Behavioral Finance

Behavioral finance as a subdiscipline of behavioral economics is finance incorporating findings from psychology and sociology into its theories. Behavioral finance models are usually developed to explain investor behavior or market anomalies when rational models provide no sufficient explanations. To understand the research agenda, methodology, and contributions, this survey reviews traditional finance theory first. Then, this survey shows how modifications (e.g. incorporating market frictions) can rationally explain observed individual or market behavior. In the second section, the survey will explain the behavioral finance research methodology -how biases are modeled, incorporated into traditional finance theories, and tested empirically and experimentally- using one specific subset of the behavioral finance literature, the overconfidence literature.

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.Overconfidence; Differences of opinion; Trading volume; Individual investors; Investor behavior; Correlation of economic and psychological variables; Combination of psychometric measures of judgment biases and field data

Overconfidence and Trading Volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors (number of trades, turnover). Approximately 3000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. The results hold even when we control for several other determinants of trading volume in a cross-sectional regression analysis. In connection with other recent findings, we conclude that the usual way of motivating and modelling overconfidence which is mainly based on the calibration literature has to be treated with caution. We argue that our findings present a psychological foundation for the ``differences of opinion'' explanation of high levels of trading volume. In addition, our way of empirically evaluating behavioral finance models - the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data - seems to be a promising way to better understand which psychological phenomena drive economic behavior.

Which Past Returns Affect Trading Volume?

Anecdotal evidence and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this prediction using linear panel regressions as well as negative binomial panel regressions and Logit panel regressions. We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the probability to trade stocks in a given month) and are thus able to confirm predictions of overconfidence models. However, contrary to intuition, the effect of market returns on subsequent trading volume is stronger for the whole group of investors. Using survey data of our investor sample, we present evidence that individual investors, on average, are unable to give a correct estimate of their own past realized stock portfolio performance. The correlation between return estimates and past realized returns is insignificant. For the subgroup of respondents, we are able to analyze the link between the ability to correctly estimate the past realized stock portfolio performance on the one hand and the dependence of trading volume on past returns on the other hand. We find that for the subgroup of investors that is better able to estimate the own past realized stock portfolio performance, the effect of past portfolio returns on trading volume is stronger. We argue that this finding might explain our results concerning the relation between past returns and subsequent trading volume.Individual investors; Investor behavior; Trading volume; Stock returns and Trading Volume; Overconfidence; Discount broker; Online broker; Online banks; Panel data; Count data

Online broker investors : demographic information, investment strategy, portfolio positions, and trading activity

It is often argued that the internet influences investor behavior. Furthermore, the recent 'bubble' in internet stocks is sometimes ascribed, at least in part, to online trading. However, little is known about how online investors actually behave. This paper contributes to fill this gap. A sample of approximately 3,000 online broker investors is studied over a 51 month period ending in April 2001. The main goal of this paper is to present various descriptive statistics on demographic information, investment strategy, portfolio positions, and trading activity. The main results of this paper can be summarized as follows. Online broker investors trade frequently. The median stock portfolio turnover is about 30 % per month. The average number of stocks in portfolios increases over time suggesting that, ceteris paribus, diversification increases. Trading activity is tilted towards technology, software, and internet stocks. About half of the investors in our sample trade warrants and half of the transactions of all investors are purchases and sales of foreign stocks. Income and age are negatively and the stock portfolio value is positively related to the number of stock transactions. Warrant traders buy and sell significantly more stocks than investors who do not trade warrants. Warrant traders and investors who describe their investment strategy as high risk have higher stock portfolio turnover values whereas the opposite is true for investors who use their online account mainly for retirement savings. The stock portfolio value is negatively related to turnover. The higher the stock portfolio value, the higher the average trading volume per stock market transaction

Behavioral Financial Engineering : eine Fallstudie zum Rationalen Entscheiden

The design of financial products, such as options or reverse convertibles, is guided by considerations that are all within the standard finance world where investors maximize expected utility and care about cash flows, but are indifferent among frames of cash flows. This case study describes the role of behavioral elements in the design of some financial products. The elements are prospect theory, non-linear probability weighting, framing effects, and mental accounting

Overconfidence of Professionals and Lay Men: Individual Differences Within and Between Tasks?

Overconfidence can manifest itself in various forms. For example, people think that their knowledge is more precise than it really is (miscalibration) and they believe that their abilities are above average (better than average effect). The questions whether judgment biases are related or whether stable individual differences in the degree of overconfidence exist, have long been unexplored. In this paper, we present two studies that analyze whether professional traders or investment bankers who work for international banks are prone to judgment biases to the same degree as a population of lay men. We examine whether there are robust individual differences in the degree of overconfidence within various tasks. Furthermore, we analyze whether the degree of judgment biases is correlated across tasks. Based on the answers of 123 professionals, we find that expert judgment is biased. In most tasks, their degrees of overconfidence are significantly higher than the respective scores of a student control group. In line with the literature, we find stable individual differences within tasks (e.g. in the degree of miscalibration). However, we find that correlations across distinct tasks are sometimes insignificant or even negative. We conclude that some manifestations of overconfidence, that are often argued to be related, are actually unrelated.

Individual Investor Sentiment and Stock Returns - What Do We Learn from Warrant Traders?

In this paper, we propose a measure of individual investor sentiment that is derived from the market for bank-issued warrants. Due to a unique warrant transaction data set from a large discount broker we are able to calculate a daily sentiment measure and test whether individual investor sentiment is related to daily stock returns by using vector autoregressive models and Granger causality tests. We find that there exists a mutual influence of sentiment and stock market returns, but only in the very short-run (one and two trading days). Returns have a negative influence on sentiment, while the influence of sentiment on returns is positive for the next trading day. The influence of stock market returns on sentiment is stronger than vice versa. Our sentiment measure simultaneously avoids problems that are associated with existing sentiment measures, which are based on the closed-end fund discount, stock market transactions, the put-call ratio or investor surveys.

Behavioral Financial Engineering: eine Fallstudie zum Rationalen Entscheiden

The design of financial products, such as options or reverse convertibles, is guided by considerations that are all within the standard finance world where investors maximize expected utility and care about cash flows, but are indifferent among frames of cash flows. This case study describes the role of behavioral elements in the design of some financial products. The elements are prospect theory, non-linear probability weighting, framing effects, and mental accounting.

Online Broker Investors: Demographic Information, Investment Strategy, Portfolio Positions, and Trading Activity

It is often argued that the internet influences investor behavior. Furthermore, the recent 'bubble' in internet stocks is sometimes ascribed, at least in part, to online trading. However, little is known about how online investors actually behave. This paper contributes to fill this gap. A sample of approximately 3,000 online broker investors is studied over a 51 month period ending in April 2001. The main goal of this paper is to present various descriptive statistics on demographic information, investment strategy, portfolio positions, and trading activity. The main results of this paper can be summarized as follows. Online broker investors trade frequently. The median stock portfolio turnover is about 30 % per month. The average number of stocks in portfolios increases over time suggesting that, ceteris paribus, diversification increases. Trading activity is tilted towards technology, software, and internet stocks. About half of the investors in our sample trade warrants and half of the transactions of all investors are purchases and sales of foreign stocks. Income and age are negatively and the stock portfolio value is positively related to the number of stock transactions. Warrant traders buy and sell significantly more stocks than investors who do not trade warrants. Warrant traders and investors who describe their investment strategy as high risk have higher stock portfolio turnover values whereas the opposite is true for investors who use their online account mainly for retirement savings. The stock portfolio value is negatively related to turnover. The higher the stock portfolio value, the higher the average trading volume per stock market transaction.