The Long and Short of It: Why are Stocks with Shorter Runs Preferred?
University of California, Berkeley - Marketing Group
Sanjiv Ranjan Das
Santa Clara University - Leavey School of Business
December 12, 2003
How good are people at interpreting numerical information presented in graphical form? Are they better at doing so when the stakes are high? There is evidence for pessimism on both counts. This paper takes an information processing view of the manner in which people process a large amount of numerical information. We propose that people sample points in a numerical series chosen due to their salience. To the extent the choice of sample points does not accurately represent the entire population of data from which they are chosen this process systematically distorts perceptions of the statistical properties of the series. This paper examines a specific bias in the presentation of graphical numerical data: the run length of a stock series. Run length is the number of consecutive periods over which the stock price moves up or down. The increasing use of graphical data in financial decisionmaking implies that visual biases in data interpretation are of growing economic importance. A primary and robust effect across three experimental studies is that stocks with longer run lengths are perceived as riskier than stocks with shorter runs, leading to a preference for the latter, despite controlling for the first four moments of the stock return paths. The effect of run length on preference for stocks appears to be driven by perceptions of risk rather than perceptions of return and are exacerbated when the stakes of the decision maker are higher. Results are robust to sample characteristics such as gender and financial experience. They are also robust to contextual differences in presentation format. Results are explained in terms of series with higher run lengths being associated with higher local maxima and minima, with these points in the series being more likely to be sampled due to their higher salience. Theoretical implications for the processing of numerical information, graphical information, and financial information are discussed.
Number of Pages in PDF File: 35
Keywords: Information processing, runlength, salience, bias
JEL Classification: A14, C91, G10
Date posted: February 12, 2004
© 2015 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo5 in 0.328 seconds