The home bias like the disposition effect is a well-researched economic phenomenon in investor behaviour which has been examined in finance journal articles for decades. While there is little doubt about the existence of the bias, its magnitude varies across countries and investor groups. The home bias has to be regarded as a multifactorial phenomenon, a combination of numerous causes which all synergistically contribute. In contrast to other biases the home bias can at least partially be explained by reasons beyond irrational investor behaviour. While institutional restrictions play a minor role, informational asymmetries and superior information of domestic investors are important factors. Thus, the performance of investments may well benefit from a home bias, and the bias then no longer would be a puzzle but rather rational behaviour as a lower diversification level may lead to higher returns. The contemporary understanding of the home bias gains in relevance as the ongoing political debate in Germany has to clarify an institutional framework for long-run retirement savings plans of private households based on equity investments.

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As a result of technological innovations in data processing, the exploitation of Internet usage data in relation to search engines or social networks is becoming increasingly intriguing for understanding and anticipating stock market movements. We analyze the impact of three alternative investor attention variables, i. e. Google search volume, Wikipedia page views, and stock market-relevant news on the rapidly growing FinTech sector. The result of the simultaneous correlation analysis reveals a highly significant correlation between the trading activities of the FinTech sector and the three investor attention variables. The time-delayed regression analysis complements the results by identifying substantial changes of the effects within one week considering the order of magnitude and sign. Furthermore, multivariate regression analysis highlights that the explanatory power for future stock trading activities and illiquidity primarily depends on Google search volume and stock market-relevant news volume, while the simultaneous correlations are best explained by the number of visits to the corresponding Wikipedia page.

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