Working Paper: CEPR ID: DP3904
Authors: Markus Glaser; Thomas Langer; Martin Weber
Abstract: Empirical research documents that temporary trends in stock price movements exist. Moreover, riding a trend can be a profitable investment strategy. Thus, the ability to recognize trends in stock markets influences the quality of investment decisions. In this Paper, we provide a thorough test of the trend recognition and forecasting ability of financial professionals who work in the trading room of a large bank and novices (MBA students). In an experimental study, we analyse two ways of trend prediction: probability estimates and confidence intervals. Subjects observe stock price charts, which are artificially generated by either a process with positive or negative trend and are asked to provide subjective probability estimates for the trend. In addition, the subjects were asked to state confidence intervals for the development of the chart in the future. We find that depending on the type of task either underconfidence (in probability estimates) or overconfidence (in confidence intervals) can be observed in the same trend prediction setting based on the same information. Underconfidence in probability estimates is more pronounced the longer the price history observed by subjects and the higher the discriminability of the price path generating processes. Furthermore, we find that the degree of overconfidence in both tasks is significantly positively correlated for all experimental subjects whereas performance measures are not. Our study has important implications for financial modelling. We argue that the question which psychological bias should be incorporated into a model does not depend on a specific informational setting but solely on the specific task considered. This Paper demonstrates that a theorist has to be careful when deriving assumptions about the behaviour of agents in financial markets from psychological findings.
Keywords: Conservatism; Financial Modelling; Forecasting; Overconfidence; Professionals; Trend Recognition
JEL Codes: C90; G10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Professional experience (A29) | Degree of overconfidence (D81) |
| Length of observed price history (E30) | Degree of underconfidence (D81) |
| Higher discriminability of price paths (D40) | Degree of underconfidence (D81) |
| Degree of overconfidence (task 1) (D81) | Degree of overconfidence (task 2) (D80) |
| Specific task (Z00) | Psychological bias in financial modeling (G41) |