关键词: 在线投资组合, 交易费用, 排序, 相对价格预测, 移动窗口

Abstract: During the process of online portfolio decision-making, frequent adjustments of asset positions will cause a lot of transaction costs. This paper proposes an online portfolio strategy by considering both expected return and transactioncosts. We calculate the expected return of the portfolio by predicting the order of assets and measure the transaction costs by using the relative entropy distance, and thus construct an optimization model thatmaximizes the expected return and minimizes the transaction costs, and then obtain an online portfolio update strategy by solving it. Then, we theoretically prove that the strategy has BH-universality, that is, its average exponential growth rate is asymptotically the same as that of the offline optimal Buy and Hold strategy. Finally, we conduct numerical analysis of the proposed strategy on actual stock data from Chinese and American markets. The results show that it achieves a better cumulative wealth than the existing online portfolio strategies and is not sensitive to the parameters of the model.

Key words: online portfolio, transaction costs, order, price relative prediction, moving window