Performance analysis of log-optimal portfolio strategies with transaction costs
提出一种近似对数最优投资策略，在考虑交易成本的情况下实现接近最优的投资增长率，并通过道琼斯30只股票数据证明其相比CAPM等模型能获得显著正异常收益。
In this paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and recommends a suboptimal portfolio for discrete investment periods. We do not assume any parametric structure for the market process, only a first-order Markov property. The model introduced is based on kernel-based agents' (experts') approximation of the maximum theoretical growth rate with transaction costs. Although the optimal solution is theoretically a complex Bellman programming problem, our suboptimal empirical result appears to be attractive for Dow Jones 30 shares. The paper presents a performance analysis where the return of the empirical log-optimal portfolio is compared with passive portfolio counterparts compiled from similar components using the CAPM, the three-factor model and the four-factor model. The proposed methods, in the presence of transaction costs, provide a significant positive abnormal return compared with the preceding equilibrium models, and is even a survivorship bias-free setup.