报告题目：Online Portfolio Selection with Transaction Costs
Transaction costs incurred by changes of investment proportions on risky assets have a significant impact on the investment strategy and the return in long-term investment horizon. We consider an adaptive online portfolio selection problem with transaction costs. An adaptive online moving average method (AOLMA) is proposed to predict the future returns of risky assets by incorporating an adaptive decaying factor, which improves the accuracy of return prediction. The adaptive online net profit maximization algorithm (AOLNPM) is then designed to maximize the cumulative return. In addition, we study the exact computation of the transaction cost and derive related constant upper and lower bounds. Considering that assets’ market states switch from time to time and their prices exhibit different behaviors in different market states, we propose the state-dependent exponential moving average method (SEMA), which can accurately predict the future returns based on historical return data and market states. Finally, we construct the net profit maximization model (NPM) and the net profit maximization model with a risk parity constraint (NPMRP) to achieve a better trade off between the cumulative return and investment risk.
郭思尼，北京理工大学管理与经济学院助理教授、特别副研究员，硕士生导师。研究方向包括数据驱动的决策优化、金融科技、物流规划。研究成果发表在European Journal of Operational Research, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy System, Automatica, Expert Systems with Applications, International Journal of Approximate Reasoning等期刊，主持国家自然科学基金青年项目、中国博士后科学基金面上项目、博士后国际交流计划引进项目。担任期刊Axioms 的Guest Editor、北京系统工程学会理事。