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Reciprocal social influence on investment decisions: behavioral evidence from a group of mutual fund managers

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Abstract

In this paper, I analyze reciprocal social influence on investment decisions in an international group of roughly 2,000 mutual fund managers who invested in companies in the DAX30. Using a robust estimation procedure, I provide empirical evidence that the average fund manager puts 0.69 % more portfolio weight on a particular stock if his or her peers, on average, assign a weight to the corresponding position that is 1 % higher compared to other stocks in the portfolio. The dynamics of this influence on choice of portfolio weights suggest that fund managers adjust their behavior based on the prevailing market situation and are more strongly influenced by others in times of an economic downturn. Analyzing the working locations of the fund managers, I conclude that more than 90 % of the magnitude of influence stems from social learning. Although this form of influence varies a great deal over time, the magnitude of influence resulting from the exchange of opinions is more or less constant.

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Notes

  1. See statistics of the Investment Company Institute on https://doi.org/www.ici.org/research/stats/worldwide/ww_09_11.

  2. As of November 30, 2011, MSCI reports a market capitalization of $30,057 billion for the MSCI ACWI All Cap Index, which covers approximately 98 % of the global equity investment opportunity set. Index fact sheets are available at https://doi.org/www.msci.com/resources/.

  3. I use the term “exchange of opinion” in order to emphasize that information is not only transmitted, but also discussed.

  4. See Frey et al. (2006) for a brief survey of studies that used the measure of Lakonishok et al. (1992).

  5. See, e.g., Bikhchandani and Sharma (2000) and Hirshleifer and Teoh (2003) for a survey of theoretical and empirical research on herd behavior in financial markets or Hirshleifer and Teoh (2008) for a more recent survey about general social influence on financial markets.

  6. Of course, lower compensation during a market downturn, for instance, might lead to less social activity. However, this is unlikely to affect a regular lunch get-together or an after-work beer, nor does it induce fund managers to cut their social relationships.

  7. Contrary to the social interaction literature, I do not consider contextual effects, i.e., the influence of an individual’s characteristics on the outcome of an other individual, as it is unlikely that a mutual fund manager’s decisions are influenced by the background of another fund manager.

  8. More generally than in the social interaction literature, I do not assume that a fund manager is equally influenced by other fund managers. This means, the values of \(\gamma _{ijt}\) do not have to be equal for fixed \(i\) and \(t\).

  9. Lee (2002) shows that this bias vanishes if the overall influence of an individual is very small. This applies if the matrix \(\varvec{\Gamma _\mathrm{t}}\) is dense. My results, however, suggest that the influential network of fund managers is sparse, such that the influence of a single fund manager cannot be ignored.

  10. To ensure sufficient degrees of freedom for the empirical analysis, I require two fund managers to hold at least 30 stocks in common on a particular reporting date before social influence can be considered. Otherwise, \(\gamma _{ijt}\) is set to zero. This is a reasonable approach because the distribution of a fund manager’s portfolio weights cannot be influenced by other fund managers who hold completely different portfolios.

  11. Actually, Lee (2003) showed that the estimators of the third step in the three-step procedure proposed by Kelejian and Prucha (1998) are not optimal. However, assuming that the error term is not correlated across fund managers, the first and the third steps in Kelejian and Prucha (1998) are equal.

  12. To put these numbers into perspective, note that in a comparable context, Hong et al. (2005) use data from 1,635 funds during a 2-year period, which leads to less than a quarter of the number of observations used in this paper.

  13. The relevance of past stock returns is also confirmed by the empirical study of Franck et al. (2013), who analyzed a similar set of fund managers.

  14. Removing the variable DIFF_EARN and including the years 2002 to 2004 qualitatively leads to the same results.

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Acknowledgments

I thank Horst Entorf, Uwe Walz, Jan Krahnen, Alfons Weichenrieder, Markus M. Schmid (the editor), and an anonymous referee for valuable comments and suggestions.

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  1. Faculty of Business Administration and Economics, Goethe-University, Frankfurt/Main, Germany

    Frederik König

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Appendix

Appendix

In this appendix, it is shown that the instruments \(\mathbf {Z}=[\varvec{\Gamma _\mathrm{t}}\mathbf {X}_{t},\mathbf {X}_{t}]\) can be used to estimate Eq. (3) by a 2SLS estimator if \(\mathbf {X}_{t}\) is uncorrelated with the error term and if the spectral radius of \(\delta _{t}\varvec{\Gamma _\mathrm{t}}\) is smaller than one.

The endogenous regressor \(\varvec{\Gamma _\mathrm{t}}\mathbf {w}_{t}\) can be expressed by the reduced form Eq. (4) as follows:

$$\begin{aligned} \varvec{\Gamma _\mathrm{t}}\mathbf {w}_{t}=\varvec{\Gamma _\mathrm{t}}\left( \mathbf {I}-\delta _{t}\varvec{\Gamma _\mathrm{t}}\right) ^{-1}\left( \mathbf {X}_{t}\varvec{\beta _\mathrm{t}}+\varvec{\epsilon }_{t}\right) \!. \end{aligned}$$
(16)

If the spectral radius of \(\delta _{t}\varvec{\Gamma _\mathrm{t}}\) is lower than one, the Neumann expansion can be used and leads to

$$\begin{aligned} \varvec{\Gamma _\mathrm{t}}\mathbf {w}_{t}=\varvec{\Gamma _\mathrm{t}}\left( \mathbf {I}+\delta _{t}\varvec{\Gamma _\mathrm{t}}+\delta _{t}^{2}\varvec{\Gamma _\mathrm{t}}^{2}+\cdots \right) \left( \mathbf {X}_{t}\varvec{\beta _\mathrm{t}}+\varvec{\epsilon }_{t}\right) \!. \end{aligned}$$
(17)

If \(\mathbf {X}_{t}\) is not correlated with \(\varvec{\epsilon }_{t}\), it follows that \(\varvec{\Gamma _\mathrm{t}}\mathbf {X}_{t}\) is a valid instrument for \(\varvec{\Gamma _\mathrm{t}}\mathbf {w}_{t}\), because it is correlated with \(\varvec{\Gamma _\mathrm{t}}\mathbf {w}_{t}\), but does not have a direct impact on \(\mathbf {w}_{t}\), as it does not appear in Eq. (3).

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König, F. Reciprocal social influence on investment decisions: behavioral evidence from a group of mutual fund managers. Financ Mark Portf Manag 28, 233–262 (2014). https://doi.org/10.1007/s11408-014-0232-2

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