«Oscar Stolper (University of Giessen) Andreas Walter (University of Giessen) Discussion Paper Series 1: Economic Studies No 23/2011 Discussion Papers ...»
The intuition is that, if investors’ preference for geographically close companies is driven by locally generated value-relevant information, then the value of that information should be reflected in an excess return of their local stocks. More readily available information allows investors to form more refined expectations about those stocks, thereby exploiting an information advantage when assessing these nearby companies (information hypothesis). In order to test for potential information advantages, we conduct a comprehensive analysis of both the holdings-based and the transactions-based portfolio performance of private households in our sample. Our results conclusively reject a ‘home-field advantage’ of local over non-local individual investors. Next, we investigate to what extent investors’ familiarity with nearby companies is able to explain the empirically observed local bias. While prior studies in the field suggest that familiarity might be the reason for local bias, they remain largely unclear about the nature and the influence of familiarity on portfolio concentration. We take a theoretical framework of investor familiarity developed by Boyle et al. (2011) to the data. The model claims that familiarity arises from investors’ aversion towards the ambiguous and predicts that investors are not only locally biased but―in addition to that―local bias should increase in times of economic uncertainty (‘flight to familiarity’ effect).
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Appendix Calculation of returns employed in the performance analysis As detailed in section 2.1, holdings data is reported at the end of each quarter. Following Coval and Moskowitz (2001), we thus update a given investor’s portfolio holdings at the beginning of every quarter on the basis of the holdings reported for the previous quarter, and assume them to remain unchanged over the subsequent three months. So, for instance, the stock positions from the last quarter of 2008 (ending December 2008) are used with return data for January, February, and March 2009. Specifically, the two returns for investor i in quarter t are calculated as
where R local,i,t and R remote,i,t are the returns over the quarter t on investor i’s local and nonlocal stockholdings, respectively. N i reflects the number of stocks local to investor i, w iact,t −1 is the rescaled (to sum to one) fraction of stock j in her portfolio at the end of ,j quarter t-1, and, finally, r j,t is the three-month raw return of stock j at time t. Each investor produces a time series of 17 quarters of local returns.
This figure plots the spatial coordinates (in degrees) of the private households (blue rhombuses) as well as the public limited companies (red squares) represented in the sample. Households are mapped within the zip code area of their custodian bank. Companies are mapped according to the geographical location of their headquarters.
This figure plots the evolution of average local bias levels across German private households for the period between end-2005 and end-2009. Overall quarter-to-quarter changes (red line) are dissected in trading-based shifts (active portfolio rebalancing) and shifts induced by price movements absent transactions (passive portfolio rebalancing).
20 8.0% 10 7.5%
This figure plots German private households' quarterly local bias levels for the period between end-2005 and end-2009 against the VDAX New index. the VDAX New captures implied return volatility of the major German stock index DAX30 at a one-month horizon. Local bias levels are adjusted for stock price movements between the reporting periods and thus are confined to investors' active portfolio rebalancing decisions.
This table presents basic characteristics of the private households and firms represented in the sample. Panel A reports descriptive portfolio statistics of households delineated by the geographical area in which they reside, distinguishing between urban and rural, as well as West German states and East German states ('New Länder'). Numbers in Panel A reflect averages across all years under review. Panel B presents summary statistics pertaining to the custodian banks, to which the sampled households are affiliated. Finally, Panel C provides information on the geographic spread of the sampled public limited companies headquarters within the ten largest cities in Germany as well as outside those areas (Other ).
Table 2 Locality of German individual investors' domestic stockholdings Panel A: Distance between investor location and company headquarters (km)
This table presents portfolio statistics of German private households referring to the locality of their domestic stock investments. Panel A displays households' average distance to their actual holdings and to the market portfolio built from the full stock universe under review (both distances weighted by free float market value). The difference between the two distances is shown in the rightmost column. Panel B reports the average fraction of a household's stockholdings which is invested locally, i.e. within a radius of 100 kilometers around the household's place of residence, as well as the proportional free-float market capitalization within the household's local range. The difference between the two percentages denotes the local bias and is reported in the rightmost column. *** indicates statistical significance at the 1%-level.