgoing link X → Yj : λYj (X) =where (.) denotes the gamma function. The use of P (EYj X | X), where EYj X is all evidence con-Dirichlet distributions as conditional distributions has nected to Yj through its parents except via X.many advantages and is not restrictive. The Dirichlet 3) The fixed CPD P (X | Ui , . . . , Un ), i.e., the condi-distribution is the natural conjugate prior for the multi- tional distribution of node X is only dependent onnomial distribution. In other words, an application of its parents.Bayes’s theorem, with the Dirichlet distribution as theprior distribution and the multinomial distribution as Pearl’s [1982] message-passing algorithm consists ofthe likelihood, leads to a closed-form solution for the two steps. In the first, belief updating, messages ar-posterior distribution with the same functional form as rive from the parents or the children of an activatedthe prior, i.e., a Dirichlet distribution. node X and lead to changes in belief parameters. In Therefore, the Dirichlet distribution is useful for the second, bottom-up and top-down propagation, theBayesian sequential analysis and for Bayesian updat- activated node computes new messages for the par-ing. It has upper and lower bounds and can be used ents λ and the children λ to send it in the appropriateto model probabilities that cannot become greater than direction.1 or lower than 0. We can incorporate prior informa- In the first step, the posterior distribution of eachtion about conditional dependencies or assign almost activated node X, proportional to the messages fromuninformative priors by setting all aijk to 1. the parents πX (Ui ) and the messages from its children λYj (X), is determined as follows:Inference Bel(xi ) = αλ(xi )π (xi ) Suppose the structure of a BN and all conditionalprobability distributions (CPD) are known, and a re- where π (xi ) = P (xi | u1 , . . . , un ) πX (ui ) u1 ,..., un isearcher has evidence about at least one node for a andnew case. The goal of probabilistic inference (also referred to as belief updating, belief propagation, or  1 if evidence is X = ximarginalization) is to update the marginal probabili- λ(xi ) = 0 if evidence is for another xjties in the network to incorporate this new evidence  j λγj (xi ) otherwise(Ghahramani [2001]). Formally, the task of inferenceis to find the posterior distribution P (X = x | E = e), and where α is a normalizing constant, rendering xi Bel (X = xi ) = 1where X denotes the query node and E is the set ofevidence nodes. In the second step, node X sends new λ messages By using the local structure of a BN, we show that to its parentsbelief updating can be divided into the predictive sup-port for X from evidence nodes connected to X through λX (ui ) = λ(xi ) P (xi | ui , . . . , un ) πX (uk )its parents, U1 , . . . , Um , and the diagnostic support for xi uk :k=i k=iX from evidence nodes connected to X through itschildren, Y1 , . . . , Ym (Korb and Nicholson [2004]). and new π messages to its children Pearl’s [1982] message-passing algorithm shows how to update the posterior distribution Bel(X). The  1 if evidence value xi is enteredderivation involves the repeated application of Bayes’s πYj (xi ) = 0 if evidence is for another value xjtheorem and the use of the conditional independencies  αBel(xi )/λYj (xi ) otherwiseencoded in the network structure. The basic idea is thatBel(X) is updated locally at each iteration of the al- This procedure is repeated until a node has received allgorithm using three parameters, λ(X), π (X), and the messages.conditional probability table (CPT), where λ(X) and We divide the inference methods into two cat-π (X) are computed using the messages received from egories, exact inference and approximate inferencethe parents π and the children λ of node X(Korb and algorithms. The latter have been developed becauseNicholson [2004]). In Bayes’s theorem, π plays the probabilistic inference can be computationally dif-role of the prior and λ plays the role of the likelihood. ficult for complex networks because the required The algorithm requires that three types of parame- computational power increases exponentially with theters be maintained: number of parent nodes. Well-known algorithms for24
  6. 6. PRIOR PERFORMANCE AND RISK-TAKINGexact inference include variable elimination, Pearl’s 28.26% in 2003. Similarly, the performance of singlemessage-passing algorithm, the noisy-or-gate algo- funds shows a high degree of dispersion.rithm, and the junction tree algorithm. For approx-imate inference, standard algorithms include likeli-hood weighting, logic sampling, and Markov chain Performance MeasurementMonte Carlo (MCMC) (for an overview, see Korb and A number of approaches have been suggested toNicholson [2004] or Neapolitan [2004]). measure fund performance (see, e.g., Kothari and Warner [2001], Wermers [2000], and Daniel et al.Data [1997]). To estimate the exposure toward the Fama For our analysis, we use a complete sample of all and French [1993] risk factors and the Carhart [1997]U.S. open-end equity funds, containing a total of 1,923 momentum factor, we run the following regression forfunds. The data set comes from Reuters Lipper. For each fund i and each calendar year t:each fund, we have information about launch date,sector (equity, international, large-cap, mid-cap, and ri,t − rf,t = αCarhart, i, t + MRPCarhart, i, t · rCarhartsmall-cap), style (income, core, growth, value), annual + HMLi, t · rHML + SMBi, t · rSMBfee, and total assets as of April 30, 2004. We alsohave monthly price information over nineteen years, + UMDi, t · rUMD + εCarhart, i, tDecember 1984 to December 2003. We exclude inter-national mutual funds from our analysis, as well asfunds with less than two years of data. where ri,t denotes the return of fund i, rf,t is the For benchmarking purposes, we use the excess re- risk-free rate, and εCarhart,i,t is the regression resid-turn on the S&P 500 index as the market portfolio, and ual. The coefficients to be estimated are denoted bythe three-month Treasury bill rate as the risk-free rate. MRPCarhart,i,t , HMLi,t , SMBi,t , and UMDi,t , and theThese data come from Datastream. As a second bench- risk premia by rCarhart , rHML , rSMB , and rUMD . We usemark, we use the Carhart [1997] four-factor model. The an analogous approach for the risk exposure with re-data for the market risk premium, the size premium, spect to the S&P 500:the value premium, and the momentum premium comefrom the Fama and French data library. ri, t − rf,t = αSP500, i, t + MRPSP500, i, t · rSP500 Table 1 gives the descriptive statistics for the datawe use here. The number of funds increased from 191 + εSP500, i, t .in 1985 to 1,478 in 2003. The average return across allfunds fluctuated substantially during that time, from In the analysis following, RRaw, i, t = ri, t is the un-a minimum of −26.35% in 2002 to a maximum of adjusted return of a fund, RSP500, i, t = αSP500, i, t is theTable 1. Descriptive Statistics for Annual Continuously Compounded ReturnsYear Funds Mean Std. Skew Kurt Min 25% 50% 75% Max1985 191 18.87 7.37 −0.01 3.67 −3.23 14.18 19.43 23.23 43.561986 216 0.49 10.80 −0.20 5.28 −48.43 −5.64 0.55 8.00 45.381987 243 −13.49 12.46 −0.23 5.87 −60.19 −19.86 −13.13 −5.38 42.811988 284 9.46 8.12 0.08 4.52 −15.31 4.27 9.58 14.36 46.101989 305 15.31 8.38 0.01 3.44 −9.40 10.36 14.95 20.95 41.451990 326 −11.70 8.79 −0.63 3.76 −41.48 −16.63 −10.80 −5.52 11.511991 350 26.28 12.08 −1.29 16.97 −75.62 18.99 25.39 32.88 63.081992 387 3.52 8.02 −0.72 7.93 −48.47 −0.39 3.59 7.29 30.101993 452 5.34 8.53 −1.14 10.06 −51.79 0.49 5.73 10.44 36.901994 539 −6.52 7.30 −0.54 5.32 −41.25 −10.34 −5.95 −2.41 21.081995 622 20.44 8.72 −0.36 5.07 −22.82 15.67 21.01 25.75 48.421996 701 9.39 9.09 −1.07 9.59 −61.24 4.93 9.56 14.72 44.061997 822 11.36 11.24 −1.18 7.27 −56.69 5.48 12.89 18.44 49.911998 977 6.57 15.34 −0.12 3.58 −48.04 −3.24 6.84 16.87 58.221999 1114 17.05 22.76 1.07 5.02 −53.06 1.48 13.49 27.79 136.392000 1233 −12.14 22.51 −1.52 9.89 −188.63 −23.45 −10.09 2.68 40.862001 1369 −11.83 16.82 −0.30 4.28 −90.89 −21.35 −12.61 −1.30 45.782002 1478 −26.35 11.34 −0.66 5.64 −103.74 −32.59 −26.08 −19.18 14.082003 1478 28.26 8.98 0.42 12.28 −58.39 22.29 26.70 33.06 97.56The table gives descriptive statistics for annual continuously compounded returns for all funds existing in one particular year. The data set isfrom Reuters Lipper. 25
  7. 7. AMMANN & VERHOFENrisk-adjusted return using the S&P 500 as a benchmark, use a multinomial distribution with four classes whereand RCarhart, i, t = αCarhart, i, t is the risk-adjusted return variables are grouped into quartiles for each year.using the Carhart model as a benchmark. The implementation of the Bayesian network has The tracking error measures a fund’s deviation from been carried out using the “Bayes’s Net Toolbox fora passive index. We define tracking error TE as the MatLab.” For testing, we created a large number ofvolatility σ of the residuals of the regressions on the different artificial data sets and re-extracted the un-index, i.e., derlying probability distributions. In all instances, the underlying probability distributions were recovered TE Carhart, i, t = σ (εCarhart, i, t ) and TESP500, i, t accurately. We initialized the BN by setting all aijk to 1, thus = σ (εSP500, i, t ). ensuring the analysis incorporates no material prior in- formation. The BN is used primarily as an econometric tool.Implementation Figure 2 shows the corresponding dynamic Empirical ResultsBayesian network. For each fund, we estimate eightdifferent factors describing mutual fund behavior for The analysis is structured as follows. We focus firsteach year: the standard deviation of returns, the beta on the marginal distributions within the time period ofagainst the S&P 500 and the Fama and French mar- the relationship between risk and return. We then focusket portfolio, the loading on the value versus growth on intertemporal relationships of different measures offactor, the loading on the size factor, the loading on risk and style. Finally, we turn to the marginal distribu-the momentum factor, and the tracking error against tions of risk and style conditional on past performance.the S&P 500 and the Fama and French market Due to the large amount of data, we focus on the em-portfolio. pirical part of risk-adjusted returns using the S&P 500 We performed a likelihood ratio test, and found that as a benchmark in a one-factor model. Unless other-all arcs in the BN are highly significant. To get robust wise stated, results for unadjusted returns and returnsresults that are independent of restrictive distributional adjusted with the Carhart four-factor model are veryassumptions and to incorporate non-linear behavior, we similar. FIGURE 2 Dynamic Bayesian Network Used in the Empirical AnalysisWe analyze the joint effect of the risk level in T and the risk-adjusted return in T on the choice of the risk level in T + 1 for a number ofdifferent measures of risk, w. The data set was provided by Reuters Lipper and consists of 1,923 funds with return data from 1984 to 2004.We used the following as risk measures: volatility (STD), beta with respect to the market portfolio (MRP), factor loading on the value premium(HML), factor loading on the size premium (SMB), factor loading on the momentum premium (UMD), and tracking error (TE). A return isdenoted by R. Tracking error and beta are computed with respect to the S&P 500 and the CRSP market portfolio using one-factor and four-factormodels, respectively (denoted as TESP500 , TECarhart , MRPSP500 , and MRPCarhart ). Similarly, return R is computed on a raw basis (without riskadjustment), risk-adjusted in a one-factor model with respect to the S&P 500 and the Carhart [1997] four-factor model (denoted as RRaw ,RSP500 , and RCarhart ).26
  8. 8. PRIOR PERFORMANCE AND RISK-TAKINGTable 2. Transition Probabilities between Risk and Return in Q4. Similarly, funds with a low standard deviation in Q1 have a 30.8% chance of reaching a high return in RSP500,T Q4, while funds with a high standard deviation in Q4From To Q1 Q2 Q3 Q4 have a 41.4% chance. Funds with a low risk level have a higher chance of reaching a return centered aroundSTDT Q1 11.8%∗ 28.9%∗∗ 28.3%∗∗ 30.8%∗∗ Q2 15.5%∗ 25.1% 30.6%∗∗ 28.6%∗∗ the mean. For example, a fund with a volatility in Q1 Q3 17.9%∗ 21.7%∗ 25.0% 34.4%∗∗ has a 28.9% chance of a return in Q2; a fund with a Q4 22.8%∗ 16.6%∗ 19.0%∗ 41.4%∗∗ high volatility has a 16.6% chance.MRPSP500,T Q1 16.0%∗ 18.6%∗ 22.6%∗ 42.6%∗∗ For the exposure to market risk, or beta, the findings Q2 15.1%∗ 22.4%∗ 27.8%∗ 34.5%∗∗ are reversed. Funds with a low beta in Q1 have a 42.6% Q3 16.5%∗ 25.7% 27.4%∗∗ 30.2%∗∗ Q4 21.1%∗ 21.8%∗ 23.9% 33.0%∗∗ chance of achieving a risk-adjusted return in Q4. High-HMLT Q1 24.0% 23.0%∗ 19.4%∗ 32.6%∗∗ beta funds have only a 33.0% transition probability of Q2 15.7%∗ 28.0%∗∗ 30.0%∗∗ 26.2% a return in Q4. The data show that the higher a fund’s Q3 13.1%∗ 24.7% 30.3%∗∗ 31.8%∗∗ beta, the lower its relative risk-adjusted return. Q4 17.8%∗ 15.6%∗ 22.2%∗ 44.2%∗∗ The style factors in the Carhart [1997] four-factorSMBT Q1 21.7%∗ 28.2%∗∗ 25.7% 24.2% Q2 14.5%∗ 30.4%∗∗ 29.9%∗∗ 25.0% model, i.e., the value premium, size premium, and Q3 13.3%∗ 24.2% 29.6%∗∗ 32.7%∗∗ momentum premium (UMD), show the expected re- Q4 19.5%∗ 14.8%∗ 21.1%∗ 44.4%∗∗ sults. As documented by Fama and French [1993],UMDT Q1 33.7%∗∗ 30.7%∗∗ 18.4%∗ 17.0%∗ trading strategies based on size factors and valuation Q2 20.3%∗ 32.1%∗∗ 26.5%∗∗ 21.0%∗ ratios have historically earned superior returns. Value- Q3 12.9%∗ 22.0%∗ 32.0%∗∗ 33.0%∗∗ Q4 12.9%∗ 14.3%∗ 23.2%∗ 49.4%∗∗ oriented funds, i.e., those with a high loading on theTESP500,T Q1 14.3%∗ 29.2%∗∗ 30.8%∗∗ 25.5% value premium (HMLT in Q4), have a 44.2% chance of Q2 18.1%∗ 25.1% 27.7%∗∗ 29.0%∗∗ a return in Q4. In contrast, growth funds (HMLT in Q1) Q3 18.0%∗ 19.0%∗ 22.5%∗ 40.3%∗∗ have a 32.6% chance of a return in Q4, and small-cap Q4 20.7%∗ 13.9%∗ 19.4%∗ 45.8%∗∗ funds (SMBT in Q4) have a 44.4% chance. Large-capThe table gives the transition probabilities between different mea- stocks have only a 24.2% chance.sures of risk and the subsequent return. *denotes a value statistically The findings for momentum funds are even moredifferent from 0.25 on a 95% level, and ∗∗ denotes a 99% level. Stan- striking. Funds with a high exposure to momentumdard errors have been computed by bootstrapping. stocks (Q4) had a 49.4% chance for a return in theThe table is interpreted as follows. Say in one particular year a fund highest quartile. For funds avoiding momentum stockshas a volatility in the lowest quartile among all other funds. Thefund would have a probability of 11.8% (first row, first column) (Q1), this chance is reduced to 17.0%.of achieving a risk-adjusted return in the lowest quartile among all The tracking error indicates the magnitude of activeother funds. Analogously, the chance is 30.8% (first row, fourth portfolio management of a fund manager. The tran-column) of obtaining a risk-adjusted return in the highest quartile sition matrix shows that fund managers with a highamong all other funds. tracking error (Q4) are 45.8% more likely to have a return in the highest quartile than fund managers with a low tracking error (Q1) (25.0%). The data indicateRisk and Return that active portfolio management has had some value. Table 2 shows the relationship between the set ofvariables describing the risk exposure of mutual fundsand the performance measured on a risk-adjusted basis. Persistence in Risk LevelsWe test for significance against a null hypothesis of ano-probabilistic relationship, i.e., the null hypothesis In this section, we analyze the persistence in thefor a multinomial distribution with four classes is that choice of risk levels. Table 3 shows the relationshipthe probability for each class is 25%. between standard deviation in period T and the succes- We interpret Table 2 as follows. Suppose one year sive period T + 1. The diagonal elements of the tran-a fund experiences volatility in the lowest quartile. sition matrix are especially interesting, because theyThe fund would then have an 11.8% probability (first represent the degree of persistence in the choice of riskrow, first column) of achieving a risk-adjusted return in levels.the lowest quartile among all funds. Analogously, the For the standard deviation, the probabilities of stay-chance is 30.8% (first row, fourth column) of obtaining ing in the same quartile (the diagonal elements ofa risk-adjusted return in the highest quartile. the transition matrix) are 50.4%, 37.8%, 41.2%, and We find that the higher the volatility, the more likely 70.1%, respectively. This means that a fund with a lowa fund will have an extreme return in quartiles 1 (Q1) return volatility has a 50.4% chance of staying in theand 4 (Q4). The transition probability of ending in the lowest quartile, a fund with a volatility in the secondfirst quartile of returns is only 11.8% for funds with a quartile has a 37.8% chance of staying in the samevolatility in Q1, and 22.8% for funds with a volatility quartile, and so on. 27
  9. 9. AMMANN & VERHOFENTable 3. Transition Probabilities between Risk in T and T ticularly high for funds with a high concentration of+1 investment in small-caps (high SMB factor). However, XXXT M: RSP500,T this finding is consistent with prior expectations, be- cause the choice of risk level and its persistence are atFrom To Q1 Q2 Q3 Q4 least partially a result of a fund’s investment policy. For the tracking error against the S&P 500, the diag-STDT Q1 50.4%∗∗ 32.9%∗∗ 14.0%∗ 2.6%∗ Q2 24.4% 37.8%∗∗ 29.7%∗∗ 7.9%∗ onal transition probabilities are 61.9%, 36.8%, 32.5%, Q3 8.4%∗ 22.8% 41.2%∗∗ 27.4% and 62.4%. Therefore, we find that the persistence of Q4 1.2%∗ 6.3%∗ 22.2%∗ 70.1%∗∗ active and passive portfolio management measured asMRPSP500,T Q1 46.0%∗∗ 28.7%∗∗ 17.6%∗ 7.5%∗ the deviation from the index is substantial. Q2 25.4% 31.0%∗∗ 27.9%∗∗ 15.5%∗ Overall, we find strong evidence for persistence in Q3 11.8%∗ 25.5% 36.4%∗∗ 26.1% Q4 4.7%∗ 13.8%∗ 27.0% 54.1%∗∗ the choice of risk levels. In particular, funds with veryHMLT Q1 49.7%∗∗ 24.6% 15.5%∗ 9.9%∗ high and very low exposure to specific risk factors show Q2 26.4% 34.3%∗∗ 25.0% 13.3%∗ a high degree of persistence. However, these results Q3 14.9%∗ 27.7%∗∗ 32.4%∗∗ 24.8% are not surprising because a number of factors, espe- Q4 11.5%∗ 15.4%∗ 28.5%∗∗ 44.4%∗∗ cially institutional restrictions, tend to lead to persistentSMBT Q1 49.3%∗∗ 29.5%∗∗ 14.7%∗ 6.4%∗ Q2 37.8%∗∗ 33.1%∗∗ 18.9%∗ 10.0%∗ behavior. Q3 15.4%∗ 17.7%∗ 35.1%∗∗ 31.7%∗∗ Q4 4.2%∗ 5.8%∗ 21.1%∗ 68.7%∗∗UMDT Q1 42.9%∗∗ 25.2% 18.0%∗ 13.7%∗ Impact of Prior Performance Q2 26.0% 32.7%∗∗ 24.5% 16.1%∗ Q3 16.1%∗ 27.8%∗∗ 31.3%∗∗ 24.6% We next examine how the impact of prior perfor- Q4 11.4%∗ 15.2%∗ 26.3% 46.9%∗∗ mance influences the risk-taking behavior of mutualTESP500,T Q1 61.9%∗∗ 25.5% 9.1%∗ 3.4%∗ fund managers. The complete empirical results are in- Q2 22.5% 36.8%∗∗ 25.4% 15.2%∗ cluded in the appendix (Tables A1 and A2). Due to the Q3 7.4%∗ 23.9% 32.5%∗∗ 36.0%∗∗ large amount of empirical data, we focus on the results Q4 1.7%∗ 9.8%∗ 26.0% 62.4%∗∗ shown in Tables 4 and 5.The table gives the transition probabilities between different mea- Table 4 shows the difference in transition probabili-sures of risk in T and T + 1. XXX means that the target (column) ties for top- and poor-performing mutual funds for risk-variable is the same as in the appropriate row. The table gives the adjusted returns using the S&P 500 as the benchmark.results for the model with risk adjustment using the S&P 500. *de- For volatility, we find that poor-performing fund man-notes a value statistically different from 0.25 on a 95% level, and∗∗ on a 99% level. Standard errors have been computed by agers tend to strongly decrease their portfolio volatil-bootstrapping. ity in the following calendar year. For example, theThe table is interpreted as follows. Suppose a fund has in one partic- difference in transition probabilities for funds with aular year a volatility in the lowest quartile among all other funds. The volatility in the highest quartile is 18.9%. Only suc-fund manager would have selected with a probability of 50.4% (first cessful funds with low volatility (in Q1) increase theirrow, first column) a return volatility in the lowest quartile amongall funds in the next calendar year. Analogously, the chance is 2.6% volatility the following calendar year.(first row, fourth column) to select a volatility on the highest quartile For beta, the results are mixed. Funds with expo-among all other funds in the next calendar year. sure to market risk above the median (Q3 and Q4) take on more market risk the following calendar year (e.g., the difference in transition probabilities for Q4 to In contrast, for the exposure toward market risk, Q4 is 9.8%). This indicates successful fund managersthe data show a lower degree of behavior persistence. are 9.8% more likely to maintain their risk level thanFor the beta against the S&P 500, the percentages of unsuccessful managers.staying in the same class are 46.0%, 31.0%, 36.4%, and In contrast, unsuccessful managers, with a beta in54.1%, respectively. Therefore, we conclude that fund Q1, increase their market risk exposure significantly.managers are more likely to adjust their market risk For example, the difference in transition probabili-than their portfolio volatility. In general, the degree of ties from Q1 to Q2 is −8.4%. This indicates poor-persistence is larger for funds with a very low or very performing funds are 8.4% more likely to increase theirhigh exposure to a risk factor. market risk exposure. Our findings are similar for the style factors in the We interpret the style factors as follows. A highCarhart [1997] four-factor model. For the HML factor, loading (in Q4) on the value factor (HML) indicates athe transition probabilities for staying in the same class fund invests in value stocks; a low factor loading onare 49.7%, 34.4%, 32.4%, and 44.4%; for the SMB fac- the value factor (Q1) is interpreted as an investment intor, they are 49.03%, 33.1%, 35.1%, and 68.7%. For the growth stocks. A fund investing in small-caps showsmomentum (UMD) factor, the percentages are 42.9%, high exposure to the size factor (SMB), and a large-32.7%, 31.3%, and 46.9%. Therefore, over the whole cap fund shows an exposure in the first quartile. Fundssample, the persistence in choice of risk levels is par- investing in momentum stocks have a high loading on28
  10. 10. PRIOR PERFORMANCE AND RISK-TAKINGTable 4. Impact of Prior Performance on the Choice of Table 5. Impact of Prior Performance on the Choice ofRisk Level (returns adjusted with the S&P 500) Risk Level (returns adjusted with the Carhart Model) XXXT+1 Difference XXXT+1 DifferenceFrom To Q1 Q2 Q3 Q4 From To Q1 Q2 Q3 Q4STDT Q1 6.3% −1.5% −1.1% −3.6%∗∗ STDT Q1 7.0% 1.6% −5.8% −2.8%∗∗ Q2 −10.3%∗∗ −0.9% 7.4%∗ 3.8%∗ Q2 2.1% −3.8% 2.6% −1.0% Q3 −8.5% ∗∗ −9.0% ∗∗ 0.0% 17.0%∗∗ Q3 1.0% 1.7% −0.1% −2.6% Q4 −2.1%∗∗ −6.0%∗∗ −10.7%∗∗ 18.9%∗∗ Q4 0.0% 0.0% −1.2% 1.4%MRPSP500,T Q1 13.3%∗∗ −8.4%∗∗ −1.8% −3.0%∗∗ MRPSP500,T Q1 −0.2% −2.1% 4.0%∗ −1.6% Q2 3.3% −6.8%∗∗ 3.3% 0.0% Q2 −6.8%∗∗ −3.7% 7.6%∗∗ 2.8% Q3 −5.8%∗∗ −4.7%∗∗ 1.7% 8.8%∗∗ Q3 −3.9%∗ −7.5%∗∗ 2.7% 8.6%∗ Q4 −5.0%∗∗ −1.8% −3.0% 9.8%∗∗ Q4 −2.3%∗∗ −1.2% 0.0% 2.9%HMLT Q1 −8.5%∗∗ −3.6% 5.4%∗∗ 6.7%∗∗ HMLT Q1 −3.4% −1.5% 2.4%∗ 2.5%∗ Q2 −8.4%∗∗ −0.5% 2.3% 6.6%∗∗ Q2 −5.9%∗∗ 7.1%∗∗ 2.1% −3.3% Q3 −7.1%∗∗ −4.1% 5.8%∗ 5.4%∗ Q3 −4.6%∗ 2.4% 0.4% 1.7% Q4 −9.8%∗∗ −0.5% 3.2%∗ 7.1%∗∗ Q4 0.0% −0.3% 0.0% −0.1%SMBT Q1 −6.7%∗ −3.1% 3.0% 6.8%∗∗ SMBT Q1 15.5%∗∗ 1.4% −10.3%∗∗ −6.6%∗∗ Q2 −11.4%∗∗ −1.8% 5.5%∗ 7.7%∗∗ Q2 7.4%∗∗ −0.3% −3.2% −3.7%∗ Q3 −13.1%∗∗ −7.9%∗∗ 5.0%∗ 16.0%∗∗ Q3 −3.9%∗ 0.0% −0.1% 4.1% Q4 −6.5%∗∗ −5.8%∗∗ −1.2% 13.6%∗∗ Q4 −0.5% −0.2% 4.1%∗ −3.4%∗UMDT Q1 −13.0%∗∗ −5.2%∗ 8.7%∗∗ 9.5%∗∗ UMDT Q1 1.7% 1.2% 1.9% −4.9%∗∗ Q2 −3.4% −1.4% 2.0% 2.7% Q2 −2.0% 8.1%∗∗ −0.4% −5.5%∗∗ Q3 −8.7%∗∗ 1.5% 9.5%∗∗ −2.2% Q3 −7.2%∗∗ 0.6% 11.2%∗∗ −4.0% Q4 −6.6%∗∗ −1.9% 5.3%∗∗ 3.2% Q4 −2.6%∗ −0.5% 5.9%∗∗ −2.6%TESP500,T Q1 13.0%∗∗ −11.5%∗∗ −4.1% 2.7%∗ TESP500,T Q1 30.4%∗∗ −6.9%∗∗ −18.9%∗∗ −4.4%∗∗ Q2 −5.8%∗ −4.3% 1.7% 8.4%∗∗ Q2 3.6% 3.6% −3.2% −4.0% Q3 −6.6%∗∗ −5.0%∗ 0.9% 10.6%∗∗ Q3 −1.8%∗ 5.9%∗ −2.6% −1.4% Q4 −3.1%∗∗ −9.3%∗∗ −4.5%∗ 17.0%∗∗ Q4 −0.1% 0.0% 1.6% −1.5%The table gives the difference in transition probabilities between dif- The table shows the difference in transition probabilities betweenferent measures of risk in T and T + 1 for funds with a performance different measures of risk in T and T + 1 for funds with perfor-in the highest quarter in T and for funds with a performance in the mances in the highest and lowest quarters in T . XXX means thatlowest quarter in T . XXX denotes that the target (column) variable the target (column) variable is the same as in the appropriate the same variable as in the appropriate row. The table gives the The table shows the results for returns adjusted with the Carhartresults for returns adjusted with the S&P 500. *denotes a value sta- model. ∗ denotes a value statistically different from 0 on a 95%tistically different from 0 on a 95% level, and ∗∗ on a 99% level. level, and ∗∗ on a 99% level. Standard errors have been computed byStandard errors have been computed by bootstrapping. The results this table are based on a risk-adjusted return using a one-factor The results in the table have been computed as follows. Top-model and the S&P 500 as the market portfolio. performing funds have a return in the fourth quartile in one calendarThe results in the table have been computed as follows. Top- year, poor-performing funds have a return in the first quartile in oneperforming funds have a return in the fourth quartile in one calendar calendar year. Both groups have different transition matrices for theyear, poor-performing funds have a return in the first quartile in one risk level in the next year. We show the difference of element-by-calendar year. Both groups have different transition matrices for the element subtraction of the transition matrices. The transition matrixrisk level in the next year. We show the difference of element-by- of poor-performing funds has been subtracted from the matrix ofelement subtraction of the transition matrices. The transition matrix top-performing funds. Therefore, positive elements indicate that theof poor-performing funds has been subtracted from the matrix of transition probability for top-performing funds was higher than fortop-performing funds. Therefore, positive elements indicate that the poor-performing funds, and vice versa.transition probability for top-performing funds was higher than forpoor-performing funds, and vice versa. investment (transition element Q1 to Q4), and poor- performing funds are 8.5% more likely to continuethe UMD factor (Q4), and contrarian funds have a low the unsuccessful growth stock investments (transitionloading on UMD. element Q1 to Q1). For the HML, SMB, and UMD factors, there is over- For the size exposure, the change in behavior iswhelming evidence of increased risk-taking by suc- even stronger. All successful managers, regardless ofcessful fund managers. They tend to invest heavily in their prior size exposure, invest substantially in small-the future in value stocks, small-caps, and momentum caps. Successful managers who have previously in-stocks. vested primarily in large-caps are 6.8% more likely to For the loading on the value premium (HML), all invest substantially in small-caps than their unsuccess-differences in transition probabilities ending in Q1 are ful counterparts (transition probability Q1 to Q4).negative; those ending in Q4 are positive. For exam- For the momentum exposure, we again see in-ple, successful funds that invested in growth stocks creased risk-taking by successful managers, but it isare 6.7% more likely to switch to a substantial value less evident. We observe material changes for fund 29
  11. 11. AMMANN & VERHOFENmanagers who previously neglected momentum stocks Discussion(Q1). If these fund managers achieved good perfor-mance, they were 9.5% more likely to invest heav- Existing literature on the behavior of mutual fundily in momentum stocks in the future than poor- managers has focused on incentives (Brown, Har-performing managers (transition probability Q1 to low, and Starks [1996], Chevalier and Ellison [1997],Q4). Carpenter [2000], Busse [2001], and Carhart et al. Tracking error as a measure of active portfolio man- [2002]). Incentives in the mutual fund industry are pri-agement validates the findings for other variables. Suc- marily driven by two factors, compensation schemescessful managers generally increase tracking error; and investor behavior. Standard compensation schemespoor-performing fund managers tend to decrease it. in the mutual fund industry are convex, i.e., fund man-Successful fund managers are 17.0% more likely to agers take part in the positive performance of theirmaintain a tracking error in the highest quartile (Q4), funds by receiving bonuses, but they do not usually takecompared to unsuccessful ones. part in the negative performance. Portfolio managers We next analyze how portfolio managers respond to have a call option on the portfolio they are managing.prior performance measured with the Carhart [1997] Moreover, intertemporal investor behavior in-four-factor model, which explains a high proportion creases the effect of convex compensation schemes.of the cross-sectional variance of mutual fund per- Investors tend to allocate a large proportion of newformance. After accounting for a fund’s exposure capital to funds that performed well in the previousagainst value and growth stocks, large- and small- period, but they do not tend to withdraw capital fromcaps, and momentum stocks, Carhart finds little ev- poorly performing funds. Therefore, if a manager’sidence for persistence in the performance of mutual salary depends on assets under management, investorfunds. behavior induces a convex relationship between fund Overall, the results for returns risk-adjusted with performance and fund size. Overall, theoretically, boththe Carhart model and with a one-factor model, the patterns lead to excessive risk-taking by mutual fundS&P 500, indicate differences (see Table 5). Table 4 managers.indicates substantial and significant changes in behav- However, in our empirical analysis, we were un-ior, especially for the first and fourth quartiles. But the able to find evidence of such behavior. Our findingsresults for the Carhart model are less apparent. are probably attributable to the different setting in this For volatility in general, we find no change in be- study compared to others. We used a large sample ofhavior. However, there is little evidence for increased funds over a longer time period and different measuresrisk-taking by low-performing funds. For beta, the data of risk and return. We also imposed less restrictive as-confirm our previous findings. A good performance in- sumptions for the empirical analysis (we do not assumeduces increased risk-taking the following period. How- any linear relationships or normal distributions).ever, most findings are statistically insignificant. Brown, Harlow, and Starks [1996] focus solely on For the HML, SMB, and UMD factors, the results volatility as a risk measure, and use only a small sampleare mixed and differ somewhat from previous findings. of funds focusing on growth stocks over a fifteen-yearFor HML, we find a clear, statistically significant pat- time period. Their analysis focuses on mid-year effects,tern. For SMB, the findings are reversed. For UMD, and they find that funds performing poorly by mid-yearthe results are ambiguous. For the Carhart model, we tend to increase their volatility over the rest of the year.find strong evidence that successful fund managers ab- Busse [2001] uses a very similar methodology andstain from small-caps (the transition probability from the same data set as in Brown, Harlow, and StarksQ4 to Q4 is −3.4%). This indicates successful fund [1996] but with a daily frequency. He finds that intra-managers tend to reduce their exposure to small-caps year fund changes are attributable to changes in thecompared to poor-performing fund managers. volatility of common stocks and are not related to After controlling for a fund’s style, we find that un- changing factor exposures or residual risk.successful funds tend to change their momentum strat- Similarly, Chevalier and Ellison [1997] analyze theegy, but in different directions. The transition proba- impact of past performance on fund flows using a semi-bility from Q2 to Q2 is significantly positive at 8.1%, parametric approach. Their results confirm prior ex-indicating successful funds are 8.1% more likely to pectations: The flow-performance relationship createschoose a comparable momentum level. Unsuccessful incentives for fund managers to adjust fund riskinessfunds change their strategy in both directions: some depending on mid-year performance.choose a contrarian strategy, others opt for a stronger How can we explain increased risk-taking aftermomentum strategy. years of good performance, and decreased risk-taking For tracking error, superior performance tends to after years of poor performance? Our explanation islead to a more passive investment style after control- two-sided. First, poor-performing managers follow aling for a fund’s style. However, this finding is only more passive strategy to minimize their future risks.significant for the first quartile transition probabilities. Relative performance, not absolute performance, is30
  12. 12. PRIOR PERFORMANCE AND RISK-TAKINGrelevant. Second, successful managers take on more a number of ways. In contrast to existing studies, werisk because they have become more confident in their do not focus solely on volatility as a measure of risk.own skills. Success creates confidence. Basically, our We use other measures such as beta, tracking error,analysis shows that the best explanation can be found and style measures such as the high-minus-low (HML)in a combination of the models by Lynch and Musto factor, the small-minus-big (SMB) factor, and the mo-[2003] for unsuccessful managers and Berk and Green mentum (UMD) factor as well. Furthermore, we use a[2004] for successful managers. Mutual fund manager robust, non-parametric approach and are therefore ablebehavior is more complex than assumed by theoretical to capture a wide range of non-linear and asymmetricmodels, which usually capture only one aspect of the patterns because we do not impose any restrictive dis-actual behavior. tributional assumptions. To combat a data bias, we use Lynch and Musto [2003] propose a model in which a complete set of all U.S. equity funds to ensure a longstrategy changes occur only after periods of bad per- time period of data, rather than a subgroup of mutualformance. However, a priori, their model does not ex- funds.plain how the strategy changes. Their empirical anal- Our analysis does not lend any support to the hy-ysis finds evidence of a change in factor loadings: pothesis that poor-performing fund managers increasePoor performers seem to increase their UMD loading their risk level. We find that prior performance has aand decrease their HML loading. Neither market beta positive impact on the choice of risk level, i.e., success-nor SMB loading is systematically affected by fund ful fund managers take on more risk in the followingperformance. time period. In particular, they increase volatility, beta, The different results by Lynch and Musto [2003] and tracking error, and assign a higher proportion ofwhen compared to our analysis might be due to their their portfolio to value stocks, small firms, and momen-shorter sample period and to our use of a non-linear tum stocks. Overall, poor-performing fund managersmodel. Our analysis shows that, after a period of poor switch to passive strategies. Unsuccessful managersperformance, managers choose a passive investment decrease the level of idiosyncratic risk and follow thestyle (lower tracking error). They take less market risk, relevant benchmark more closely.decrease their exposure to value, and increase their ex-posure to large-caps and stocks with a low momentumeffect. Acknowledgments Berk and Green [2004] propose a model that in-corporates two important features. First, performance We thank the editor, Bernd Brommundt, Alexanderis not persistent, i.e., active portfolio managers do not Ising, Stephan Kessler, Axel Kind, Jennifer Noll, An-outperform passive benchmarks on average. Second, gelika Noll, Ralf Seiz, Stephan S¨ ss, Rico von Wyss, ufund flows respond rationally to past performance. and Andreas Zingg for valuable comments. We ac-They assume investors behave as Bayesians, updat- knowledge helpful comments of the participants froming their beliefs about a fund manager’s skill based on the Joint Research Workshop of the University of St.observed returns and prior beliefs. Gallen and the University of Ulm in 2005. We ac- knowledge financial support from the Swiss National Science Foundation (SNF). Conclusion How do mutual fund managers react to pastperformance? 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  14. 14. Table A1. Impact of Prior Performance on Choice of Risk Level (returns adjusted with the S&P 500) XXXT+1 RSP500,T =4Q XXXT+1 RSP500,T =1Q XXXT+1 Difference from to Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 STDT Q1 50.6%∗∗ 31.1%∗∗ 15.5%∗ 2.7%∗ 44.2%∗∗ 32.7%∗ 16.6%∗ 6.3%∗ 6.3% −1.5% −1.1% −3.6%∗∗ Q2 22.3% 35.4%∗∗ 29.8%∗ 12.3%∗ 32.7%∗ 36.3%∗∗ 22.4% 8.4%∗ −10.3%∗∗ −0.9% 7.4%∗ 3.8%∗ Q3 4.8%∗ 16.1%∗ 39.5%∗∗ 39.4%∗∗ 13.4%∗ 25.2% 38.9%∗∗ 22.3% −8.5%∗∗ −9.0%∗∗ 0.0% 17.0%∗∗ Q4 0.3%∗ 2.6%∗ 14.3%∗ 82.5%∗∗ 2.5%∗ 8.6%∗ 25.1% 63.6%∗∗ −2.1%∗∗ −6.0%∗∗ −10.7%∗∗ 18.9%∗∗ MRPSP500,T Q1 54.3%∗∗ 23.8% 14.8%∗ 6.9%∗ 40.9%∗∗ 32.2%∗∗ 16.7%∗ 10.0%∗ 13.3%∗∗ −8.4%∗∗ −1.8% −3.0%∗∗ Q2 31.0%∗∗ 26.5% 26.0% 16.3%∗ 27.6% 33.3%∗∗ 22.6% 16.3%∗ 3.3% −6.8%∗∗ 3.3% 0.0% Q3 13.8%∗ 23.3% 30.6%∗∗ 32.1%∗∗ 19.6%∗ 28.1% 28.8% 23.3% −5.8%∗∗ −4.7%∗∗ 1.7% 8.8%∗∗ Q4 3.9%∗ 11.3%∗ 22.1%∗ 62.5%∗∗ 8.9%∗ 13.2%∗ 25.1% 52.6%∗∗ −5.0%∗∗ −1.8% −3.0% 9.8%∗∗ HMLT Q1 46.1%∗∗ 21.6%∗ 17.8%∗ 14.3%∗ 54.7%∗∗ 25.2% 12.4%∗ 7.5%∗ −8.5%∗∗ −3.6% 5.4%∗∗ 6.7%∗∗ Q2 26.0% 28.8% 24.5% 20.4%∗ 34.5%∗∗ 29.4%∗∗ 22.1%∗ 13.8%∗ −8.4%∗∗ −0.5% 2.3% 6.6%∗∗ Q3 14.3%∗ 18.7%∗ 31.9%∗∗ 34.9%∗∗ 21.4% 22.8% 26.1% 29.5%∗∗ −7.1%∗∗ −4.1% 5.8%∗ 5.4%∗ Q4 7.7%∗ 13.3%∗ 26.2% 52.5%∗∗ 17.6%∗ 13.9%∗ 2.0% 45.4%∗∗ −9.8%∗∗ −0.5% 3.2%∗ 7.1%∗∗ SMBT Q1 38.7%∗∗ 24.7% 22.2%∗ 14.2%∗ 45.5%∗∗ 27.9% 19.2%∗ 7.3%∗ −6.7%∗ −3.1% 3.0% 6.8%∗∗ Q2 24.0% 29.2%∗∗ 25.3% 21.3%∗ 35.5%∗∗ 31.1%∗∗ 19.7%∗ 13.6%∗ −11.4%∗∗ −1.8% 5.5%∗ 7.7%∗∗ Q3 7.2%∗ 10.7%∗ 36.0%∗∗ 45.9%∗∗ 20.3%∗ 18.7%∗ 30.9%∗∗ 29.9%∗∗ −13.1%∗∗ −7.9%∗∗ 5.0%∗ 16.0%∗∗ Q4 0.8%∗ 2.3%∗ 19.1%∗ 77.6%∗∗ 7.4%∗ 8.1%∗ 20.4%∗ 63.9%∗∗ −6.5%∗∗ −5.8%∗∗ −1.2% 13.6%∗∗ UMDT Q1 33.2%∗∗ 17.6%∗ 24.9% 24.1% 46.3%∗∗ 22.8%∗ 16.2%∗ 14.6%∗ −13.0%∗∗ −5.2%∗ 8.7%∗∗ 9.5%∗∗ Q2 25.3% 24.6% 25.3% 24.5% 28.8%∗ 26.1% 23.1% 21.8%∗ −3.4% −1.4% 2.0% 2.7% Q3 13.3%∗ 23.8% 32.2%∗∗ 30.6%∗∗ 22.1% 22.2% 22.6% 32.8%∗∗ −8.7%∗∗ 1.5% 9.5%∗∗ −2.2% Q4 8.3%∗ 11.4%∗ 25.1% 55.0%∗∗ 14.9%∗ 13.3%∗ 19.8%∗ 51.8%∗∗ −6.6%∗∗ −1.9% 5.3%∗∗ 3.2% TESP500,T Q1 55.0%∗∗ 27.1% 11.4%∗ 6.3%∗ 42.0%∗∗ 38.7%∗∗ 15.5%∗ 3.6%∗ 13.0%∗∗ −11.5%∗∗ −4.1% 2.7%∗ Q2 13.1%∗ 35.0%∗∗ 27.3% 24.3% 19.0%∗ 39.4%∗∗ 25.0% 15.8%∗ −5.8%∗ −4.3% 1.7% 8.4%∗∗ Q3 2.1%∗ 19.7%∗ 33.4%∗∗ 44.5%∗∗ 8.8%∗ 24.8% 32.5%∗∗ 33.8%∗∗ −6.6%∗∗ −5.0%∗ 0.9% 10.6%∗∗ Q4 0.3%∗ 4.7%∗ 20.8%∗ 74.0%∗∗ 3.5%∗ 14.0%∗ 25.4% 56.9%∗∗ −3.1%∗∗ −9.3%∗∗ −4.5%∗ 17.0%∗∗ The table shows the transition probabilities between different measures of risk in T and T + 1 for funds with a performance in the highest quarter in T (first four columns), those with a performance in the lowest quarter in T (middle four columns), and the difference between these transition probabilities. XXX means that the target (column) variable is the same as in the appropriate row. This table gives the results for returns adjusted with the S&P 500. For the left and middle set of columns,∗ denotes a value statistically different from 0.25 on a 95% level, and ∗∗ on a 99% level. For the difference between transition probabilities in the right set of columns, the null hypothesis is 0.00, i.e., we test whether this difference is statistically different from 0. Standard errors have been computed by bootstrapping.33
  15. 15. 34 Table A2. Impact of Prior Performance on Choice of Risk Level (returns adjusted with the Carhart model) XXXT+1 RCarhart,T =4Q XXXT+1 RCarhart,T =1Q XXXT+1 Difference from to Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 STDT Q1 47.3%∗∗ 32.2%∗ 17.5%∗ 2.9%∗ 40.2%∗∗ 30.5%∗ 23.4% 5.8%∗ 7.0% 1.6% −5.8% −2.8%∗∗ Q2 23.6% 32.5%∗∗ 33.4%∗∗ 10.4%∗ 21.4% 36.3%∗∗ 30.7%∗ 11.4%∗ 2.1% −3.8% 2.6% −1.0% Q3 8.1%∗ 22.2% 38.4%∗∗ 31.1%∗∗ 7.0%∗ 20.5%∗ 38.5%∗∗ 33.8%∗∗ 1.0% 1.7% −0.1% −2.6% Q4 1.3%∗ 6.4%∗ 20.7%∗ 71.5%∗∗ 1.5%∗ 6.4%∗ 21.9%∗ 70.0%∗∗ 0.0% 0.0% −1.2% 1.4% MRPSP500,T Q1 42.3%∗∗ 26.8% 21.1%∗ 9.5%∗ 42.6%∗∗ 29.0%∗ 17.0%∗ 11.2%∗ −0.2% −2.1% 4.0%∗ −1.6% Q2 19.0%∗ 28.2% 31.3%∗∗ 21.3% 25.8% 31.9%∗∗ 23.6% 18.5%∗ −6.8%∗∗ −3.7% 7.6%∗∗ 2.8% Q3 12.0%∗ 20.5%∗ 32.9%∗∗ 34.5%∗∗ 15.9%∗ 28.0% 30.1%∗∗ 25.8% −3.9%∗ −7.5%∗∗ 2.7% 8.6%∗ Q4 4.8%∗ 12.3%∗ 23.4% 59.3%∗∗ 7.1%∗ 13.5%∗ 22.8% 56.4%∗∗ −2.3%∗∗ −1.2% 0.0% 2.9% HMLT Q1 50.9%∗∗ 24.1% 14.4%∗ 10.4%∗ 54.3%∗∗ 25.6% 12.0%∗ 7.9%∗ −3.4% −1.5% 2.4%∗ 2.5%∗ Q2 29.1%∗ 32.9%∗∗ 24.6% 13.2%∗ 35.0%∗∗ 25.8% 22.4%∗ 16.5%∗ −5.9%∗∗ 7.1%∗∗ 2.1% −3.3% Q3 16.4%∗ 25.1% 31.4%∗∗ 26.9% 21.0% 22.7% 30.9%∗∗ 25.1% −4.6%∗ 2.4% 0.4% 1.7% Q4 16.2%∗ 14.8%∗ 26.0% 42.8%∗∗ 16.1%∗ 15.1%∗ 25.5% 43.0%∗∗ 0.0% −0.3% 0.0% −0.1% SMBT Q1 55.9%∗∗ 26.4% 11.1%∗ 6.4%∗ 40.3%∗∗ 24.9% 21.5%∗ 13.1%∗ 15.5%∗∗ 1.4% −10.3%∗∗ −6.6%∗∗ Q2 37.3%∗∗ 29.9%∗∗ 19.3%∗ 13.3%∗ 29.9%∗∗ 30.3%∗∗ 22.5% 17.1%∗ 7.4%∗∗ −0.3% −3.2% −3.7%∗ Q3 10.6%∗ 14.8%∗ 34.2%∗∗ 40.2%∗∗ 14.6%∗ 14.8%∗ 34.4%∗∗ 36.0%∗∗ −3.9%∗ 0.0% −0.1% 4.1% Q4 4.1%∗ 6.0%∗ 23.4% 66.3%∗∗ 4.6%∗ 6.2%∗ 19.2%∗ 69.8%∗∗ −0.5% −0.2% 4.1%∗ −3.4%∗ UMDT Q1 46.4%∗∗ 22.4% 17.8%∗ 13.2%∗ 44.6%∗∗ 21.2%∗ 15.9%∗ 18.1%∗ 1.7% 1.2% 1.9% −4.9%∗∗ Q2 26.5% 35.0%∗∗ 22.4% 15.9%∗ 28.7% 26.9% 22.8% 21.5%∗ −2.0% 8.1%∗∗ −0.4% −5.5%∗∗ Q3 16.1%∗ 25.3% 32.9%∗∗ 25.5% 23.3% 24.7% 21.6% 30.2%∗ −7.2%∗∗ 0.6% 11.2%∗∗ −4.0% Q4 12.7%∗ 14.3%∗ 27.8%∗ 45.0%∗∗ 15.3%∗ 14.9%∗ 21.9%∗ 47.7%∗∗ −2.6%∗ −0.5% 5.9%∗∗ −2.6% TESP500,T Q1 67.1%∗∗ 24.0% 5.3%∗ 3.4%∗ 36.7%∗∗ 31.0%∗ 24.3% 7.9%∗ 30.4%∗∗ −6.9%∗∗ −18.9%∗∗ −4.4%∗∗ Q2 18.7%∗ 37.8%∗∗ 25.5% 17.8%∗ 15.0%∗ 34.2%∗∗ 28.8% 21.9% 3.6% 3.6% −3.2% −4.0% Q3 4.2%∗ 24.9% 31.9%∗∗ 38.7%∗∗ 6.1%∗ 19.0%∗ 34.6%∗∗ 40.2%∗∗ −1.8%∗ 5.9%∗ −2.6% −1.4% Q4 1.6%∗ 10.0%∗ 26.7% 61.5%∗∗ 1.8%∗ 9.9%∗ 25.1% 63.0%∗∗ −0.1% 0.0% 1.6% −1.5% This table gives the transition probabilities between different measures of risk in T and T + 1 for funds with a performance in the highest quarter in T (first four columns), those with a performance in the lowest quarter in T (middle four columns), and the difference between these transition probabilities. XXX means that the target (column) variable is the same as in the appropriate row. This table gives the results for returns adjusted with the Carhart model. For the left and middle set of columns, ∗ denotes a value statistically different from 0.25 on a 95% level, and ∗∗ on a 99% level. For the difference between transition probabilities in the right set of columns, the null hypothesis is 0.00, i.e., we test whether this difference is statistically different from 0. Standard errors have been computed by bootstrapping.