This paper provides an analytical overview of the most widely used capital flow datasets. The paper is written as a guide for academics who embark on empirical research projects and for policymakers who need timely information on capital flow developments to inform their decisions. We address common misconceptions about capital flow data and discuss differences between high-frequency proxies for portfolio flows. In a nowcasting “horse race” we show that high-frequency proxies have significant predictive content for portfolio flows from the balance of payments (BoP). We also construct a new dataset for academic use, consisting of monthly portfolio flows broadly consistent with BoP data.
This paper utilizes a new dataset of foreign and domestic mutual funds in Mexico to assess their behavior and obtains three new findings. First, foreign mutual funds are more sensitive to global financial conditions and engage more in herding and positive feedback trading than domestic mutual funds, notably during episodes of market stress. Second, the behavior of foreign funds differs substantially across types of funds: bond funds are more sensitive to global factors and engage more in positive feedback trading than equity funds; funds sold to retail investors, open-end funds, small funds, and regional funds also appear to be less stable sources of capital flows. Third, there is indicative evidence that foreign funds’ trading behavior is associated with higher local market volatilities, notably in periods of market stress; however, domestic mutual fund investors played some mitigating role.