Integrated strategy for online portfolio by considering the reversal effect and investor attention

The online portfolio selection (OPS) problem is different from the classical portfolio model problems, as it dynamically adjusts asset positions in response to historical price sequences. Existing OPS strategies based on the reversal effect achieve greater cumulative return than those based on momentum. However, they face theoretical challenges in achieving competitive performance and rely primarily on explicit data variables, such as historical relative prices and trading volumes. Besides, existing research indicates that investor behaviour, an implicit data variable, may influence stock prices. Therefore, we take into account the reversal effect and investor attention to formulate expert strategies, subsequently integrating these strategies through the online gradient update. Theoretically, the regret of our integrated strategy is proven to have a sub-linear upper bound. Empirical results demonstrate that our integrated strategy outperforms existing OPS strategies in most cases. Specifically, across all datasets, annualised returns range from 8.12% to 206.35%. Also, the average Sharpe Ratio, Calmar Ratio, and Information Ratio are 1.2580, 2.8975, and 0.0342, respectively. Moreover, our integrated strategy exhibits robustness under varying parameter settings and remains effective under reasonable transaction costs.