Empirical Behavioral Finance

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and survey responses of 5,500 retail investors, they find that investors who use technical analysis ... documented disposition effect among their subjects.
Empirical Behavioral Finance

Doron Kliger Department of Economics, University of Haifa Mount Carmel, Haifa, 31905, Israel; [email protected]

Martijn J. van den Assem Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute P.O. Box 1738, 3000 DR, Rotterdam, the Netherlands; [email protected]

Remco C.J. Zwinkels Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute P.O. Box 1738, 3000 DR, Rotterdam, the Netherlands; [email protected]

Journal of Economic Behavior & Organization, November 2014 (107, Part B), 421-427

JEL: A31, G02 Keywords: behavioral finance, special issue, diversification heuristic, 1/n rule

We thank all authors for submitting their work for inclusion in the special issue, and the many reviewers who provided us and the authors with invaluable support and advice. We are grateful to William Neilson for giving us the opportunity to organize the special issue, and for his support throughout the entire process.

1.

Introduction

For several decades now, behavioral finance has been integrating insights from the broad social science perspective into research in financial economics. A large body of literature has evolved since the pioneering work of, for example, Shiller (1981), De Bondt and Thaler (1985), Shefrin and Statman (1985), and Roll (1986). In a nutshell, it has brought realism and descriptive power to a field that was originally built on perfectly rational agents and capital market efficiency. Much of the progress has been summarized in excellent books and papers. For a comprehensive overview we recommend Barberis and Thaler (2003), Subrahmanyam (2008), Shefrin (2009), and Baker and Nofsinger (2010). In addition, there are a number of excellent reviews of behavioral research that specifically focus at asset pricing (Hirshleifer, 2001; Shefrin, 2008), decisions of individual investors and households (Campbell, 2006; Barber and Odean, 2013), or corporate finance (Shefrin 2006; Baker and Wurgler, 2013). In September 2012, the Journal of Economic Behavior & Organization (JEBO) published a call for papers for a special issue on empirical research in behavioral finance. Along with enriching our understanding of human financial decision making and market dynamics, the interaction with other fields of study has also paved the way for the use of alternative data and methods in financial research. To further stimulate this development, it was stated in the call for papers that preference would be given to empirical work that uses innovative experiments, field data, qualitative and quantitative surveys, and other types of data that are not available in standard databases. The call generated an overwhelming number of 163 submissions. This enormous interest underlines the significance of the field and the many different research opportunities that it continues to offer. In what follows, we first briefly introduce the 25 selected contributions published in the special issue. Next, we report on a questionnaire that we distributed among researchers who submitted a paper or were asked to review a paper. The questionnaire serves two purposes. First, it gives the current view of the behavioral finance research community about the relative importance of different types of research within the theme of empirical behavioral finance. Second, it tests whether behavioral finance experts display one of the many intriguing choice patterns that are being studied by the profession. With respect to the first purpose, the main results are that (i) respondents attach roughly equal importance to the use of real and constructed data, (ii) there is a strong preference for further research on individuals over research on groups and organizations, and (iii) research on preferences receives more weight than research on beliefs, and both preferences and beliefs are deemed more important than limits to arbitrage. Regarding the second purpose of the questionnaire, we find [2]

strong evidence for the bias that follows from naïve reliance on the diversification heuristic, or the more extreme 1/n rule (Read and Loewenstein, 1995; Benartzi and Thaler, 2001).

2.

Contributions in this special issue

The first article in the special issue is De Neve and Fowler (2014). The recent progress in DNA analysis has created fascinating opportunities to examine the links between genetic variations and particular behaviors. In their unique large-scale study conducted with approximately 12,000 individuals, De Neve and Fowler find a link between credit card usage and the monoamine oxidase A (MAOA) gene. Next, Feltovich and Ejebu (2014) experimentally investigate whether people save less when they observe others’ consumption. They find evidence of undersaving in the treatment that permits interpersonal comparisons, and conclude that the effect is driven by subjects who are trying to “keep up with the Joneses”. Gathergood and Weber (2014) use UK survey data to shed light on the co-holding puzzle, the observation that many people simultaneously hold high-cost consumer debt and lowyield liquid assets. They conclude that it is lack of self-control and not financial illiteracy that drives this costly behavior. Collins and Urban (2014) report on a natural experiment that shows how seemingly irrelevant changes in the decision environment can affect the behavior of firms. They investigate the effect of a state-level policy that merely changed the status quo for U.S. mortgage loan servicing organizations from “wait and see” to “taking action”, and find that it changed the firms’ behavior and led to more loan modification and filings of foreclosures. The next three papers study individual investor data. Hoffmann and Shefrin (2014) ask how technical analysis impacts investors’ portfolio selection and performance. On the basis of transaction records and survey responses of 5,500 retail investors, they find that investors who use technical analysis make poor portfolio decisions that cost them 50 basis points per month in terms of raw returns. Gherzi et al. (2014) investigate investors’ portfolio monitoring decisions. In contrast to earlier work that pointed out that investors’ behavior is reminiscent of the myth of ostriches, because of their tendency to “stick their heads in the sand” in response to news of negative market movement, they find that investors increase their portfolio monitoring not just after upswings but also after downswings. Heimer (2014) sheds further light on the phenomenon of over-trading among individual investors. Using a U.S. nationwide household survey with data on respondents’ investments and their social expenditures, he uncovers evidence for a positive relation between social interaction and active portfolio management.

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Frydman and Rangel (2014) use a laboratory experiment to investigate whether it is possible to debias investors’ propensity to sell securities with capital gains and hold on to securities with capital losses. They find that reducing the salience of purchase prices successfully reduces this welldocumented disposition effect among their subjects. Roth and Voskort (2014) examine how accurately financial advisors can gauge the risk preferences of their advisees. Their artefactual field experiment with financial professionals and student-subjects points out that advisors do not solely rely on targets’ self-assessments, but also on stereotyping and their own risk preference. Hytönen et al. (2014) employ functional magnetic resonance imaging (fMRI) to explore the origins of path dependence in risky choice. They find that the increased risk appetite after gains and losses is related to increased activity of affective brain processes and decreased activity of deliberation-related brain processes. Hochman, Shahar and Ariely (2014) explore how prepayment affects financial decisions in a riskless context. Among other things, they show that people tend to overvalue prepaid money. Their experiments inform firms on how they can use prepayment mechanisms to get more out of their agents and how payment structures can be used to help individuals better manage their finances. Vogel et al. (2014) study whether task-oriented and relation-oriented dimensions of diversity in an entrepreneurial team affect funding decisions of external financiers. They conclude that both dimensions have an impact on the funding decision, implying a trade-off between the social costs of diversity and access to external funding. The next set of papers study belief formation using different types of data. Choi and Hui (2014) focus on in-play soccer betting markets to study whether underreaction and overreaction to news are related to the degree of surprise. Their results indicate that underreaction decreases with surprise, and that overreaction occurs when events are extremely surprising. Jacobsen et al. (2014) use a set of quantitative surveys to study differences in optimism between men and women. They find that men are more optimistic than women, and show that this difference explains systematic gender differences in asset allocation. Egan, Merkle, and Weber (2014) use a panel survey to study individual investors’ beliefs about return expectations of others. The results show that second-order expectations are inaccurate and affected by several psychological biases, and that second-order expectations influence investment decisions. Goldbaum and Zwinkels (2014) use a quantitative survey to analyze how investors in a foreign exchange market forecast exchange rates. They find evidence that panelists switch between a fundamental and a technical model, and that switching behavior depends on the forecast horizon, investor-specific characteristics, and period-specific characteristics. Markiewicz and Pick (2014) test whether adaptive learning models are capable of replicating survey expectations of professional forecasters for a range of macroeconomic and financial variables. Among other things, they find that adaptive learning models describe survey

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expectations better than typical time-series models do, and that the adaptive learning model with a constant gain parameter performs especially well. Sentiment is a central concept in behavioral finance, but it is notoriously difficult to define and measure empirically. Kim and Kim (2014) use internet message postings to quantify investors’ sentiment regarding specific companies. They show that sentiment is affected by prior returns, but find no evidence that it has predictive power for future returns, volatility, or volume. Siganos, Vagenas-Nanos and Verwijmeren (2014) use Facebook’s Gross National Happiness Index (FGNHI) to measure daily sentiment at the country level. They document a positive contemporaneous relation between sentiment and returns, followed by mean reversion over the following weeks. Billett, Jiang and Rego (2014) use survey data to investigate how stock prices depend on perceptions of brand prestige and familiarity. Their results point out that stocks of companies with more prestigious brands have higher valuation ratios and negative loadings on the Fama-French HML factor. Pantzalis and Park (2014) study whether stock prices are affected by sentiment that is generated by sports results. They find a positive contemporaneous relation between results from sports matches close to firms’ headquarters and the stock returns of these firms, followed by reversion to the mean. The final set of papers report on experimental asset markets. Lugovskyy et al. (2014) study the effect of asset-holding caps on the formation of bubbles. They find that permanent caps reduce positive bubbles but tend to create negative ones. With temporary caps, neither positive nor negative bubbles emerge. Huber, Kirchler and Stefan (2014) look into the effect of a skewed distribution of the fundamental value on market prices. Their results show that positive skewness initially causes overpricing and negative skewness initially causes underpricing, and that these mispricings disappear with learning. Füllbrunn, Rau and Weitzel (2014) examine the conditions for ambiguity effects in markets. They conduct various asset market experiments, and find that ambiguity effects occur in low-feedback call markets for assets that provoke high ambiguity aversion. The work of FellnerRöhling and Krügel (2014) concludes the special issue. They investigate the link between overconfidence and trading activity. In line with earlier studies, they find no relation between miscalibration measures and trading activity, but when they employ their novel measure of misperception of signal reliability they do find a relation in one of the treatments.

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3.

Questionnaire

The exciting collection of articles in this special issue demonstrates that the field of behavioral finance is still progressing. The amount and variety of available data and methods are growing at a rapid pace. In light of the abundant opportunities for new research, we have distributed a questionnaire to sketch where the field should be heading according to the behavioral finance research community. In addition to this primary motivation for the questionnaire, we also had another purpose in mind. We designed it such that it allowed us to find out whether behavioral finance researchers – experts who are supposedly well-informed about the roles of heuristics and biases in judgment and decision making – rely on the diversification heuristic when they express their views about the future of the profession.1 Each questionnaire contained six questions. The first three questions were the same for all respondents, and asked for their gender, age, and academic position. Next, three main questions invited respondents to express their views on how the proportion of journal space in the theme of empirical behavioral finance should be allocated.2 We distinguished between allocations across different types of data (real vs. constructed data; henceforth, question 1), decision makers (individuals vs. groups and organizations; question 2), and fundamental topics (preferences vs. beliefs vs. limits to arbitrage; question 3). With each question, the weights assigned to the different components of the “research portfolio” had to add up to 100 percent. There were two versions of the questionnaire. Half of the potential respondents received the version where each question listed two or three possible portfolio components. The other half received the exact same questions, but in their version one of the portfolio components – the one we call the grouped component – was further partitioned into finer components. Consequently, each question in this alternative version contained a total of four to six possible components. In the former (henceforth coarse) treatment, all answer categories were rather broad (e.g., individuals and groups and organizations), whereas in the latter (henceforth fine) treatment the grouped component was partitioned into subcomponents (e.g., individuals was partitioned into consumers, investors, 1

In the same spirit, Gächter et al. (2007) examined whether experimental economists are sensitive to gain-loss framing. Their experiment used a conference acceptance email, in which the benefits of early registration were framed as either collecting a discount or preventing a penalty. They find that junior faculty have a significantly higher propensity of registering early in the penalty frame than in the discount frame, while no effect was detected for senior faculty. 2 We did not seek to establish a guideline for journal editors, as they are, obviously, dependent on the number and quality of papers submitted to their journals. The desired allocation of journal space is merely our measure of the perceived relative importance of different categories of research.

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managers, and advisors). To prevent the possibility that respondents in the coarse treatment would hold narrower definitions of the grouped components than respondents in the fine treatment, we added the subcomponents between parentheses.3 After the review process of all submissions for the special issue was completed, we issued invitation emails to take part in the questionnaire to 748 researchers with a known or findable email address who had submitted or had been asked to review a paper for the special issue. The emails informed the potential respondents about the progress we were making with the special issue, and provided a link to the questionnaire. Each potential respondent was randomly allocated to one of the two treatments. Apart from the link to one of the two questionnaire versions, both groups received the exact same email. The questionnaire versions are presented in the appendix. If respondents are not sensitive to framing, the weights given to the grouped components should be the same in both treatments. For each question, the sum of the weights given to the subcomponents in the fine treatment should be equal to the single weight given to the component encompassing them in the coarse treatment. If, however, our respondents apply the diversification heuristic to express their views about the future of their profession, then the weight given to the grouped component in the coarse treatment (e.g., individuals) will be lower than the sum of the weights given to the subcomponents in the fine treatment (consumers, investors, managers, and advisors). Table 1 details the numbers of respondents in the two treatments, broken down by some basic personal characteristics. A total of 239 researchers completed the questionnaire. Most respondents are male (77%), between 30 and 50 years of age (74%), and tenured (64%). The two groups are roughly comparable in terms of size and composition. Table 2 shows the mean and median replies for each treatment. Overall, respondents assign roughly the same weight to constructed and real data. In the coarse treatment, they express a slight preference for real data (55%) over constructed data (44%), whereas in the fine treatment they give a higher weight to the grouped subcategories of constructed data (57%). Across the subcategories, there is a clear preference for experiments over surveys and questionnaires. The responses to the second question indicate that research on individuals is considered more important than research on groups and organizations. Especially in the fine treatment, the total weight given to research on the financial decisions of individuals (86%) is substantially higher than

3

The lists of components that we used are subjective and probably not complete. The possibility of incomplete partitioning of the grouped component, however, strengthens our results as it would work in the opposite direction of the treatment effect that we observe in our data.

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Table 1: Numbers of respondents. The table shows the number of respondents by treatment (coarse, fine, and combined) and by respondents’ characteristics (gender, age bracket, and academic position). Coarse treatment

Fine treatment

Total

Female

30

24

54

Male

92

93

185

Age < 30

14

6

20

30 ≤ Age < 40

46

56

102

40 ≤ Age < 50

42

34

76

50 ≤ Age

20

21

41

Student

8

6

14

Non-tenured faculty

40

32

72

Tenured faculty

74

79

153

Total

122

117

239

the weight given to groups and organizations (14%). Within the grouped component of the fine treatment, respondents assign the highest priority to investors, followed by managers, consumers, and advisors, respectively. When we question respondents about the relative importance of fundamental topics in behavioral finance, they give most weight to research on preferences, followed by research on beliefs. Investigation into the limits to arbitrage is deemed the least important. The strength of this finding, however, differs substantially between the two treatments. In the coarse treatment, preferences and beliefs are believed to be roughly equally important (weights of 40% and 37%, respectively). In the fine treatment, the total weight assigned to the subcategories of preferences is approximately three times as high as the weight assigned to beliefs (64% and 21%, respectively). The distribution of weights over the preferences subcategories is strikingly uniform, with roughly 15% assigned to each subcategory. For the second purpose of our questionnaire  to see if respondents apply the diversification heuristic  we focus on the differences between the feedback in the two treatments. The 1/n rule, if strictly implemented by a respondent, would result in allocating identical relative journal spaces to all listed components. In the coarse treatment, as many as 51 (42%), 36 (30%) and 13 (11%) respondents have indeed allocated identical relative journal space to each of the components

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Table 2: Results. The table shows the mean and median replies by treatment and by question. For each question, the treatment means and the treatment medians for the grouped components are statistically compared in the last two columns. Differences are calculated as the average reply in the fine treatment minus the average reply in the coarse treatment. The p-values for the comparisons of means are from t-tests, the p-values for the comparisons of medians are from Wilcoxon-Mann-Whitney tests. Coarse treatment

mean

median

Fine treatment

mean

median

(a) questionnaires

12.71

10

mean diff. (p-value)

median diff. (p-value)

12.71 (0.00%)

10 (0.00%)

25.71 (0.00%)

30 (0.00%)

23.90 (0.00%)

25 (0.00%)

Question 1 (data)

(b) surveys

16.36

15

(c) experiments

28.21

30

(a) constructed data

44.57

50

grouped

57.28

60

(b) real data

55.43

50

(d) real data

42.72

40

(a) consumers

22.85

20

Question 2 (decision makers)

(b) investors

28.75

30

(c) managers

21.02

20

(d) advisors

13.79

10

(a) individuals

60.70

60

grouped

86.41

90

(b) groups and organizations

39.30

40

(e) groups and organizations

13.59

10

Question 3 (fundamental topics) (a) loss aversion

16.85

17

(b) reference points

16.44

15

(c) ambiguity aversion

15.59

15

(d) probability weighting

14.91

15

(a) preferences

39.89

40

grouped

63.79

65

(b) beliefs

36.75

40

(e) beliefs

20.60

20

(c) limits to arbitrage

23.36

20

(f) limits to arbitrage

15.62

15

presented in question 1, 2 and 3, respectively. For the fine treatment, the corresponding figures are 9 (8%), 20 (17%) and 3 (3%).4 In line with a more general tendency to diversify across choice options, the mean and median allocations for the three different grouped components are consistently higher in the fine treatment than in the coarse treatment. The mean weights differ by 13 (question 1), 26 (question 2) and 24 percentage points (question 3). Each of these differences is significant at the one percent level. Comparisons of the medians yield the same picture.

4

For each question, we counted the cases where a respondent’s allocations did not differ by more than one percentage point from each other.

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Table 3: Results grouped components per respondent category. For separate subsets of respondents, the table shows the mean and median replies for the grouped components by treatment and by question. The different subsets group respondents on the basis of their gender, age bracket, and academic position, respectively. Differences are calculated as the average reply in the fine treatment minus the average reply in the coarse treatment. The p-values for the comparisons of means are from t-tests, the p-values for the comparisons of medians are from Wilcoxon-Mann-Whitney tests. Coarse treatment mean

median

Fine treatment mean

median

mean diff. (p-value)

median diff. (p-value)

Question 1 (data), constructed data component Female

50.23

50

64.38

70

14.14 (0.15%)

20 (0.16%)

Male

42.73

50

55.45

60

12.72 (0.00%)

10 (0.00%)

Age < 30

44.64

40

60.83

65

16.19 (5.47%)

25 (7.62%)

30 ≤ Age < 40

41.96

50

56.46

60

14.51 (0.01%)

10 (0.01%)

40 ≤ Age < 50

48.29

50

58.09

60

9.80 (1.22%)

10 (0.43%)

50 ≤ Age

42.75

50

57.14

60

14.39 (1.53%)

10 (1.89%)

Student

38.75

40

50.00

50

11.25 (17.36%)

10 (19.67%)

Non-tenured faculty

43.25

50

55.78

60

12.53 (0.43%)

10 (0.13%)

Tenured faculty

45.92

50

58.44

60

12.52 (0.00%)

10 (0.00%)

Question 2 (decision makers), individuals component Female

63.90

70

85.83

82.5

21.93 (0.00%)

12.5 (0.00%)

Male

59.65

60

86.56

90

26.91 (0.00%)

30 (0.00%)

Age < 30

64.00

63

80.83

80

16.83 (1.91%)

17 (1.50%)

30 ≤ Age < 40

55.48

50

87.55

90

32.08 (0.00%)

40 (0.00%)

40 ≤ Age < 50

62.90

68.5

86.91

90

24.01 (0.00%)

21.5 (0.00%)

50 ≤ Age

65.75

70

84.14

85

18.39 (0.00%)

15 (0.00%)

Student

55.00

50

86.67

85

31.67 (0.01%)

35 (0.55%)

Non-tenured faculty

59.33

60

86.97

90

27.64 (0.00%)

30 (0.00%)

Tenured faculty

62.05

66

86.16

90

24.11 (0.00%)

24 (0.00%)

Question 3 (fundamental topics), preferences component Female

40.33

40

64.33

67

24.00 (0.00%)

27 (0.00%)

Male

39.74

40

63.65

65

23.91 (0.00%)

25 (0.00%)

Age < 30

40.64

40

64.17

67.5

23.52 (0.29%)

27.5 (0.57%)

30 ≤ Age < 40

38.24

40

64.34

67.5

26.10 (0.00%)

27.5 (0.00%)

40 ≤ Age < 50

41.40

40

64.56

65

23.15 (0.00%)

25 (0.00%)

50 ≤ Age

39.95

40

60.95

65

21.00 (0.00%)

25 (0.00%)

Student

33.75

40

59.83

69.5

26.08 (0.46%)

29.5 (2.39%)

Non-tenured faculty

39.65

40

62.16

65

22.51 (0.00%)

25 (0.00%)

Tenured faculty

40.68

40

64.75

65

24.07 (0.00%)

25 (0.00%)

Table 3 shows the comparisons for subsets of the data, partitioning by gender, age, and academic position. Evidently, naïve diversification is a widespread behavioral phenomenon. Virtually all comparisons yield statistically significant differences; the insignificant results pertain to the comparisons of small groups of respondents (Age < 30 and Position = Student). Multivariate regression analyses for the three different questions with the grouped components’ weights as the

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dependent variables and a treatment dummy variable and respondent characteristics as regressors corroborate the results (not tabulated), regardless of the precise model specifications. Interestingly, these findings suggest that the evidence for naïve reliance on the diversification rule previously documented in the literature is not limited to lay people and laboratory subjects. Even among experts in our field, when asked their opinion on where the profession should be heading, there is a tendency to diversify. Learning may not be as fast as critics of behavioral finance sometimes assume.

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Appendix: the questionnaire Marking added here (not provided to respondents): […] = coarse treatment only {…} = fine treatment only * = grouped component Gender:

Male

Female

Age:

Age