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Behavioral Portfolio Analysis of Individual Investors

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Arvid O. I. Hoffmann* Maastricht University and Netspar

Hersh Shefrin Santa Clara University Joost M. E. Pennings

Maastricht University, Wageningen University, and University of Illinois at Urbana-Champaign

Abstract: Existing studies on individual investors’ decision-making often rely on observable socio-demographic variables to proxy for underlying psychological processes that drive investment choices. Doing so implicitly ignores the latent heterogeneity amongst investors in terms of their preferences and beliefs that form the underlying drivers of their behavior. To gain a better understanding of the relations among individual investors’ decision-making, the processes leading to these decisions, and investment performance, this paper analyzes how systematic differences in investors’ investment objectives and strategies impact the portfolios they select and the returns they earn. Based on recent findings from behavioral finance we develop hypotheses which are tested using a combination of transaction and survey data involving a large sample of online brokerage clients. In line with our expectations, we find that investors driven by objectives related to speculation have higher aspirations and turnover, take more risk, judge themselves to be more advanced, and underperform relative to investors driven by the need to build a financial buffer or save for retirement. Somewhat to our surprise, we find that investors who rely on fundamental analysis have higher aspirations and turnover, take more risks, are more overconfident, and outperform investors who rely on technical analysis. Our findings provide support for the behavioral approach to portfolio theory and shed new light on the traditional approach to portfolio theory.

JEL Classification: G11, G24

Keywords: Behavioral Portfolio Theory, Investment Decisions, Investor Performance, Behavioral Finance

*

Corresponding author: Arvid O. I. Hoffmann, Maastricht University, School of Business and Economics, Department of Finance, P.O. Box 616, 6200 MD, The Netherlands. Tel.: +31 43 38 84 602. E-mail: a.hoffmann@maastrichtuniversity.nl.

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The authors thank Jeroen Derwall and Meir Statman for thoughtful comments and suggestions on previous versions of this paper. Any remaining errors are our own.

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I. Introduction

The combination of increased self-responsibility for retirement and an aging population has led a growing number of people to become accountable for their own financial futures. Considering the significant impact of current investment choices on future lifestyles (Browning and Crossley, 2001), it is important to understand how individual investors differ when it comes to the triangular relationship among the decisions they make, the processes leading to these decisions, and the resulting investment performance.

To date, our understanding of these relationships remains limited (Wilcox, 2003), as existing research either studies only part of this triangle (Nagy and Obenberger, 1994) or uses observable socio-demographic variables such as gender, age, or transaction channel to proxy for the underlying psychological processes that drive investors’ decision-making (Graham, Harvey, and Huang, 2009).2 In so doing, these studies implicitly assume that investors in the same age bracket, having the same gender, or using the same transaction channel are homogenous in their underlying psychological processes and the impact these have on their decision-making.

Recent literature on latent heterogeneity suggests that identifying the influence of unobservable variables such as investors’ preferences and beliefs is key to achieving a better understanding of financial market participants’ choices and behavior (Heckman, 2001; Pennings and Garcia, 2009). Unobservable, individual-level differences may help to explain the underlying mechanisms of a wide variety of behavioral anomalies (Dhar and Zhu, 2006; Graham et al.,

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A well-known finding is women’s outperformance of men, on a risk-adjusted basis, due to the accumulation of transaction costs by overconfident male investors who trade heavily (see e.g., Barber and Odean, 2000). Other important results are that older investors have better diversified portfolios and trade less aggressively than their younger counterparts (Dorn and Huberman, 2005; Goetzmann and Kumar, 2008), whereas investors switching from phone-based to online trading are found to trade more actively, more speculatively, and less profitably than before

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2009; Lee, Park, Lee, and Wyer, 2008), but to date they have not been widely used to explain individual investors’ decision-making or performance.

Our investigation into the role of individual differences focuses on the following questions: How do investors differ from each other in respect to the type of information upon which they rely to develop their strategies? How do investors differ from each other in respect to their general investing objectives and risk attitudes? To what extent do differences among investors impact the composition of their portfolios, trading activity, and investment performance?

To address these questions, we develop a dynamic behavioral theoretical framework and an empirical study. The theoretical framework is a behavioral extension of the traditional Euler equation approach and combines preferences, beliefs, and other variables that are typically unobservable such as investors’ ambition level and risk attitude to explain how investors make portfolio choices.3 As such, the framework reflects some of the essential features of behavioral portfolio theory (BPT) (Shefrin and Statman, 2000) and findings from studies on overconfidence (Barber and Odean, 2001; Kahneman and Lovallo, 1993). BPT emphasizes the role of behavioral preferences in portfolio selection and proposes that individual investors’ portfolio choices and consequently return performance reflect characteristics such as aspirations, hope, fear, and narrow framing. In this respect, BPT helps to explain why some investors simultaneously buy bonds and lottery tickets by investigating multiple objectives (e.g., protection from poverty at retirement and potential for a shot at riches) as well as aspirations (Statman, 2002). Studies on overconfidence emphasize the role of beliefs and help to explain why some investors are overly

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In the remainder of this paper, we refer to “observable” variables when discussing variables that can be constructed from secondary data, such as transaction records, and “unobservable” variables when discussing variables that as a general matter cannot be observed using secondary data, but require primary data collection, such as our investor survey. Thus, although technically the latter variables are not “unobservable” for our sample we continue to use this terminology throughout this paper for reasons of consistency.

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optimistic (Barber and Odean, 2001) and develop excessively bold forecasts (Kahneman and Lovallo, 1993).

Our empirical study combines individual investors’ survey responses with their trading records to create a unique dataset combining soft and hard data over an extended time period. The survey allows us to directly measure investor characteristics that typically remain unobservable, such as their objectives and strategies. Instead of using proxies based on, for example, demographics, we directly measure these aspects of investors’ underlying preferences and beliefs (Graham et al., 2009). Together with their trading records, this allows us to relate investors’ decision-making processes with their observed choices instead of inferring the first from the latter (cf. Manski, 2004). We empirically identify different segments of individual investors, label and profile these segments, and compare their return performance.

In doing so, we contribute to the literature in several ways. We (1) characterize some of the key ways in which individual investors differ from each other in terms of both preferences and beliefs, (2) develop a stylized dynamic behavioral portfolio selection model to explain how differences in preferences and beliefs lead to differences in investors’ portfolio decisions, (3) develop a series of hypotheses based on predictions stemming from the model, and (4) present a series of empirical findings, some of which serve to test our hypotheses, and some of which strike us as surprising and at odds with conventional wisdom. Our most striking result is that overtrading does not necessarily result in underperformance. Rather, underperformance depends on the circumstances. Investors with strong beliefs that stem from using fundamental analysis trade more frequently but stilloutperform investors using other strategies.

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A traditional model of dynamic portfolio choice (Merton, 1971; Viceira, 2001) involves an expected utility maximizing investor choosing, at each time t, consumption (ct) and securities (xt = xt,1 …, xt,J) given initial wealth (W), a stochastic stream of labor income (Lt), and stochastic

prices (qt). The standard Euler condition for this problem involves the purchase of a marginal

unit of security j at time t, and requires that the marginal benefit of this purchase be equal to the marginal cost. The marginal benefit is the expected marginal utility of consumption at time t+1

generated by the marginal investment in security j. The marginal cost is the foregone marginal utility of consumption at time t, as the increased expenditure on security j comes at the expense of less consumption at time t.

In the traditional model, the expected utility function has the form E(∑t

δ

t (u(ct)), where δ is

a subjective time preference discount factor, and the expectation is taken over a subjective probability belief (P) which an investor associates with the underlying stochastic process. The Euler condition has the following form:

qt,j

u/

ct = E(qt+1,j

δ

u/

ct+1) (1)

In words, purchasing an additional unit of security j at time t reduces consumption by qj,t units,

with each unit reduction of consumption resulting in the decline in utility at t of

u/

ct. At t+1,

the additional unit of security j will result in the ability to purchase qj,t+1 units of consumption,

whose consumption increases discounted utility by

δ

u/

ct+1. At the optimum, the foregone

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The traditional model postulates that investors’ subjective probability beliefs (P) are objectively correct, and implicitly assumes that markets are efficient.4 In this setting, the purpose of the portfolio is to manage the risk profile of the investor’s consumption stream, based on initial wealth (W) and stochastic labor income (L). This means that the portfolio serves to hedge uncertain labor income so as to smooth consumption over time. Unless labor income is highly volatile, most trading activity would only involve marginal adjustments to a diversified portfolio with the purpose of rebalancing or dealing with liquidity needs to finance consumption.

III. Behavioral Portfolio Analysis: Stylized Model

The behavioral approach to portfolio choice emphasizes additional motives for trading besides rebalancing and consumption-related liquidity. These motives are connected to a series of phenomena documented in the behavioral literature including:

• Probability weighting and reference point effects involving gains and losses, psychophysics, emotions, and aspirations (Kahneman and Tversky, 1979; Lopes, 1987)

• Mental accounting (Thaler, 1985; Thaler, 2000)

• Ambiguity aversion (Fox and Tversky, 1995; Heath and Tversky, 1991)

• Status quo bias (Mitchell, Mottola, Utkus, and Yamaguchi, 2006).

• The disposition effect (Shefrin and Statman, 1985)

• The attention hypothesis (Barber and Odean, 2008)

• Lack of diversification (Benartzi and Thaler, 2001; Goetzmann and Kumar, 2008)

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• Realization and evaluation utility (Barberis and Xiong, 2008)

• Insufficient saving due to a lack of self-control (Shefrin and Thaler 1988, Benartzi and Thaler 2007)

A. Behavioral Euler Equation

Consider a behavioral analogue to the traditional framework, which can capture the particular phenomena just described. We begin with a full optimization extension to (1), which we subsequently interpret in terms of a quasi-optimization analogue. Write the analogue of expected utility as an objective function U. Let U have as its arguments consumption stream c = [ct],

portfolio choices x = [xt], changes in portfolio positions y = [xt – xt-1], prices q = [qt], and

probability beliefs (P). The arguments c, x, and y are random variables, with c and x being the objects of choice. The inclusion of x, y, and q as arguments allows for an investor’s preferences to reflect not only consumption, but also the performance of his or her portfolio and the impact of gains and losses from trading.

In the neoclassical framework, investors with predictable streams of labor income make small but frequent adjustments to their portfolios, by weighing the costs of foregone marginal current consumption associated with a marginal security purchase against the expected marginal future consumption so generated. Notice that the criterion driving portfolio choice is consumption and savings.

In the corresponding behavioral framework, the consumption-savings feature is augmented by additional considerations. When a behavioral investor contemplates a marginal increase in his or her holdings xt,j of security j at time t, (s)he adds three additional components to the

neoclassical calculus. Those components take the form of

U/

xt,j,

U/

yt,j, and

U/

yt+1,j. The

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qt,j

U/

ct = t+1(qt+1,j

U/

ct+1) +

U/

xt +

U/

yt - t+1

U/

yt+1 (2)

The term ∑t+1 (qt+1,j∂U/∂ct+1) is the analogue to E(qt+1,jδ∂u/∂ct+1), with the summation ∑t+1 over

the support of outcomes at t+1. The term

U/

xt captures the effects of marginal evaluation

utility, meaning the psychological feelings the investor experiences from the value of his or her portfolio at different points in time. Here an investor’s sense of wellbeing at a given moment, apart from his or her consumption, is enhanced when his or her portfolio grows, and is diminished when it falls. The terms ∂U/

yt and ∂U/

yt+1 capture realization utility (Barberis and

Xiong, 2008), meaning the impact of trading a position. In this respect, an investor who sells at a gain might experience positive realization utility whereas an investor who sells at a loss might experience negative realization utility (cf. Thaler and Johnson, 1990).5 The minus sign associated with the term ∑t+1

U/

yt+1 in (2) reflects the fact that increasing xt reduces yt+1 = xt+1 – xt.

B. Preferences: Evaluation Utility and Realization Utility

In the stylized thought process that underlies condition (2), the investor’s decision regarding a security at time t balances the benefits from trading and holding a security against the associated costs. As in the neoclassical condition (1), increases in future consumption appear as benefits and decreases in current consumption appear as costs. As for the psychological benefits that appear on the right-hand-side of (2), consider the determinants of evaluation utility and realization utility.

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Evaluation utility reflects the emotional experience associated with holding a position in a security. Among the determinants of evaluation utility are three variables described in Shefrin and Statman (2000) and Shefrin (2008), namely SP, α, and P(A).

The variable SP, known as a security-potential function, is similar to expected utility. It is associated with gains and losses to the value of a position, and relates to feelings associated with thrill-seeking, presence or absence of anxiety, and value-expressive benefits derived from, for example, holding stocks of socially responsible firms (Statman, 2004).

In contrast to an expected utility function, SP features rank-dependent weights in place of probabilities. Rank-dependent weights reflect particular emotions, such as fear and hope.6 Investors who are overly fearful act as if they overweight unfavorable events relative to more favorable events. Notably, although the probability weight attached to an event does not vary with portfolio decisions, the decision weight assigned to an outcome can vary with the position an investor takes in a security. In particular, when holding a long position in security j, a fearful investor will tend to overweight the probability that the return is negative. However, should the same investor instead hold a short position in security j, s(he) would overweight the probability that the return is positive.

The variable α refers to an aspiration level. For example, the investor’s aspiration might be that the portfolio s(he) selected at t-1 be worth at least αt at time t. Correspondingly, P(At) is the

probability the investor assigns to meeting that goal. Investors who set both high aspirations and high probabilities of achieving those aspirations are said to be ambitious. A key feature of the portfolio selection framework developed by Shefrin and Statman (2000) is that ambitious investors take on high risk.

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Psychological-based decision theories tend to use an inverse-S shaped weighting function for distribution functions (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992). This corresponds to a U-shaped weighting function for density functions in which probabilities of extreme events are exaggerated. .

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The realization utility terms on the right-hand-side of (2) embody the impacts of pride or regret directly associated with the act of trading. Examples are the feeling of pride associated with selling at a gain or the feeling of regret associated with selling at a loss.

C. Beliefs: Biases, Framing and Probability Weighting

The behavioral approach emphasizes the importance of both preferences and beliefs. The discussion in the previous paragraphs has emphasized preferences. In shifting the emphasis from preferences to beliefs, we identify three key issues. First, investors typically have erroneous beliefs stemming from behavioral biases. Examples of biases are excessive optimism and overconfidence. Excessive optimism can lead investors to overestimate expected returns (De Bondt and Thaler, 1985), whereas overconfidence can lead them to underestimate risk (Barber and Odean, 2000; Odean, 1998). In conjunction, this can lead to forecasts which are too bold (cf. Kahneman and Lovallo, 1993). Moreover, most individual investors have only the vaguest notion of how security returns are jointly distributed (Benartzi and Thaler, 2001).

Second, because of framing effects, behavioral investors ignore information relating to return covariance. This is the key reason underlying the violation of stochastic dominance in prospect theoretic choice experiments (Kahneman and Tversky, 1979). Therefore, the beliefs used in connection with (2) across securities might not be compatible with a single set of beliefs

P.7 Instead, we follow the approach of prospect theory with narrow framing and assume that investors’ beliefs consist of marginal distributions for each security, which are applied to (2) on a

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In the behavioral model, investors seek to achieve equality (2) for each security, thereby balancing the marginal benefits and marginal costs of increasing the amount held of each security j. However, computing the values of

∂U/∂ct, ∂U/∂ct+1, ∂U/∂xt,j, ∂U/∂yt,j, and ∂U/∂yt+1,j is a tall order requiring full knowledge of the joint distribution of all security returns. For this reason, we postulate that investors use a heuristic approach to estimate marginal benefits

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security by security basis.8 In particular, investors are assumed to ignore covariances when choosing their portfolios.

Third, preferences and beliefs interact through probability weighting. In this regard, decision weights are applied to subjective probabilities. However, both preferences and beliefs also combine to impact at least two other psychological phenomena, aversion to ambiguity and status quo bias, a topic to which we now turn.

D. Ambiguity Aversion and Status Quo Bias

Ambiguity aversion reflects discomfort stemming from a lack of knowledge of the underlying probabilities (Ellsberg, 1961). For example, knowing that an urn contains 100 balls, of which 50 are red and 50 are black is different from knowing that an urn contains 100 balls whose color is either black or red, but with no knowledge of the fraction of each. Status quo bias involves the tendency to preserve the status quo instead of to make a change from the status quo.

Both aversion to ambiguity and status quo bias play key roles in the portfolio issues we analyze. Aversion to ambiguity can lead investors to hold relatively few securities, leading for example to x=0 for most securities. Status quo bias involves an underlying reluctance to trade (Samuelson and Zeckhauser, 1988), leading for example to y=0 holding much of the time.9

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Prospect theory takes as its starting point expected utility theory and replaces the utility function with a value function, probabilities with probability weights, and a single complex optimization with a collection of simpler optimizations. Notably, the value function and probability weighting function are consistent across the simpler optimizations.

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An example that is often used to explain the relation between status quo bias and regret avoidance is the following. Suppose you own stock worth $1,000 in Company A and can exchange it for $1,000 of stock in Company B. Given your investment assessment, you choose to hold your current shares. Your neighbor holds $1,000 in Company B and, for reasons similar to yours, decides to switch his shares for $1,000 of Company A. During the next six months, the value of each person’s stock in Company A falls to $700. Which one feels the greater regret? According to the psychology literature, the answer is that your neighbor will experience more regret because he took an action that produced an unfavorable consequence, and can easily imagine having done otherwise. In contrast, you took no action, and so it is more difficult for you to imagine acting differently.

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Notably, solutions featuring zero holdings (x=0) or zero trading (y=0) are instances of corner solutions. Shefrin (2008) points out that behavioral preference maps involving rank-dependent weighting feature many kinks, and these in turn give rise to corner solutions where marginal conditions like (2) fails to hold with equality. Aversion to ambiguity is manifest in a strong fear response, the fear that a highly unfavorable event will occur if the investor takes a decision with limited knowledge of the underlying probabilities. Such fear can lead an investor to view any non-zero position in a particular security as unattractive, be that position long or short. Formally, this would entail

U/

xt < 0 for xt > 0 and ∂U/

xt > 0 for xt < 0 with a point of

non-differentiability (kink) at xt = 0. Similar remarks apply to status quo bias (in respect to ∂U/

yt).

Status quo bias does not mean that investors refrain from trading altogether, only that other forces must be strong enough to counter the bias. Aversion to ambiguity and status quo bias induce investors to hold a few securities rather than many, and to trade intermittently rather than continuously. Certainly, if at some point in time, the needs for hedging, rebalancing, and liquidity are sufficiently strong, investors will overcome status quo bias and trade. Likewise, investors can overcome status quo bias if they have enough confidence in their stock picking skills to feel little potential for regret (Kahneman, Knetsch, and Thaler, 1991), derive sufficiently high evaluation utility from their portfolios, or have bold enough forecasts (cf. Kahneman and Lovallo, 1993).

Bold forecasts stem from conviction, a combination of familiarity, strong opinions, and confidence, just the opposite of ambiguity. Consider some of the findings in the psychology literature about the influence of information on decision makers’ degree of conviction. An often cited study of horse race handicappers by Slovic and Corrigan (1973) analyzes how confidence and accuracy change as functions of the amount of racing sheet information. Accuracy increases

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with the amount of information, until a point of information overload is reached, after which it slightly declines (Oskamp, 1965). However, confidence increases steadily with the amount of information (Locander and Hermann, 1979). Hahn et al. (1992) confirmed that decision quality is an outcome of both time pressure and information load.

The findings in Heath and Tversky (1991) on the determinants of ambiguity aversion provide further insight into the drivers of conviction. They show that ambiguity aversion is reduced by a sense of familiarity and expertise. Fox and Tversky (1995) establish that the degree of ambiguity aversion in a particular choice increases when decision makers contrast the choice with a situation in which he or she has more knowledge, or someone else has more knowledge.

E. Setting the Stage for Hypotheses Development

To set the stage for the development of our hypotheses, we recapitulate some of the interpretive features in the stylized behavioral Euler approach embodied within condition (2). In respect to preferences, consider (2) to be a dynamic extension of the mental accounting version of BPT in Shefrin and Statman (2000). Here securities are evaluated relative to goals defined by aspiration levels and success probabilities, with each mental account and associated aspiration level corresponding to a different goal (cf. Das et al., 2010). Examples of the types of goals we consider in the remainder of the paper relate to capital growth, retirement saving, hobby, and speculation (cf. Lewellen, Lease, and Schlarbaum, 1980). In this paper, we refer to these types of goals as “objectives.”

As in BPT, condition (2) pertains to two points in time. However, unlike BPT, (2) contains terms pertaining to realization utility, which impacts trading behavior. In addition, the relative strength of the different terms in (2) is assumed to reflect the general nature of different types of

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goals. For example, we think it reasonable to assume that realization utility is stronger for investors whose primary objective is thrill seeking than investors whose primary objective in investing involves savings for retirement.

In respect to beliefs, we interpret the model as if investors are quasi-rational, relying on subjective marginal distributions rather than on joint return distributions. These distributions are all implicitly conditional. To this end, our paper focuses on the source of the conditioning. Examples include the media (financial news), past prior prices (technical analysis), and financial variables (fundamental analysis) (cf. Lease, Lewellen, and Schlarbaum, 1974). In this paper, we refer to these types of information sources as “strategies.”

As a system, (2) is more akin to a consumer choice model than a mean-variance portfolio model. In this respect, securities are selected and held for their attributes, and their contribution to satisfying needs (cf. Wilcox, 2003). Just as each consumer purchases only a small subset of available products, so do behavioral investors hold only a small subset of available securities, at least directly. The determinants of which securities are held at any time reflect the interaction among ambiguity aversion, status quo bias, and boldness of beliefs, as in the “bold forecasts, timid choices” framework of Kahneman and Lovallo (1993). In addition, the quasi-rational feature of (2) might involve investors holding different types of securities, not because they value diversification, but because they have a taste for variety. Although variety might mimic diversification, investors ignore covariance information in (2), and so do not value diversification per se.

In the next section we develop a series of hypotheses, based on the behavioral Euler condition (2) and some related assumptions. Our first major assumption is that (2) features the “bold forecasts, timid choices” property, in which forecasts need to be sufficiently bold to

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overcome status quo bias. Our second major assumption is that investors implement (2) in a manner similar to making consumer choices, meaning that they do not value diversification per se, although they might have a taste for variety in their security holdings. Our additional assumptions pertain to the manner in which evaluation utility, realization utility, and aspiration variables vary across investor objectives, and confidence and accuracy vary across investor strategy. We develop these additional assumptions in the next section, where we use condition (2) to describe how variation in boldness across investor strategies, and variation in aspirations across investor objectives predicts variation in trading patterns and associated returns.

IV. Hypotheses

Overconfidence pertains to beliefs, and status quo bias pertains to preferences. The behavioral approach emphasizes the psychological features associated with both preferences and beliefs. In this paper, we focus on the role of investment objectives as a reflection of investor preferences, and the role of investment strategy as a reflection of investor beliefs. In this section, we develop hypotheses about the impact of both strategies and objectives.

Our hypotheses relate to individual differences across the spectrum of investors. In this regard, overconfidence leads some investors to trade too much, while status quo bias leads other investors to trade too little (Goetzmann and Kumar, 2008; Rantapuska, 2006). Overconfidence leads investors’ forecasts to be excessively bold, while status quo bias leads to timid choices and inaction (Kahneman and Lovallo, 1993). As discussed below, in our framework, both features can operate simultaneously with the result that investors trade only intermittently, when their beliefs are sufficiently bold to outweigh status quo bias.

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Status quo bias is strong. Mitchell et al. (2006) provide evidence that 80% of participants in 401(k) accounts initiate no trades in a two-year period, and an additional 11% make only one trade. Therefore, few investors in their sample rebalance. Similarly, Ameriks and Zeldes (2004) find that 50% of the investors in their sample do not rebalance over a nine year period. In a related vein, Choi et al. (2008) find that 80% of investors in 401(k) plans maintain the plan’s default savings contribution and investment option.

Investors who trade rarely if ever lie at one end of the spectrum. At the other end of the spectrum lie investors who trade on a daily basis. Barber et al. (2009) report that 17% of traders in Taiwan are day traders. For most day traders, overconfidence is strong. In the main, our hypotheses deal with investors lying in the middle of the spectrum, where status quo bias and overconfidence operate in tension.

Active trading stems from conviction. An overconfident investor with sufficiently high conviction in his or her stock picking skills will tend to overcome status quo bias and engage in frequent trading (cf. Kahneman and Lovallo, 1993). In respect to beliefs, our hypotheses for active traders focus on the nature of the information upon which investors rely, and the degree to which that information generates conviction. We suggest that variation in investors’ trading activity will be influenced by the nature of the information upon which their trading strategies depend. If investors possess information that generates high conviction, the resulting overconfidence leads to bolder forecasts. Bolder forecasts are able to overcome investors’ status quo bias that would otherwise cause timid choices and inaction. This relationship is a key feature of the hypotheses we develop below, especially in respect to trading strategies.

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First, compare investors who rely on fundamental analysis as a strategy with those who rely on technical analysis. Investors using fundamental analysis examine all underlying conditions relevant for future stock price developments. Besides financial statements, these include economic, demographic, and geopolitical factors. In contrast, investors relying on technical analysis only study the stock price movements themselves, believing that historical data provides indicators for future stock price developments.

To us, this suggests that fundamental analysis typically involves more information than technical analysis (cf. Shleifer and Summers, 1990). Investors relying on fundamental analysis are therefore more likely to become more familiar with the firms they follow than investors relying on technical analysis. After all, fundamental analysis serves to focus primary attention on details pertaining to the firms themselves, whereas technical analysis focuses attention on price patterns generated by firms’ stocks. This focus on firm fundamentals instead of the kind of pattern recognition tasks inherent in technical analysis leads us to conclude that familiarity bias will tend to be stronger by investors relying on fundamental analysis than investors relying on technical analysis.

In the language of Kahneman and Lovallo (1993), investors who rely on fundamental analysis are more inclined to adapt an “inside view” (Kahneman and Lovallo, 1993) and become overconfident than those who rely on technical analysis, as confidence is an increasing function of the amount of information (Locander and Hermann, 1979). We hypothesize that as a result their forecasts become bolder and they more easily overcome status quo bias, leading to less timid choices. Thus, based on condition (2) we expect investors who rely on fundamental analysis to trade more frequently than those who rely on technical analysis, ceteris paribus. In short:

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H1: Relative to investors relying on technical analysis, investors relying on fundamental analysis will form bolder beliefs, and their greater overconfidence will induce them to trade more frequently.

Apart from a very small segment of highly skilled investors who hold concentrated portfolios (Barber, Lee, Liu, and Odean, 2009; Goetzmann and Kumar, 2008), overtrading typically leads to underperformance due to the accumulation of transaction costs (Barber and Odean, 2000). As there is no a priori reason to expect that investors using fundamental analysis are more skilled than investors using other strategies, we expect:

H2: Relative to investors relying on technical analysis, investors relying on fundamental analysis will earn lower risk and style adjusted returns.

Second, compare investors who rely on fundamental analysis with those relying on their intuition. In the behavioral framework, investors do not place high intrinsic value on diversification. In the spirit of prospect theory’s isolation effect (mental accounting, narrow framing) (Kahneman and Tversky, 1979), investors act as if they implement condition (2) on a security-by-security basis, rather than as part of an integrated optimization.10 As a result, status

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Prospect theory is a boundedly rational theory of choice involving maximization of a weighted value function. The maximization does not typically correspond to a full optimization, as complex decision tasks are often simplified into smaller subtasks with important information about the subtasks being omitted. In this respect, the value function used to make decisions corresponds to a “proxy” of the decision maker’s utility function. Decision makers rely on proxies because they lack the ability required to compute utility. The use of proxies featuring omissions can result in suboptimal choice, of which a notable example is the selection of stochastically dominated risks. A key feature of prospect theory is that the value function and weighting function are common across decision tasks. In the present analysis, think of equation (2) featuring a proxy for the expected utility terms, in which the omitted information involves the contribution to utility from securities other than . If we follow the prospect theory

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quo bias will typically lead to underdiversification. Ceteris paribus, (2) implies that investors holding more securities will tend to be those with stronger convictions in their stock picking skills and in possession of better and more information which leads them to make bolder forecasts (Kahneman and Lovallo, 1993). Only in these cases, will investors be able to overcome status quo bias and be willing to invest in multiple stocks and thus make less timid choices.

As discussed previously, it is likely that the latter features correlate with reliance on fundamental analysis. As such, investors who rely on fundamental analysis will tend to hold a larger number of different stocks in their portfolios than other investors.11 Conversely, investors who only rely on intuition, and therefore less information, will tend to have less conviction regarding their stock picking skills for most securities and their status quo bias leads them to make timid choices. As a result, these investors may be biased towards a small(er) number of stocks with which they are familiar (Huberman, 2001). Goetzmann and Kumar (2008) point out that as investors increase the number of stocks in their portfolios, they tend to choose stocks which co-move, thereby depriving themselves of the benefits of diversification. Moreover, to avoid feelings of regret (Kahneman et al., 1991) investors relying on intuition will exhibit a strong status quo bias and hold fewer securities in their portfolios. In short:

H3: Investors relying on fundamental analysis will hold a larger number of different stocks in their portfolio than investors relying on intuition.

B. Investment Objectives

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The common proxy assumption implies that at the origin, the decision to trade is determined by whether or not the net benefit is sufficient to overcome the obstacle imposed by the kink, with the latter being common across securities.

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Investment objectives are imbedded in investors’ preferences. Aspiration levels constitute an important component of objectives. A key implication of behavioral portfolio theory is that investors whose goals involve high aspirations act as if they have a high tolerance for risk, implying that investors who set high aspiration levels in combination with an associated high probability of achieving those levels, will tend to choose risky portfolios (Shefrin and Statman, 2000). Risky portfolios are portfolios that are more exposed to market risk and overweight small firms (Barber and Odean, 2001). Hence we hypothesize:

H4: Investors with higher aspiration levels have higher risk profiles than investors with lower aspiration levels.

H5: Investors with higher risk profiles will hold riskier portfolios (i.e. with higher exposure to the market and small-firm factors) than investors with lower risk profiles.

As previously discussed, because of familiarity bias, investors who rely on fundamental analysis are likely to have high conviction in their stock picking skills. In addition to leading them to make bold forecasts, we suggest that familiarity also leads them to be more ambitious than investor whose beliefs feature more ambiguity. This is because ambiguity involves uncertainty about P(A), the probability of achieving the aspiration level. In turn, ambiguity aversion induces pessimism about P(A), which results in less risk taking. This leads to the following hypothesis:

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The behavioral framework links investments objectives to trading behavior. In this regard, investors saving for retirement or building a financial buffer and investors who invest to speculate or exercise a hobby lie at opposite ends of a continuum. For investors who mainly invest as a hobby or to speculate, the second and third term in (2) loom large, as these relate to the benefits from (anticipated) evaluation utility (Barberis and Xiong, 2008) and thrill seeking (Grinblatt and Keloharju, 2006). To experience these positive emotions such investors will trade more frequently than other investors. Hence we hypothesize:

H7: Investors who invest primarily as a hobby or to speculate will trade more frequently than investors whose primary investment objective is to build a financial buffer or save for retirement.

Additionally, investors who mainly invest as a hobby or to speculate might have very high conviction, make bold forecasts, tolerate risk, and set ambitious targets. Indeed, recent literature shows that investors who trade to entertain themselves (Dorn and Sengmueller, 2009) or to speculate – essentially seeing stocks as a lottery ticket providing a shot at riches (Statman, 2002) – have higher aspirations and take more risk relative to investors who do not associate investing with gambling (Kumar, 2009). In contrast, investors whose primary investment objective is to build a financial buffer or save for retirement are likely to have lower aspirations and choose more conservative portfolios. In short:

H8: Investors whose primary investment objective is to build a financial buffer or save for retirement have lower aspirations and take less risk than investors who invest primarily as a hobby or to speculate.

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V. Data and Methods

The analyses in this paper draw on transaction records of all clients and questionnaire data obtained for a sample of clients of the largest online broker in The Netherlands. Due to trading restrictions, we exclude accounts owned by minors (age <18 years). We also exclude accounts with a beginning-of-the-month value of less than €250 and accounts owned by professional traders to ensure we deal with active accounts owned by individual investors. Imposing these restrictions leaves 65,325 individual accounts with over 9 million trades from January 2000 until March 2006.

A. Brokerage Records

Opening positions as well as complete transaction records are available for all prospective participants of the survey, regardless whether they choose to participate or not, allowing us to control for sample selection bias. The typical record consists of an identification number, an account number, transaction time and date, buy/sell indicator, type of asset traded, gross transaction value, and transaction commissions.

B. Survey Sampling and Selection

In 2006, we designed and performed an online survey amongst all clients of the online broker. In total, 6,565 clients completed the questionnaire. To prevent biased responses, the purpose of the survey was framed in a neutral way and no reference to the objective of the study at hand was made. In the call to participate, respondents were requested to “provide their opinion of the online broker”. Brokerage clients who participated in the survey could win a personal computer

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that was raffled amongst respondents who fully completed the questionnaire. Amongst other questions, the questionnaire probed for investors’ preferences as reflected in their investment objective, beliefs as reflected in their investment strategies, aspiration level as reflected by their ambition level, risk-taking behavior as reflected in the risk profile of their current investment portfolio, and their sophistication as reflected in their self-categorization into novice, advanced, or very advanced investor classes. Figure 1 provides an overview of the questions we used.

After matching transaction records with questionnaire data, a sample of 5,500 clients and corresponding accounts remain for which both hard (transaction) and soft (survey) data are available and which have an account history of at least 36 months.

[Figure 1 about here]

C. Descriptive Statistics

In Table 1 (Panel A) we report descriptive statistics for the respondents to the investor survey. We also report these descriptives for the non-respondents to test for selection bias (Panel B).

Of the sample of 5,500 investors for which both accounting and survey data is available, 58% is male and the mean age is about 50 years. The mean (median) number of total trades over the sample period is 76.45 (30.00). Average (median) monthly turnover is about 42% (11%). The average (median) portfolio value is €45,915 (€15,234). Combining the average portfolio value with a total portfolio value of €50,000-€60,000 for the average Dutch investor (Bauer, Cosemans, and Eichholtz, 2009) indicates that our average client invests more than three-fourth of his or her total investment portfolio at this particular online broker, showing that we do not investigate investors’ “play accounts” (Goetzmann and Kumar, 2008) but deal with

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representative and serious investor accounts. In fact, 40.8% of our survey respondents only hold an investment account at this particular broker. Of the respondents who do also hold an investment account at another broker, 51.6% indicate that this comprises less than half of their total portfolio. As a robustness check, we compare the results of investors who only invest through this particular broker with those who also have another brokerage account but find no significant differences regarding our hypotheses. Following Seru, Shumway, and Stoffman (2008) we measure experience by the number of months an investor has been trading since account opening. The results in Table 1 show that the mean (median) experience is about 40.21 (39.00) months. As compared to recent findings by Odean and Barber (2000) and Goetzmann and Kumar (2008) our investors’ portfolios are better diversified, although still far from well-diversified. The mean (median) number of stocks held by our investors is 6.57 (4.00) while the mean (median) Herfindahl-Hirschmann Index (HHI) is 27.78% (21.14%). Comparing the HHI with the normalized HHI (HHI*) indicates that investors’ portfolio weights are not uniformly distributed. Rather, investors distribute their overall portfolio value unevenly over different assets. Mean (median) monthly returns over the period 2000-2006 are -0.30% (0.30%). On average, the respondents to the investor survey are relatively risk-seeking, with a mean (median) score of 5.31 (6.00) (1=very defensive, 7=very speculative).

A comparison between the respondents to the investor survey (Panel A) with non-respondents (Panel B) shows that relative to non-non-respondents, the non-respondents feature more females, are older, transact more frequently, have higher portfolio values, are more experienced, better diversified (hold a larger number of different stocks and have a lower HHI), obtain a better monthly return performance, and take more risk (all p<0.00). Although the differences between survey respondents and non-respondents are relatively small, they suggest that the sample of

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clients which completed the investor survey tend to be relatively sophisticated investors with a sizeable portfolio which adds to the relevance and importance of the study at hand.

[Table 1 about here]

D. Measuring Investor Performance

Investor performance is defined as the monthly change in market value of all securities in an investor’s account (Bauer et al., 2009). End-of-the-month account value is net of transaction costs the investor incurred during the month. As performance is measured on a monthly basis, assumptions have to be made considering the timing of deposits and withdrawals of cash and securities. To be conservative, we assume that deposits are made at the start of each month and withdrawals take place at the end of each month. Analyses with the assumption that deposits and withdrawals are made halfway during the month yield similar results. Hence, we calculate net performance as ) ( ) ( 1 1 it it it it it net it D V NDW V V R + − − = − − , (3)

where Vit is the account value at the end of month t, NDWitis the net of deposits and withdrawals

during month t, and Dit are the deposits made during month t.

Gross performance is obtained by adding back transaction costs incurred during month t,

TCit, to end-of-the-month account value,

) ( ) ( 1 1 it it it it it it gross it D V TC NDW V V R + + − − = − − . (4)

Only direct transaction costs (commissions) are considered. We do not add back any indirect transactions costs (market impact and bid-ask spreads). The trades of most individual investors are relatively small, making market impact costs unlikely. Moreover, Keim and Madhavan

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(1998) show that bid-ask spreads may be imprecise estimates of the true spread, as trades are often executed within the quoted spread.

E. Attributing Investor Performance

To obtain investors’ abnormal performance, we attribute the returns on investor portfolios to different risk and style factors using the Carhart (1997) four-factor model. This model adjusts investor returns for exposure to market (RMRF), size (SMB), book-to-market (HML), and momentum (UMD) factors. Following Bauer et al. (2009), we construct these factors for the Dutch market, as our sample of investors mainly invests in Dutch securities.12 The market return in the RMRF factor is represented by the return on the MSCI Netherlands equity index. All factor-mimicking portfolios are constructed according to the procedure by Kenneth French.13

The following time series model is estimated to obtain risk and style adjusted returns:

= + + = K k it kt ik i it F R 1 ε β α . (5)

In this model Rit represents the excess return on investor i’s portfolio, βik is the loading of

portfolio i on factor k, and Fkt is the month t excess return on the k’th factor-mimicking portfolio.

The intercept αimeasures abnormal performance relative to the risk and style factors. The factor

loadings indicate whether a portfolio is tilted towards market risk or a particular investment style.

F. Segmenting Investors

12

In terms of volume (value) 95% (85%) of all trades are transactions in Dutch securities. This suggests the presence of a home bias among Dutch investors, which has previously been documented by French and Poterba (1991) for the US, UK, and Japan and by Karlsson and Norden (2007) for Sweden. Hence, we find that Dutch versions of the factor-mimicking portfolios lead to a better model fit than do international factors.

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We group the 5,500 investors for which we obtained both hard transaction and soft survey data into groups based on their preferences or beliefs. Investment objectives pertain to preferences, whereas strategies pertain to beliefs.

While the investors in our sample typically have only one investment objective (e.g., saving for retirement, building a financial buffer, speculate, exercise a hobby), they combine different strategies to attain this objective (e.g., combining financial news with intuition or professional advice).14 Therefore, we use univariate sorting to distinguish different segments of investors based on investment objective (cf. Kumar, Page, and Spalt, 2009), but have to use cluster analysis to discern segments based on investment strategy (Hair, Anderson, Tatham, and Black, 1998).

The univariate sorting results indicate five segments of investors based on their dominant investment objective. These segments are labeled Capital Growth, Hobby, Saving for

Retirement, Speculation, and Building Financial Buffer.

To distinguish segments of investors based on investment strategy, we use a non-hierarchical cluster analysis following Hair, Anderson, Tatham and Black (1998).15 The cluster analysis groups together investors with similar scores on certain (combinations of) strategies. In particular, differences between segments in terms of scoring are maximized and within segments minimized (Punj and Stewart, 1983). This procedure leads to six segments, which are labeled

14

In its original specification, behavioral portfolio theory (Shefrin and Statman, 2000) is a static framework that describes an investors’ portfolio as consisting of multiple layers, each layer corresponding to a particular investment goal or objective. In this paper, we incorporate elements of behavioral portfolio theory into a dynamic framework to develop hypotheses about investors’ trading behavior on an ongoing basis, focusing on investors’ most important investment objective.

15

Nonhierarchical clustering procedures are less susceptible to outliers in comparison to hierarchical clustering procedures. In addition, unlike hierarchical clustering, K-means clustering as used in this study is able to analyze large data sets as this procedure does not require prior computation of a proximity matrix of the distance/similarity of every case with every other case (Punj and Stewart, 1983).

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Financial News, Financial News and Intuition, Intuition, Technical Analysis, Fundamental

Analysis, and Financial News, Intuition, and Professional Advice.

Table 2 reports descriptive statistics for these segments in regard to a number of observable variables, while Table 3 does the same for the unobservable variables. Observable variables are variables which can be constructed from the secondary data (transaction records) as obtained from the online brokerage firm. Unobservable variables cannot be constructed using secondary data, but require primary data as obtained by our investor survey.

[Tables 2 and 3 about here]

VI. Profiling Investor Segments

This section profiles the different segments of investors as obtained previously using a combination of observable and unobservable variables.

A. Segments based on Investment Objectives

Table 2 shows that male investors are especially well represented in the segments Hobby (0.62) and Speculation (0.64). The latter segments also contain the youngest (47.31 and 48.61 years, respectively) investors, whereas those in the segment Speculation also trade most heavily during the sample period (99.25 times). Monthly turnover is highest in the segment Speculation

(78.87%) and lowest in the segment Saving for Retirement (26.44%). Investors in the segment

Capital Growth have the largest portfolio value (€62,646) while Hobby investors have the

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experienced (43.49 months) and best diversified both in terms of number of stocks and the HHI (holding 7.57 different stocks and having a HHI of 24.39%) while investors in the segment

Speculation are least experienced (34.47 months) and less diversified (5.63 different stocks, HHI

is 30.38%).16 The profiles of the segments Speculation and Hobby thus obtained, containing younger male investors who overtrade and underdiversify, are in line with recent findings on speculative trading as gambling (Kumar, 2009) or entertainment (Dorn and Sengmueller, 2009).

Table 3 demonstrates that the segment Speculation has the highest score on ambition level (3.52), the most speculative risk profile (5.80), reports to have the lowest percentage of novice investors (24.13%), and the highest percentage of advanced (59.74%) and very advanced (15.48%) investors, respectively. Together with the high turnover and dominance of males in this segment, these findings confirm and enrich earlier work that finds that especially male investors are subject to overconfidence and trade excessively (Barber and Odean, 2001). Additionally, these findings confirm the prediction by Statman (2002) that investors who perceive investing as playing the lottery may have particularly high aspiration levels and be subject to overconfidence. Not surprisingly, we find that investors in the segment Saving for Retirement have lower ambition levels (3.26), a less speculative risk profile (4.98) and are more modest about their level of sophistication (only 7.64% of this group of investors judge themselves to be very advanced).

B. Segments based on Investment Strategy

Table 2 shows that the fraction of males is highest (0.64) in the segment Fundamental Analysis

and lowest in the segments Financial News and Financial News, Intuition, and Professional

Advice (both 0.55). The number of trades during the sample period is highest for investors in the

segment Fundamental Analysis (106.09 times) and lowest in the segment Intuition (59.80 times).

16

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The previous combination of gender and turnover is consistent with earlier work by Barber and Odean (2001) who find that relative to women, men are overconfident and trade heavily. The combination of using fundamental analysis and excessive trading is in line with our expectations that especially investors who feel they have more complete information are likely to make bold forecasts and overcome their status quo bias, leading to less timid choices in terms of transaction frequency. The average age is highest (51.05) in the segment Financial News, Intuition, and Professional Advice and lowest (48.40) in the segment Intuition. Monthly turnover is highest (46.63%) in the segment Financial News and Intuition and lowest (36.20%) in the segment

Technical Analysis. The segment Fundamental Analysis has the highest portfolio value (€72,509)

while the segment Intuition has the lowest portfolio value (€31,379). Investors in the segment

Financial News are most experienced (41.93 months) while those in the segment Technical

Analysis are least experienced (37.34 months). We also find interesting differences between

segments with regard to portfolio diversification. The segment Fundamental Analysis is best diversified (8.05 different stocks, HHI is 25.68%), while the segment Intuition has the worst diversification (5.68 different stocks, HHI is 30.56%). These investors may have less conviction in their capabilities as they have less complete information, resulting in forecasts that are more conservative and not sufficiently bold to overcome their status quo bias, leading to timid choices (cf. Kahneman and Lovallo, 1993).

Table 3 demonstrates that investors in the segment Fundamental Analysis have the highest ambition level (3.43), while investors in other segments, such as Intuition (3.09) and Financial News (3.10) have more modest ambitions. In line with the previous results, investors in the segment Fundamental Analysis have the most speculative risk profile (5.52), whereas investors in the segment Financial News have the least speculative risk profile (5.09). Finally, whereas the

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segments Fundamental Analysis (16.53%) and Technical Analysis (10.13%) have the highest percentage of investors who regard themselves to be very advanced, these numbers are considerably lower in the other segments, reaching a minimum in the segment Financial News

(3.95%). The lower score of the latter category of investors indicates that they may be less likely to be overconfident about their own abilities. Instead of trying to make an independent estimate of a company’s attractiveness using, for example, fundamental or technical analysis, they rely on widely available financial news to make their investments.

VII. Performance per Investor Segment

In this section we compare the raw returns and alphas of the different segments of investors as previously identified. We expect important differences between segments in terms of performance due to the previously identified differences with respect to observable (e.g., turnover, age, transaction frequency, and portfolio diversification) as well as unobservable variables (ambition level, risk profile, sophistication) and the predictions of the behavioral portfolio framework. Table 4 reports the investment performance per investor segment.

[Table 4 about here]

A. Segments based on Investment Objectives

Panel A of Table 4 shows that the segment Speculation has the worst raw return (gross), while the segment Capital Growth does best. The average investor in the segment Speculation loses 0.38% per month in gross terms, whereas the average investor in the segment Capital Growth

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The right hand side of Panel A shows that the performance difference between the different segments of investors widens when transaction costs are taken into account. The return of the segment Speculation incurs the most transaction costs, which is intuitive considering this segment’s high turnover. The raw net return of this segment is now -2.22% per month, whereas the performance of the segment Capital Growth is still positive with 0.22% per month.

After also adjusting for both risk and style tilts, the segment Capital Growth still achieves the best performance with a net alpha of -0.40%, whereas the segment Speculation remains the worst performer with a net alpha of -1.28%. The latter result is in line with the observable and unobservable characteristics of the investors in this segment. Investors whose objective is to speculate have high ambition levels, high risk profiles, high turnover, and judge themselves to be very advanced. These characteristics are typical for overconfident investors who overtrade and consequently underperform (Barber and Odean 2001). In addition, the factor loadings show that these investors are heavily investing in small cap stocks, which may be a risky strategy in combination with the lower levels of diversification we find for this segment.

B. Segments based on Investment Strategy

Panel B of Table 4 shows that the segment Technical Analysis has the worst raw return (gross), while the segment Financial News and Intuition does best, closely followed by Fundamental

Analysis. The average investor in the segment Technical Analysis gains only 0.07% per month in

gross terms, whereas the average investor in the segment Financial Analysis and Intuition gains 0.86% and Fundamental Analysis 0.76% per month, respectively.

The right hand side of Panel A shows that when transaction costs are taken into account the segment Technical Analysis remains the worst performer and the segment Financial News and

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Intuition stays the best. The raw net return of the segment Technical Analysis becomes negative at -0.92% per month, while the performance of the segment Financial News and Intuition stays mildly positive at 0.13%.

This pattern also remains the same after adjusting for risk and style tilts, although the difference between segments now narrows. The segment Financial News and Intuition

achieves the best performance with a net alpha of -0.46%, closely followed by the segment

Fundamental Analysis, which obtains a net alpha of -0.47%. The segments Technical Analysis

and Financial News, Intuition, and Professional Advice are the worst performers, having a net alpha of -0.73% and -0.71% per month, respectively. The superior performance of the segments

Financial News and Intuition and Fundamental Analysis is interesting and suggests some

stock-picking skills.17 After all, these investors’ stock choices must be good enough to overcome the detrimental effect of the relatively high level of transactions of these segments. The inferior performance of the segment Financial News, Intuition, and Professional Advice is remarkable and suggests that the advice of investment professionals may not be very helpful for the performance of individual investors, but is associated with a relatively high number of transactions and turnover. Finally, the inferior performance of the segment Technical Analysis

illustrates the limited usefulness of past stock market information for future return performance.

VIII. Testing of Hypotheses

This section reports the results of testing the hypotheses of the behavioral portfolio framework as presented in section IV. To determine whether investment objectives and strategies result in significant differences between investors regarding their investment behavior and return

17

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performance we employ a series of t-tests and ANOVA’s (Hair et al., 1998). Detailed results are provided in Tables 2-4.

As predicted by H1, investors relying on fundamental analysis are more overconfident than those relying on technical analysis as reflected by the higher proportion of fundamental traders who report to be either “advanced” (t(1584) = 5.64, p < 0.00) or “very advanced” (t(1584) = 3.78, p < 0.00) and the substantially larger proportion of technical traders who report to be “novice investors” (t(1584) = 9.05, p < 0.00). Additionally, as predicted, trading frequency is higher (t(1584) = 3.54, p < 0.00) for the more overconfident fundamental traders than for the less overconfident technical traders.

Surprisingly, we have to reject H2, as despite their frequent trading, the risk and style adjusted return performance of fundamental traders is actually higher than those of technical traders (t(1584) = 2.06, p = 0.04). These results show that overtrading does not necessarily result in underperformance (cf. Barber and Odean, 2000). Rather, underperformance depends on the circumstances. In this case, we distinguish between traders relying on fundamental versus technical analysis. We find that although fundamental investors trade more, they may not be “overconfident” in the traditional sense, as their high level of confidence is actually warranted by a detailed insight in the underlying economic fundamentals and their frequent trading leads to higher returns even after accounting for transaction costs. These investors may learn by trading, leading to superior returns (cf. Glaser and Weber, 2007; Nicolosi, Peng, and Zhu, 2009).

We accept H3: Investors relying on fundamental analysis are better diversified than investors relying solely on their intuition as represented by the larger number of different stocks that are held by the former group (t(1486) = 6.07, p < 0.00) and their lower HHI score (t(1420) = 3.83, p < 0.00). This result is in line with the discussion above, as relative to other investors,

References

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