Does geographic location matter to stock return predictability?

Abstract

Building on recent and growing evidence that geographic location influences information diffusion, this paper examines the relation between firm's location and the predictability of stock returns. We hypothesize that returns on a portfolio composed of firms located in central areas are more likely to follow a random walk than returns on a portfolio composed of firms located in remote areas. Using a battery of variance ratio tests, we find strong and robust support for our prediction. In particular, we show that the returns on a portfolio composed of the 500 largest urban firms follow a random walk; however, all variance ratio tests reject the random walk hypothesis for a portfolio that includes the 500 largest rural firms. Our results are robust to alternative definitions of firm's location and portfolio formation.

Capital structure and monitoring by local owners

“…This effect is stronger among rural areas, as they are more exposed to regional shocks than urban areas having a more diversified industry mix and thus can increase predictability in rural areas. Boubaker et al (2018) hypothesize and find that the returns from a portfolio composed of urban firms are more likely to follow a random walk than the returns of a portfolio of firms located in remote areas. Evidence of higher risk, increased predictability and the only game in town effect would potentially lead to monitoring being more effective in rural areas.…”

Analysis of the relevance of sentiment data for the prediction of excess returns in a multiasset framework

“…The world of finance and economics is multifactorial. Including other data such as commodity prices (Black et al, 2014), the geographic location of companies (Boubaker et al, 2019), business cycles (Liu et al, 2021), information on equity block trades (Kurek, 2014(Kurek, , 2016, and other derived economic data (Baetje, 2018;Cenesizoglu et al, 2019;Rahman et al, 2021) would help predictions. Furthermore, it soon became apparent that including extrafinancial data in order to quantify certain intangibles (such as how the public feels about a stock, or how environmentally sound a company's activity is) has its place in statistical models.…”