«Oscar Stolper (University of Giessen) Andreas Walter (University of Giessen) Discussion Paper Series 1: Economic Studies No 23/2011 Discussion Papers ...»
∗ Corresponding author: Markus Baltzer, Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany, email@example.com.
Oscar Stolper, University of Giessen, Department of Financial Services, Licher Strasse 74, 35394 Giessen, Germany, Oscar.firstname.lastname@example.org.
Andreas Walter, University of Giessen, Department of Financial Services, Licher Strasse 74, 35394 Giessen, Germany, email@example.com.
The authors would like to thank the staff of the Bundesbank Statistics Department and especially Markus Amann and Matthias Schrape for supporting us with the relevant data. We are grateful to Ulrich Grosch and Heinz Herrmann as well as participants of seminars at the Deutsche Bundesbank, and the University of Giessen for their helpful comments on earlier versions. The authors are exclusively responsible for any remaining errors and inaccuracies.
This puzzle is referred to as home bias. See Karolyi and Stulz (2003) for a review of the home bias literature.
Ivkovic and Weisbenner (2005) and Seasholes and Zhu (2010) find that local stocks are overrepresented in the equity portfolios of U.S. discount brokerage clients. Grinblatt and Keloharju (2001) provide qualitatively similar evidence for private households in Finland, Massa and Simonov (2006) and Bodnaruk (2009) document that Swedish individual investors overweight firms with geographically close premises, and Feng and Seasholes (2004) point to a local bias among Chinese retail investors. Coval and Moskowitz (1999) show that, while less pronounced in magnitude, local bias is also observed among U.S. fund managers.
On the one hand, local bias has been shown to be strong enough to move markets. Recently, Korniotis and Kumar (2009) show that stock returns feature a predictable local component.
Likewise, Hong et al. (2008) identify an ‘only-game-in-town effect’ in the presence of locally biased investors, which characterizes a negative relation between the density of firm domiciles within a given region and the stock price levels of a company headquartered in that region. Finally, Loughran and Schultz (2004) and Jacobs and Weber (2010) show that a preference for local equity among investors also has a significant impact on firm-level turnover. In sum, this evidence implies that identifying the reasons behind local bias may improve understanding the market impact of geography.
On the other hand, local bias is equivalent to an under-diversification of risky assets and as such constitutes one of retail investors’ most fundamental deviations from what textbook models claim about optimal asset allocation. Under-diversification has been identified as a major challenge in household finance since it is assumed to have widespread effects on household welfare.3 Interestingly enough, however, the direction in which portfolio concentration affects investors’ welfare is subject to an active debate briefly outlined in the following.
Local bias and informational advantages
Several contributions to the local bias literature suggest that households’ overweight in geographically close stocks reflects informed (i.e. rational) investment decisions. One common approach to measure the informativeness of investment decisions is to analyze investors’ portfolio performance. The general idea is that, if investors’ preference for nearby stocks is driven by locally generated value-relevant information, the value of that information should be reflected in an excess return of their local holdings. Related studies assume real information asymmetries between local and remote investors and argue that information is more readily available for local stocks. This allows local investors to form more accurate expectations about the prospects of those stocks, thereby exploiting an information advantage in evaluating nearby companies (information hypothesis). Indeed, several authors including Feng and Seasholes (2004), Ivkovic and Weisbenner (2005), Massa and Simonov (2006), and Bodnaruk (2009) find that households’ local stock investments outperform their non-local ones. Note that in these studies, local bias is not tantamount to a violation of mean-variance portfolio See Campbell (2006) for a detailed discussion of this facet of household finance.
optimization. They argue that the increased portfolio risk incurred through the regional focus is rewarded by a superior performance of the local stockholdings.
Local bias and a preference for the familiar However, empirical evidence on informational advantages as the trigger for investors’ local bias is mixed and a variety of studies indicate that local bias, quite on the contrary, is actually detrimental to investor welfare. If this is the case, understanding the root cause of local bias is particularly important since it provides the basis for reducing the welfare costs of this investment mistake. In a recent contribution, Seasholes and Zhu (2010) re-estimate the findings of Ivkovic and Weisbenner (2005) using identical data and present diametrically opposed evidence of significant underperformance for U.S. households’ local equity investments. In an earlier study, Huberman (2001) shows that shareholders of regional phone companies in the U.S. tend to live in the area served by the company. He argues that exploiting an informational advantage essentially involves rebalancing one’s portfolio in a timely manner. Yet, his data suggests that investors tend to buy and hold the familiar stocks, a behavior which is inconsistent with trading on information. Similarly, Grinblatt and Keloharju (2001) state, that if investors make money by exploiting information, then those investors with superior information processing abilities should realize higher excess returns. However, in an earlier study, Grinblatt and Keloharju (2000) find that portfolio performance among Finnish investors is inversely related to investor sophistication and thus conjecture that local bias is unlikely to be driven by information.4 Zhu (2002) finds that the local bias of retail investors decreases with growing advertisement expenditures of the companies they hold in their portfolios. He figures that this is driven by selective attention rather than relevant information being delivered. Related results have been obtained by Ackert et al. (2005), who indicate that local bias cannot be associated with real information asymmetries but rather with the simple fact that companies close to home are recognizable. In an experimental analysis, they show that investors with an otherwise identical information set perceive themselves to be more knowledgeable about stocks in companies whose name they recognize, and subsequently overweight these securities.
All these studies soften or even reject the above-mentioned information hypothesis and instead advocate that local bias is the result of investors’ preference to invest in the familiar.
Barber and Odean (2000) document similar evidence for U.S. households.
However, due to the lack of a comprehensive analytical framework, these studies cannot exactly explain how investors’ familiarity with an asset actually affects local bias.
A comprehensive approach This paper investigates whether local bias can be explained when incorporating familiarity as an additional dimension to the portfolio selection process. To this end, we rely on a framework of familiarity established by Boyle et al. (2011), who build on the classic Markowitz model but allow investors to have different degrees of ambiguity across assets. This leads to a portfolio selection setting in which investors choose from a universe of familiar (where little relative ambiguity pertains) and unfamiliar securities. Assuming ambiguity aversion, the model imposes that investors optimize over risk, return, and familiarity. The resulting portfolio composition features some interesting deviations from the Markowitz-type portfolio and offers novel, empirically testable implications. First, the optimal portfolio is biased towards familiar assets. Second, the fraction of familiar assets increases in times of economic uncertainty, an effect which Boyle et al. (2011) call ‘flight to familiarity’.
Using geographic proximity as a proxy for familiarity towards an asset, we examine whether this framework of familiarity is able to explain local bias among German individual investors. Before we do so, however, we ask if German households overweight nearby stocks at all5, and examine whether this investment behavior is nevertheless consistent with meanvariance portfolio optimization, i.e. if informational advantages may be the underlying reason for local bias. In order to answer these questions, we study the Securities Deposits Statistics maintained by Deutsche Bundesbank which collects the common stock investments of retail customers at German regional banks on a security-by-security basis and allows specifying the geographical distance between investors and company headquarters.
We find that, indeed, private households in Germany significantly overweight nearby stocks and show that this result is robust across a number of different breakdowns. Second, we apply comprehensive performance analysis to investigate whether the observed portfolio locality is information-driven―i.e. generates positive alpha―and conclusively reject the notion of a ‘home-field advantage’ for German individual investors. Finally, we test key propositions of the framework of investor familiarity developed by Boyle et al. (2011). Our data While not the principal objective of their work, Dorn and Huberman (2005) report that equity holdings of clients of a German online broker are locally biased; also, the research of Hau (2001), Dorn et al. (2008), and Jacobs and Weber (2010) points to a local equity preference among German investors. Yet, as of now, there is no comprehensive investigation of the local bias phenomenon among German individual investors.
clearly confirms their hypotheses with regard to overinvestment in the familiar and empirically support a ‘flight to familiarity’ during financial crises. Taken together, our results suggest that including investors’ ambiguity aversion towards the available assets in the asset allocation problem contributes to explaining local bias among individual investors.
The remainder of this study is organized as follows. Section 2 describes the data set. In section 3, we introduce a measure of portfolio locality which we apply to private households’ domestic stockholdings. In section 4, we run a performance analysis to test whether the observed portfolio locality is a result of superior information about geographically close stocks.
Section 5 examines whether ambiguity aversion, on the contrary, explains investors’ local bias. Section 6 concludes.
2 Data and descriptive statistics
2.1 Data The database for this study is compiled from several sources. Our primary data set consists of mandatory filings of German commercial banks for the period from December 2005 to December 2009. Each bank in Germany is required to report the aggregate quarterly shareholdings of its retail customers on a security-by-security basis. This stock data is part of a centralized register of security ownership across a variety of asset classes and investor groups maintained by the Deutsche Bundesbank for the Securities Deposits Statistics (henceforth SecuStat).6 For an investigation of investor locality, we restrict our securities sample to domestic common stocks held by German private households at commercial banks. We confine the universe of reported equities to shares of publicly listed companies headquartered in Germany. The resulting sample comprises 1,317 different common stocks issued by 1,109 different corporations and effectively represents the entire universe of publicly listed companies in Germany.
Unlike most other economies, Germany still builds upon a three-pillar commercial banking system which consists of private banks, public savings banks and credit cooperatives. The latter two sectors have traditionally focused on providing access to banking services for the
For a technical documentation of this database, see Amann et al. (2011).