Aggregating exponential gradient expert advice for online portfolio selection under transaction costs
该研究将现有在线投资组合策略CAEG扩展到考虑交易成本的情形，提出新策略CAEGc，理论证明其对数累积财富均值有渐近上界，数值实验显示其优于其他相关策略且与基准策略相当。
As an application of machine learning techniques in the field of portfolio management, online portfolio selection (OLPS) aims at optimising the allocation of wealth in an uncertain environment. When making investment decisions, the transaction cost is such an important factor that investor should not ignore. Thus, this paper extends an existing online portfolio selection strategy Continuous Aggregating Exponential Gradient (CAEG) (Yang et al., 2022) in the presence of transaction costs. The proportional transaction costs model is constructed when the transaction costs are incorporated into the decision-making process, and we call this new strategy CAEGc. Theoretical guarantee proves that the mean of the logarithmic cumulative wealth of CAEGc has an asymptotic upper bound with that of its benchmark. The numerical examples demonstrate the impact of transaction costs on the proposed CAEGc strategy on the one hand, and on the other hand, verify that CAEGc outperforms other related OLPS strategies and is comparable to its benchmark strategy.