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Review of Quantitative Finance and Accounting

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17-06-2017 | Original Research

Disposition effect and analyst forecast dispersion

Author: Daniela Vesselinova Balkanska

Published in: Review of Quantitative Finance and Accounting | Issue 3/2018

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Abstract

Behavioral finance theories posit that behavioral biases are more pronounced when there is higher information uncertainty about fundamentals. This paper examines the relation between the disposition effect, the tendency to ride losses and realize gains, and dispersion in financial analysts’ earnings forecasts for a sample of large U.S. discount brokerage accounts from January 1991 to December 1996. I find that the disposition effect is exacerbated in stocks with higher analyst forecast dispersion. In particular, the disposition effect is 10% in stocks in the highest forecast dispersion quintile and not significant in the lowest forecast dispersion quintile. The driving factor behind these findings is investors’ higher propensity to realize gains when facing higher information uncertainty. The results are robust to controlling for firm size, analyst coverage, idiosyncratic volatility, turnover, and past market-adjusted returns. The results provide supportive evidence for a behavioral bias explanation of the disposition effect consistent with mean-reversion beliefs for winners and loss actualization avoidance for losers.

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There is an on going debate in the literature as to whether dispersion in analyst earnings forecasts captures differences of opinion or parameter uncertainty. See, for example, Johnson (2004).

Other measures of differences of opinion include breadth of ownership (Chen et al. 2002) and the institutional ratio (Hobbs et al. 2017). I do not use these measures as they are only available quarterly.

Higher heterogeneity of beliefs could thus simply imply that mean reversion beliefs are more extreme and these erroneous expectations take longer to be corrected. For example, suppose that there are two sets of investors holding rational or mean-reversion beliefs. In this setting, as the dispersion in beliefs increases, so does the fraction of mean-reversion investors (up to one half of the total number of investors.) Thus, it is plausible to expect stronger mean reversion beliefs or slower updating of investor beliefs when there is higher heterogeneity of beliefs.

Among behavioral finance models focusing on the importance of disagreement for mispricing, Hong and Stein (1999) derive momentum in a setting where information diffuses gradually across heterogeneous agents.

It is also possible that higher order beliefs could cause slower incorporation of information and updating of investors’ beliefs. A higher order beliefs setup could be useful to study the effect of disagreement on the disposition effect. This setup has been used by Makarov and Rytchkov (2012), for example, to model heterogeneous beliefs and the momentum anomaly, predicting stronger momentum for stocks with higher belief heterogeneity.

The reverse disposition effect in their paper is obtained using the historical purchase price as the reference price, which is the approach followed in this paper. A low disposition effect is obtained in their paper when using the ever high price as the reference price.

More recent studies find opposite results, e.g. Coval et al. (2005), Kaniel et al. (2008), and document that individual investor trading improves stock liquidity (Wang and Zhang 2015).

Li and Yang (2008) model prospect theory preferences in a full equilibrium setting in which investors hold heterogeneous beliefs about the dividend growth rate and time variation in beliefs is important.

Gomes (2005) finds that the optimal portfolio choice with loss averse investors is consistent with the disposition effect, but he does not consider the initial investment decision. Barberis and Xiong (2008) and Hens and Vlcek (2011) model the trading behavior of an investor with prospect theory preferences, taking into account the initial decision. In a partial equilibrium setting, Barberis and Xiong (2008) find that prospect theory cannot explain the ex-post disposition effect for short time horizons, as the equity premium must be relatively high for loss averse investors to invest initially in the stock.

Based on the psychological literature on entrapment, escalating commitment, and sunk cost, Zuchel (2001) also suggests that the disposition effect and investors’ reluctance to realize losses in particular could be explained based on cognitive dissonance stemming from self-justification. In order to avoid cognitive dissonance, investors may irrationally prefer to hold losers to justify their initial purchase decision. Reluctant to close mental accounts at a loss, investors could become entrapped in a losing situation if they believe they are close to breaking even.

With regard to beliefs about future reversals, there are also some studies (Ji et al. 2001) which argue that Asians have a stronger tendency to expect reversal of fortune than do Westerners, suggesting that the effects considered in this study may operate differently across cultures.

The irrational belief in mean reversion in their model works in the domain of losses.

See Barber and Odean (2000) for details on the large discount brokerage dataset.

Results in this paper are similar using the summary statistics from the I/B/E/S U.S. Summary History files.

I also correct some errors in the classification of purchases and sales based on the sign of the quantities traded. The sales or purchase price for the aggregated transactions is the weighted average price.

The average purchase price, for example, is used by Odean (1998) and Ranguelova (2001). LIFO is used by Goetzmann and Massa (2008). FIFO, which is motivated by U.S. tax practice, is used by Frazzini (2006) and Kaustia (2010).

There are 231,456,050 observations for which CRSP data is available.

Due to availability of I/B/E/S data, the large discount brokerage portfolio holding sample is reduced by 28% to 165,844,122 observations.

The standard error for the difference in the proportions PGR and PLR is:

\( se = sqrt(PGR \times (1 - PGR)/(nRG + nPG) + PLR \times (1 - PLR)/(nRL + nPL)), \)

where nRG, nPG, nRL, nPL are the number of realized gains, paper gains, realized losses and paper losses.

Results in the paper are similar using the residuals from a Fama–French three factor model regression.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

Grinblatt and Keloharju (2001) find that the propensity to sell a stock is related to returns over the past month.

For the sake of brevity, Table 2 reports the statistical significance of the difference between dispersion quintile 5 (high) and quintile 1 (low) only. The full results are available upon request.

Since smaller stocks have lower analyst coverage, the I/B/E/S data is effectively restricted to larger stocks.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

T-statistics for the differences in PGR, PLR and PGR-PLR across all forecast dispersion quintiles are available upon request.

Unlike Ranguelova (2001), I find that the small stocks in my sample do not exhibit a reverse disposition effect.

For the sake of brevity, Table 3 reports the statistical significance of the difference between dispersion quintile 5 (high) and quintile 1 (low) only. The full results are available upon request. The differences are positive but insignificant between D1 and D2 in the bottom size quintile, between D1 and D2 in the second size quintile, D2 and D3 in the fourth size quintile, and D2 and D3 in the top size quintile.

The results are robust to using the analyst coverage measure.

Quintile differences are insignificant for dispersion quintiles D2 and D3 in the second, third and fourth coverage quintiles. For the sake of brevity, Table 4 reports the statistical significance of the difference between dispersion quintile 5 (high) and quintile 1 (low) only. The full results are available upon request.

For the purpose of brevity, Table 5 reports the statistical significance of the difference between dispersion quintile 5 (high) and quintile 1 (low) only. The full results are available upon request.

The differences in PGR across dispersion quintile are significant for 17 out of 20 cases, except for D3–D2 for V1, D3–D2 for V2, and D2–D1 for V4.

For the sake of brevity, Table 6 reports the statistical significance of the difference between dispersion quintile 5 (high) and quintile 1 (low) only. The full results are available upon request.

Differences in adjacent forecast dispersion quintile PGR are insignificant for D4–D3 for T1, D3–D2 for T2, T4 and T5.

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Title: Disposition effect and analyst forecast dispersion
Author: Daniela Vesselinova Balkanska
Publication date: 17-06-2017
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 3/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0648-7

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