Abstract

If transitory profitable trading opportunities exist, filter rules are used to mitigate transaction costs. We use a dynamic programming framework to design an optimal filter which maximizes after-cost expected returns. The filter size depends crucially on the degree of persistence of trading opportunities, transaction cost, and standard deviation of shocks. Applying our theory to daily dollar-yen exchange trading, we find that the optimal filter can be economically significantly different from a naïve filter equal to the transaction cost. The candidate trading strategies generate positive returns that disappear after accounting for transaction costs. However, when the optimal filter is used, returns after costs remain positive and are higher than for naïve filters.