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«Oscar Stolper (University of Giessen) Andreas Walter (University of Giessen) Discussion Paper Series 1: Economic Studies No 23/2011 Discussion Papers ...»

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local population.7 This distinctive feature provides us with the opportunity to geographically demarcate their respective business spheres. We thus further narrow our sample to SecuStat filings of savings banks and credit cooperatives. In the case of savings banks, an institution’s outreach is typically bound to the local district it is located in. Generally, it is not possible for savings banks to expand their activities into another institution’s business sphere.8 Analogously, cooperative banks have a mandate to promote their (local) members, and thus are also regionally bounded. Since data about the business areas of German credit cooperatives is not available, we define a cooperative bank’s headquarter as the geographic center of its business district.9 While we do not know the exact location of each private investor, we are reasonably sure that customers of a certain savings or cooperative bank reside nearby the respective institution: A virtually identical portfolio of products and services within the respective banking pillars does not provide any incentive for a customer to choose a remote institution when there is a local one available. Consequently, they will pick the local bank for convenience, and we assume that the holdings which a savings or cooperative bank reports, stem from local customers. Following the approach of Grinblatt and Keloharju (2001), we define the zip code area of the bank as the geographic center of its associated pool of investors.10 Throughout the paper, we will refer to corresponding aggregations of private clients’ stockholdings at the bank level as ‘(individual/private) investor’ and ‘(private) household’, respectively.11 A decentralized organizational structure of the two banking pillars together with nationwide geographic penetration leads to a high density of independent savings and cooperative banks in the German market.12 Specifically, the resulting data set covers nearly 94% of all German Wengler (2006), p. 286.

Until 2005, local districts typically incurred the guarantor liability of their respective regional savings bank.

Since then, banks’ geographic outreach has not changed materially.

This approach follows Conrad et al. (2009), p. 398.

Grinblatt and Keloharju (2001) use the center of the municipality in which the investor resides as the starting point for their distance calculations.

Seasholes and Zhu (2010) mention that studying investor-level portfolios elevates the impact of small stock positions and easily biases overall results; see section 4.2 for further details. In order to overcome potential distortions, they form portfolios which aggregate the value-weighted shareholdings of many individual investors at the zip code level, which is essentially what we do.

Conrad et al. (2009) investigate regional variation of sector-specific bank outreach to retail customers in Germany and find that branch, deposit, and loan penetration is higher for public savings banks and credit cooperatives as compared to private banks.

commercial banks (1,715 out of 1,830 independent reporting entities during the period under review).13 We match the quarterly domestic equity holdings from the SecuStat database with company-specific information on returns and free float market capitalization as well as index membership obtained from Datastream. Also, we make use of information provided by the OpenGeo Database to translate the postal codes of investors and firm headquarters into latitudinal and longitudinal coordinates.

2.2 Descriptive statistics

Summary statistics for the sampled households and companies are reported in Table 1, while Figure 1 plots their geographic distribution across Germany. We include the domestic shareholdings of nearly 6 million private households throughout Germany.14 Panel A of Table 1 presents basic characteristics of the average household portfolios constructed from our sample. Overall, the mean (median) value of direct investments in common stock of companies headquartered in Germany―i.e. domestic stock―during the period under review adds up to EUR 7,183 (EUR 6,289). Depositors living in urbanized areas of Germany account for roughly 75% of all portfolios under review and feature higher average amounts of domestic stock investments than those in rural areas.15 Interestingly, regardless of the proximity to an urban center, the average percentage of domestic stockholdings remains virtually identical at about 19% of households’ total portfolio value across all asset classes. Considerable heterogeneity in the value of domestic stockholdings is however observed when comparing households in the Western states to those in the New Länder. For the New Länder, the mean portfolio fraction held in domestic stocks declines by almost 75% to EUR 1,800 or 8.4% of average total portfolio value. In addition, stockholders living in the New Länder constitute only 7.6% of all portfolios under review, while the region is home to more than 16% of the German populaNote, however, that savings banks and credit cooperatives cover only roughly 36% of the total German stock market capitalization held by domestic private households.

This approach differs from other local bias studies such as Ivkovic and Weisbenner (2005), Dorn and Huberman (2005), and Seasholes and Zhu (2010), among others, who infer their findings from studying the clients of a single discount brokerage house.





Areas with above-median (below-median) population density are referred to as urbanized (rural). The necessary data is derived from a joint research data center run by the Federal Statistical Office and the Federal Ministry of Transport, Building, and Urban Development (INKAR), which collects respective items on an annual basis.

tion.16 Taken together, however, these figures indicate that German individual investors are less geographically concentrated than private investors in other European countries.17 Figure 1 and Table 1, Panel B, reveal that banks, as well, are much less densely distributed in the New Länder. Only 8.5% of the sampled institutions have their premises in East Germany. This uneven spread is largely driven by the disproportionately low presence of cooperative banks, which make up nearly 75% of institutions in the full sample. Moreover, the rightmost column of Panel B of Table 1 provides some information with regards to the banklevel aggregations of households’ portfolios employed in our subsequent analyses. On average, each bank in the sample reports the securities holdings of 3,401 private households.18 Finally, the map plotted in Figure 1 suggests that a considerable number of the firms sampled in our study cluster in only a handful of agglomeration areas, while the rest of the country is rather sparsely populated with company domiciles. Yet, with more than half of the 1,109 companies in the sample headquartered outside the ten biggest cities (Panel C of Table 1), Germany still appears to be more evenly industrialized than other countries for which similar empirical studies exist.19 3 Do German individual investors exhibit a local equity preference?

3.1 Assessing the locality of investors’ stockholdings To start off, we require a distance threshold with which to classify shares that are local to a given investor, i.e. issued by a company which is local to the investor’s home. Following the standard approach by Coval and Moskowitz (2001), we categorize each stock within 100 kilometers of an investor’s zip code area as a local stock; shares beyond this radius are referred to as remote or nonlocal stocks. While it can be argued that a radius of 100 kilometers is an arbitrary threshold, we replicate our results for a number of different radii and find that they As of December 31, 2009, data obtained from the Federal Statistical Office.

For instance, Bodnaruk (2009) reports that as much as 60% of the households analyzed in his study live in the three largest Swedish cities. Similar proportions apply to studies conducted in Finland, cf. Grinblatt and Keloharju (2001), and Norway, cf. Doskeland and Hvide (2011).

Seasholes and Zhu (2010), who apply the same technique, on average aggregate the holdings of 120 households at the zip code level; see section 4.2 of this paper for further details.

Grinblatt and Keloharju (2001), for instance, report that as much as two thirds of all sampled firms in their study are domiciled in the city of Helsinki.

do not change materially. Therefore, we stick to the radius of 100 kilometers in the following, which makes our results more easily comparable.

In order to obtain the geographic distance between households and companies, we translate the postal codes of each investor and each company headquarters20, respectively, into latitudes and longitudes (measured in degrees). Using the conventional formula, we then compute the

linear distance disti, j in kilometers between investor i and stock j as:

–  –  –

where lat and lon denote the latitudinal and longitudinal coordinates of the sampled investors and companies, and r is the radius of the earth (≈6,378 kilometers). Occasionally, investor and company headquarters share a common zip-code. In such cases, instead of assigning a zerodistance, we use one quarter of the linear distance between the pertaining zip code and the closest neighboring postal area. This convention follows Thomas and Huggett (1980) and has been stated customary in geographic science. Next, each stock j is assigned a weight w BM j,t which corresponds to the total value of its readily available shares relative to the free float market capitalization across the aggregate of sampled stocks at the end of the reporting period t (last day of the respective quarter); w BM may be interpreted as the weight of company j in the j,t

–  –  –

3.2 Results Table 2 reports empirical evidence on the degree to which an average German household’s portfolio composition deviates from the benchmark of locally available investments. Panel A provides an intuitive approach to assessing the local equity preference by comparing an average investor’s distance (in kilometers) from her actual portfolio versus the market portfolio which consists of all stocks in the sample.22 The rightmost column reports the difference between the two distances and gives a first indication as to whether households actually tilt their equity portfolios towards local companies. During the 17 quarters under review, the average individual investor holds stocks which are 255.1 kilometers away from her place of residence, while the distance to the market portfolio amounts to 290.5 kilometers. Hence, she invests in stocks which are 35.4 kilometers closer than the benchmark, pointing to a substantial overweight of local companies.

Basic breakdown

Panel B of Table 2 displays the results for the local bias metric derived in section 3.1. The average fraction of stock investments in companies headquartered within 100 kilometers of a given household amounts to 20.1%, whereas the mean share of the market portfolio within this radius is 11.8%. Thus, our data documents a substantial local bias of 8.3% for the period For a detailed description of this measure, see Coval and Moskowitz (1999).

under review. This is less than the 13% excess local holdings among Norwegian households reported by Doskeland and Hvide (2011) and the 14% local bias for U.S. retail investors documented by Seasholes and Zhu (2010).23 Interestingly, however, the inter-country difference does not appear to stem from households’ actual holdings―at 19.6%, Seasholes and Zhu (2010), for instance, find a virtually identical fraction of locally invested stock―but instead from differing benchmark levels. This is intuitive, since in the U.S., the mean market capitalization within a range of 100 kilometers represents much less of the country’s aggregate market capitalization than in Germany.

Next, we check for the robustness of the basic breakdown. Specifically, we consider the possibility that our findings are essentially the result of households residing in certain regions and invested in certain stocks.

Local bias and investor location



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