Risk Diversification Strategy with Moving Average Reversion for Automatic Portfolio Optimization

Automatic portfolio optimization (APO) is the process of automatically optimizing the allocation of assets in an investment portfolio through algorithms and models. Empirical evidence suggests that stock prices are likely to follow the mean reversion theory. Although existing mean reversion strategies for APO have been shown to achieve good empirical performance across many real-world datasets, they tend to construct single-stock portfolios, which results in high risk. In this paper, we propose a risk diversification version of Online Moving Average Reversion (OLMAR) [16], one of the major mean reversion strategies. The parameter k of our proposed method OLMAR-k controls the search space for optimization to guarantee the existence of solutions and prevent from solutions close to uniform. According to experimental results in resent datasets, OLMAR-k outperforms other state-of-the-art APO methods in terms of popular metrics for return and risk.