Does a firm’s geographic feature matter for stock returns? Evidence from the Chinese A-share market

ABSTRACT

This study reveals a new stock return predictability that relates to firms’ geographic features in the Chinese A-share market. Using a text-based measure of the degree of localness to capture the economic ties between firms and their provinces, we find that low-localized firms are slow to incorporate local information into stock prices. Specifically, there is a significant lead-lag effect in stock returns between high- and low-localized firms in the same region, and a portfolio that exploits this pattern can generate a monthly alpha of about 1%. This effect cannot be explained by geographic or industry return momentum, investors’ inattention, and limits to arbitrage. We find that this return predictability is mainly driven by investors’ limited information-processing capacity, and the evidence of predictability is stronger among low-localized firms with highly complicated business structures.

KEYWORDS:

• Return predictability
• geography
• information processing
• investors’ inattention
• Chinese A-share market

JEL CLASSIFICATION:

• G10
• G12
• G14

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Cohen and Frazzini (2008), Cao, Chordia, and Lin (2016), and Smajlbegovic (2019) note that investors’ inattention can cause stock prices to delay response information related to the value of a firm.

² Platikanova and Mattei (2016) show that the geographic dispersion of firm’ operations affects the information processing of financial analysts, making it difficult.

³ The assumption that firms have the closest economic connection with their HQ province does not always hold because a firm’s main plants and operations may be located in other provinces that are far away from its HQ province. This study considers that firms more frequently mention the region with more economic connections. Therefore, the province with the highest mention share should be the province of a firm’s main economic activities (ELP). We find that more than 92% of the A-share listed firms mentioned their HQ province the most in their annual reports.

⁴ In the robustness tests, we show that our results are unaffected with different cut-offs (e.g. 65%, 75%, and 80%).

⁵ For example, for a low localized firm A with a degree of localness at a% in province X, which also has two high-localized firms B and C, we compute the local information proxy of firm A as a% × value-weight stock returns of firm B and C. If our hypothesis holds, the proxy should be able to predict firm A’s future stock return.

⁶ Some studies indicate that local bias arises from non-informative behaviour, such as because investors are more familiar with firms in geographical proximity (Huberman 2001; Seasholes and Zhu 2010).

⁷ The administrative division of the People’s Republic of China (version 2017) excludes Taiwan, Hong Kong SAR, and Macau SAR.

⁸ Province-level administrative regions in China include provinces, municipalities, and autonomous regions. We do not count only province-level region names because it is more common for firms to mention county- and city-level region names in their annual report. The use of only province names for counting may reveal a large bias.

⁹ According to Pirinsky and Wang (2006) and Bernile, Kumar, and Sulaeman (2015), the strength of stock return co-movement between a firm and its location generally refers to the strength of the economic tie between a firm and its location. Thus, in Appendix C, we provide more empirical evidence to support our annual-report-based location measure through the co-movement of stock returns between firms in the same region: (1) For firms whose ELP is different from their HQ province, we find that their stock returns have stronger co-movement with the firms in the same ELP than those in the HQ province. (2) For firms with a changed ELP, the co-movement of stock returns between them and the firms in the new ELP is significantly enhanced after the change. However, after the change, the co-movement of stock returns between them and the firms in the old ELP is significantly weaker. (3) The higher the degree of localness of a firm, the stronger the co-movement between its stock returns and the firms within the ELP.

¹⁰ The factor data we use is obtained from CSMAR, which is constructed based on all stocks in the A-share market. We also reconstruct the six factors based on the stocks selected in this study after cleaning the data (see Panel B of Table 1). Then we rerun the six-factor model regression. In unreported results, we obtain a 1.345% (t = 5.09) monthly alpha from the equal-weighted L/S portfolio, and a 0.988% (t = 2.32) monthly alpha from the value-weighted L/S portfolio.

¹¹ In addition, we have obtained consistent findings when we re-identify the firm’s ELP and degree of localness based on the firm’s annual report over the past three years. We also rerun our test on subsamples, such as state-owned and non-state-owned listed firms, different market conditions (up or down), and find consistent findings with our main result. For brevity, the results are not reported here and are available on request.

¹² Rather than Google (Da, Engelberg, and Gao 2011), Baidu is the search engine with the highest market share in China. We collect data of media coverage and the Baidu index from the Chinese Research Data Services Platform (CNRDS).

¹³ Table 8 , Tables 9, and 10 report the equal-weighted portfolio return and alphas, and the results are unchanged when we use value-weighted portfolio return and alphas.

Additional information

Funding

This paper is funded by the National Natural Science Foundation of China (no.71874145; no.71903154), Social Science Foundation of Chongqing Municipal Education Commission (no. 22SKGH182;no.21SKJD070), and Young scholar Research Foundation of Chongqing Philosophy and Social Sciences (no. 2021NDQN36).