This paper explores the connection between corporate headquarter location and stock comovement. The authors find that stocks in the same region demonstrate comovement with each other that is not explained by the general market or industry comovement. Furthermore, companies that move their headquarters show less stock comovement with companies in the previous location and greater comovement with those in the new location. This comovement could be the result of either local fundamental factors or geographic segmentation of investors. The paper tests and finds evidence against the idea that stock comovement is determined by local economic conditions and instead argues in favor of geographic investor segmentation.
The paper considers the location of a firm to be the location of its headquarters since firms often choose to locate their headquarters in a region that is close to their main operations. Of 272 Metropolitan Statistical Areas (MSA) used to define location, about 90 have at least five publicly traded companies over the sample period of 1988 to 2002. This set of MSAs is used in the empirical tests.
To measure a stocks comovement with other stocks in the same MSA the authors construct equal weighted portfolios for each MSA. They then regress the excess returns of each stock on the return of this local portfolio, $R_i^{LOC}$, (excluding the stock used as the dependent variable) and on the market factor.
\[ R_{t} = \alpha_i + \beta^{LOC}R_i^{LOC} + \beta^{MKT}R_i^{MKT} + \epsilon_{i,t} \]
This regression is also expanded to include an equal weighted industry portfolio. To address the concern that industries, as defined by CRSP, do not accurately reflect the interaction of local companies, the authors also include whichever two additional industries they find exhibit the greatest comovement with the returns of the particular stock in the regression.
\[ R_{t} = \alpha_i + \beta^{LOC}R_i^{LOC} + \beta^{MKT}R_i^{MKT} + \beta^{IND}R_i^{IND} + \sum_{K=1}^{2} \beta^{IND_K}R_i^{IND_K} + \epsilon_{i,t} \]
Table 2 presents the results from three regressions over the full sample and two sub-periods. The location beta is positive and statistically significant across all periods and regressions. Although including industry returns decreases the magnitude of the location beta, stocks continue to exhibit significant comovement with local portfolios. When additional industries are included, which are independently correlated with the stock movement, their betas are found to be negative, adding to the strength of the local comovement result.
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| Table 2 |
Next, they identify 118 firms that relocate their headquarters during the period 1992-1997. Many of these firms are small and cite reasons for relocation such as moving closer to customers or the means of production. The following regression is run for the five years before and after the relocation, not including the year of the relocation. The returns of the old MSA’s index are represented by $R_t^{LOCO}$ and the returns of the new MSA’s index by $R_t^{LOCN}$.
\[ R_{t} = \alpha_i + \beta^{LOCO}R_i^{LOCO} + \beta^{LOCN}R_i^{LOCN} + \beta^{MKT}R_i^{MKT} + \beta^{IND}R_i^{IND} + \epsilon_{i,t} \]
As shown in table 3, the change in comovement with the old index is negative and the change in comovement with the new index is positive with both statistically significant. This holds both without the industry factor (2) or with (1). In fact, the comovement with the old index is reduced by nearly half in the five years after the move. The authors note that it is unlikely that the change in comovement is due to changes in the fundamental aspects of the firm since the firms in the sample generally did not make any changes in their production process.
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| Table 3 |
The authors further test if local comovement of stock prices can be explained by local economic variables. They find no comovement in local earnings and no explanatory power in a measure of local economic fundamentals.
Finally, the paper address the cross-section of stocks to determine the relationship between common firm characteristics and local comovement. As seen in table 7, size, return on asset (ROA), and institutional ownership are all negatively and generally significantly correlated with comovement throughout the full sample and three sub-periods. The interpretation is that smaller stocks, as well as less profitable stocks and stocks with more individual ownership experience greater local comovement.
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| Table 7 |
Given the lack of evidence for comovement of local fundamentals, as well as the finding that comovement is stronger for small firms with greater individual investor ownership, the authors suggest local comovement is due to geographic segmentation. This indicates that geographic diversification is an important consideration. However the finding that investors’ biases contribute to the local comovement effect indicates a general lack of geographic diversification. The authors conclude by noting that geographic diversity could play an important role in pension plans and 401(k) accounts.
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