Of course, every transaction can be viewed from the perspective of the buyer or the seller and this makes the behavior of any flow data inherently ambiguous. A randomly selected subsample of buys or sells, is, by definition, uncorrelated with similarly obtained subsamples as well as with returns. So portfolio flows in general, and our flows in particular, are interesting only to the extent they identify a group which differs from other investors. For us, large institutional investors domiciled outside of the “local” market are that group. An inflow into the “local” market is defined as any purchase by one of these investors which settles in local currency. This is useful because the profile of these transactions corresponds closely to the generic definition of cross-border flows. Such flows are often thought to respond to similar information (and misinformation), and as already mentioned, to give rise to contagion and excessive volatility in local-market asset prices.
We put the flow data to work in a number of ways. First, we examine the behavior of flows across countries. We find that there is a small, but significant, correlation in contemporaneous cross-country flows, and that this correlation is larger within regions. Using factor analysis, we then identify a regional factor that summarizes regional flows across countries. The factor explains roughly 40% of flow variation within the region. It also helps remove much individual-country noise.
Second, we characterize the flow data by their persistence. Standard market microstructure models predict that traders with private information reach their desired positions slowly, in order to reduce transaction costs. Thus, the order flow of informed traders will be positively autocorrelated. Furthermore, in models in which informed investors are prevented from borrowing, transactions are (on average) less autocorrelated than uninformed transactions. Empirically, we find substantial evidence that flows are persistent. We also find that gross outflows are more persistent than gross inflows.
Third, we examine the covariance of equity and currency returns with cross-border flows. A major disadvantage of previous studies that use quarterly or monthly data is that they cannot be precise about whether measured covariance is truly contemporaneous. The daily data allow us far greater precision in determining contemporaneous versus non-contemporaneous components of quarterly covariance. We decompose the covariance of quarterly flows and quarterly returns into three components: a) covariance of flows and lagged returns; b) the covariance of contemporaneous flows and returns; and c) the covariance of flows and future returns.