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Home bias in portfolio choices: social learning among partially informed agents

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Abstract

Using an ‘incomplete information’ model, we explore the role of social learning in the global portfolio choices of stock market investors. When partially informed followers attempt to estimate true domestic (home) mean returns, they likely acquire private domestic signals from partially informed leaders. However, the calibration results indicate the existence of home bias when partially informed agents have poor quality information. Partially informed agents are prone to a learning bias; they overreact to new domestic information due to overconfidence in their domestic private signals, but they demonstrate a conservative response to new information in foreign markets. Links between the private signals of partially informed agents may lead to correlated foreign investment strategies among such agents through social learning. We suggest the acquisition of private signals, along with the dissemination of information, affect international portfolio decision rules and are determinants of the phenomenon of home bias.

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Notes

  1. For recent estimates across countries, see Table 1 in Gau et al. (2010).

  2. For detailed surveys of home bias literature, see Lewis (1999) and Cooper et al. (2013).

  3. This tendency is confirmed by growing evidence on home bias, as demonstrated by Karlsson and Norden (2007) and Lutje and Menkhoff (2007).

  4. For social learning models, see Cao et al. (2011) and Kaustia and Knupfer (2012); reviews of social learning in the field of finance are provided by Hirshleifer and Teoh (2003).

  5. A detailed survey of the incomplete information model is provided by Feldman (2007).

  6. The same methodology for solving for equilibria with incomplete information can be employed to solve for the case with complete information, because it is possible to restate the incomplete information non-Markovian problem as an σ-algebraic equivalent of the complete information Markovian problem (Feldman 2007).

  7. Merton (1980) demonstrates that in reality, expected returns are notoriously more difficult to estimate than variances; Chopra and Ziemba (1993) suggest that accurate means forecasting is critical to mean–variance frameworks.

  8. Regardless of the value taken by \(\xi\) and ϕ, the optimal portfolio weight of all agents is still restricted to within the range of 0 ≤ θ B  ≤ 1.

  9. Brandt et al. (2004) suggest that conservatism could be interpreted as a person’s overconfidence in his or her ability to learn.

  10. Social learning can also arise as a result of a change in the behavior of the followers in our study, resulting in their acting against the crowd (Eyster and Rabin 2011; Park and Sabourian 2011); that is, some followers have contrarian behavior in the model, viewing the good news possessed by their leaders as a signal of bad news, such that they expect a reversal in current trends. Therefore, their estimation of the mean domestic returns may be more accurate than their better-informed leaders, because their domestic private signal relates negatively to that of their counterparts (ρ L,F  = − 0.99). Their contrarian behavior also enlarges their information sets and enhances their prior beliefs. Under the same scenario, they may have less hedging demand for their global portfolio choices than their leaders, such that they lean toward the benchmark weights suggested by mean–variance optimization.

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Acknowledgments

The authors would like to thank C. F. Lee (the editor), an anonymous referee, and the participants at The 2012 NTU International Conference on Finance, the 20th Conference on Securities and Financial Markets, the 2013 Annual Conference of Taiwan Finance Association, the 8th NCTU International Finance Conference, and the 22nd Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management (PBFEAM) for many useful comments and suggestions on earlier drafts of this paper. Any remaining errors are our own. Wen-Lin Wu acknowledges the research grant of the Ministry of Science and Technology of Taiwan (NSC102-2410-H-035-011).

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  1. Department of International Trade, Feng Chia University, 100, Wenhaw Road, Seatwen, Taichung City, 40724, Taiwan, ROC

    Wen-Lin Wu

  2. Department of Finance, National Central University, Taoyuan City, 32001, Taiwan, ROC

    Yin-Feng Gau

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Correspondence to Wen-Lin Wu.

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Wu, WL., Gau, YF. Home bias in portfolio choices: social learning among partially informed agents. Rev Quant Finan Acc 48, 527–556 (2017). https://doi.org/10.1007/s11156-016-0560-6

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