Using a unique FinTech data containing monthly individual-level consumption, investments, and payments, we examine how FinTech can lower investment barriers and improve risk-taking. Seizing on the rapid expansion of offline usages of Alipay in China, we measure individuals’ FinTech adoption by the speed and intensity with which they adopt the new technology. Our hypothesis is that individuals with high FinTech adoption, through repeated usages of the Alipay app, would build familiarity and trust, reducing the psychological barriers against investing in risky assets. Measuring risk-taking by individuals’ mutual-fund investments on the FinTech platform, we find that higher FinTech adoption results in higher participation and more risk-taking. Using the distance to Hangzhou as an instrument variable to capture the exogenous variation in FinTech adoption yields results of similar economic and statistical significance. Focusing on the welfare-improving aspect of FinTech inclusion, we find that individuals with high risk tolerance, hence more risk-taking capacity, and those living in under-banked cities stand to benefit more from the advent of FinTech.

This study investigates attributes of local firms that determine investment biases using mutual funds holding data across 48 markets. Controlling for variations in market level environments, we find that firm characteristics related to transaction cost, corporate governance, information asymmetry and local familiarity create significant barriers to foreign investments. The extent to which information asymmetry and familiarity constrain investment allocation is more observable for foreign than for domestic investors, even in developed and liberalized markets. However, in emerging and restricted markets, variations in foreign investment bias are mainly driven by market level cross-border investment barriers. Overall, the well-documented “home bias” phenomenon may be a joint effect of both firm and market level investment barriers.

The current study documents an interesting phenomenon that retail investors prefer to invest in stocks listed at the stock exchange that is geographically close to them in China. This pattern is robust when we control for the well-documented local bias within a country. Among companies with similar distances to both stock exchanges, investors still display a much stronger tendency to invest in locally-listed companies. Among stocks with similar distances to both stock exchanges, those listed in Shanghai (Shenzhen) co-move more in returns and volume, with the benchmark at the Shanghai (Shenzhen) stock exchange. Such a preference for local exchange seems not to be motivated by information advantage, because investors do not obtain abnormal returns from their trades on stocks listed nearby. Our findings provide additional evidence that non-information-based familiarity bias induces investment and that such investor biases and exchange-level sentiment influence asset price formation.

Based on detailed trade records of individual investors who participated in both China’s A- and B- share markets, we find investors are more likely to buy A (B) shares when the A-share premium is lower (higher), when they have already held the same firm’s A (B) shares and when they have previously traded the same firm’s A (B) shares. Given that the correlation between the same firm’s A and B shares is below 70% and that A shares are more expensive, it is sensible for investors to invest more into the B shares. Our evidence suggests that investors accept a less than optimal portfolio due to lack of investment experience.