by Dr Mehrshad Motahari, Research Associate, Cambridge Centre for Finance and Cambridge Endowment for Research in Finance
A large body of literature documents how firms’ geographical locations can affect their stock returns. For example, Pirinsky and Wang (2006) show that the stock returns of firms headquartered in the same geographical area comove with each other. They argue that this comovement is not related to economic fundamentals but to the trading patterns of local investors. Various papers attribute this excess comovement to local bias that induces local investors to take larger positions in local stocks. Bernile et al. (2015) suggest that even institutional investors overweigh firms whose 10-Ks frequently mention the investors’ state. In contrast, Kumar et al. (2013) show that retail trades cause comovement in local stocks, whereas institutional trades mitigate the issue.
Local bias also leads to the incorporation of the behaviours and preferences of local investors in the prices of local stocks. Korniotis and Kumar (2013) highlight that local risk tolerance affects the returns of local stocks. Specifically, they argue that US state-level heterogeneity in economic conditions leads to variations in investor risk tolerance across states, and heterogeneous risk tolerance results in variations in the cross section of stock returns. In other words, the economic conditions of the region in which a firm is based can affect its stock price, irrespective of the firm’s fundamentals.
In the article “Geographic Heterogeneity, Local Sentiment, and Market Anomalies”, CCFin/CERF Research Associate Mehrshad Motahari shows that market anomalies (i.e. strategies that beat the market, such as momentum) have different performances for stocks headquartered in different US states. In other words, if we break the US cross section down into states in which anomalies have recently worked well and those in which anomalies have worked poorly, we observe that the first group will continue to have a better performance in the future. Using a famous anomaly variable such as momentum, the study shows that we can predict how well momentum predicts future returns by taking a firm’s headquarters into account. To illustrate, if we construct the momentum strategy (i.e. going long on high momentum stocks and low on low momentum ones) for stocks headquartered in either California or Texas in 2020 and find that this strategy works better for Californian stocks, it will likely continue to generate higher alphas for stocks in California in 2021.
This pattern can be explained by arguing that investors in different regions have different levels of sentiment. Local investors in states experiencing a relatively higher level of sentiment are more likely to buy excessively or overpay for local stocks. In the presence of local bias, short-selling impediments and information uncertainty, this behaviour exacerbates stock overpricing. The resulting mispricing is more severe in states experiencing higher sentiment and will persist due to limits to arbitrage.
The study also looks at analyst forecasting errors as a proxy for information uncertainty surrounding stocks. The idea is that investor biases and sentiment levels are more likely to be reflected in prices when the stock is hard to be valued. In line with this, the findings show that geography predicts the performance of anomalies only for stocks experiencing higher levels of analyst forecasting errors. Overall, the findings of this research and other papers in this area imply that it is preferable to tilt a portfolio towards stocks in specific geographic regions when devising systematic trading strategies to exploit mispricing. More importantly, studies on this subject establish that firms’ locations have more extensive effects on stock prices than previously documented. That is, the location of a stock can determine how the fundamentals of the stock will be priced in the cross section and relative to other local and non-local firms.
Bernile, G., Kumar, A. and Sulaeman, J. (2015) “Home away from home: geography of information and local investors.” Review of Financial Studies, 28: 2009-2049
Korniotis, G.M. and Kumar, A. (2013) “State-level business cycles and local return predictability.” Journal of Finance, 68: 1037-1096
Kumar, A., Page, J.K. and Spalt, O.G. (2013) “Investor sentiment and return comovements: evidence from stock splits and headquarters changes.” Review of Finance, 17: 921-953
Pirinsky, C. and Wang, C. (2006) “Does corporate headquarters location matter for stock returns?” Journal of Finance, 61: 1991-2015