SR-BLITS: SHARPE RATIO’S BACKWARD-LOOKING IMPROVEMENT AS A TRADING STRATEGY

A common and trivial strategy with respect to a single security or a tradeable asset is to buy and-hold. In contrast, a strategy named SR-BLITS is proposed that takes a position based on buy and sell signals which are calculated at each decision index T. These signals correspond to the maximisation of a backwards-looking Sharpe Ratio (SR), a measure of risk-adjusted returns, which is calculated using past (T-1) returns values as input. At index T, a new vector of positions – for all indices t<T thus far -- is calculated such that the backward-looking SR is maximised, and payments for adjusting the existing vector of positions are accounted for. This purchase (or sale) to correct all past positions taken at indices t<T, is assumed to be performed at the current price of the security or asset. The computation for these signals involves solving at most 2 systems of linear equations at each T, and only 1 if transaction cost is not considered. However, the matrix size to be inverted increases with T, requiring the algorithm to be restricted to episodes of size M. Numerical experiments on Geometric Brownian Motion series, NSE, and NASDAQ indices are conducted. With transaction costs considered, these reveal more than 30% improvement in average SR for an episode of trading, when compared to a buy-and-hold strategy.