We propose new metrics for investment performance based on short-run trading profitability. Since investment opportunities are scarce and value-relevant information decays over time, marginal decisions made by fund managers (i.e., trades) should provide more accurate signals about underlying skill than portfolio alphas, which are contaminated by the returns on "stale" positions. Our measures range from the very simple ("hit rate," or the fraction of trades that outperform the benchmark over the subsequent month) to the more complex (regressions relating trade size to subsequent profitability). We examine the validity of these measures in a global sample of long-only equity funds, for which we observe daily trading activity. In our sample, trade-based metrics are more persistent than portfolio alphas and, more importantly, are better able to forecast future portfolio alphas (in a mean squared error sense). Simple and complex methods are almost equally effective. A hypothetical manager-selection exercise reveals that trade-based performance measurement can improve the risk-adjusted returns to investors by up to 3% per annum.
Di Mascio, Rick, Anton Lines, and Narayan Naik. "Trade-based performance measurement." Columbia Business School, 2018.
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