香港大学Waiki Ching教授应邀做学术报告
应澳门永利唯一官网304的邀请，香港大学Waiki Ching教授于2024年10月19日上午9点在中关村校区主楼317会议室做了题为《On Adaptive Online Mean-Variance Portfolio Selection Problems》的学术报告。报告会由郭思尼老师主持，学院众多师生参加了本次报告会。
程教授围绕在线投资组合选择问题展开深入阐述，聚焦市场快速变化情境下如何借助先进技术和模型提升投资者决策的准确性。他指出，传统投资组合理论如Markowitz的均值-方差模型虽构建了理想的风险与收益平衡框架，但在复杂多变的市场环境中显得力不从心。为此，程教授引入适应性技术，提出两种创新模型，为在线投资组合选择提供了强有力的支持。
程教授首先介绍了在线投资组合选择问题的背景，强调投资者需根据市场实时信息动态调整资产配置，而短期内精确预测未来资产回报并合理规避风险是当前研究的难点。针对这一问题，他提出了适应性在线移动平均方法（AOLPI），通过结合历史数据和同行资产影响，动态调整资产预测中的衰减因子，提高预测准确性。实验证明，AOLPI在多种市场环境下显著优于传统简单移动平均（SMA）和指数移动平均（EMA）方法。随后，程教授介绍了适应性均值-方差模型（AMV），该模型综合考虑风险与收益的平衡，通过动态更新协方差矩阵捕捉资产间的风险关联，并通过风险偏好参数灵活调整投资策略。AMV使投资者能够根据最新市场数据动态管理风险，特别在高风险市场中展现显著优势。
程教授通过多组实际市场数据集验证了AOLPI和AMV模型的有效性，并展示了两者结合形成的AOLPIMV算法在提升投资组合优化效果方面的卓越表现。该算法在交易成本考虑下仍能保持较高回报，优于传统在线投资组合选择方法。程教授的研究为在线投资组合选择提供了新的技术工具，帮助投资者在复杂的市场环境中做出更科学的决策，同时推动了金融科技领域的进一步发展。
报告结束后，程教授和与会师生展开了积极的讨论交流。报告反响热烈，得到了师生们的一致好评。
汇报人简介：
Professor Waiki Ching is a distinguished academic at the University of Hong Kong, well-known for his contributions to stochastic modeling, financial mathematics, and computational biology. He has extensive expertise in areas such as matrix computations, operations research, and quantitative finance. His research focuses on applying mathematical techniques to solve real-world problems in finance and biology, including portfolio optimization, risk management, and biological network analysis. Professor Ching has published a wide array of peer-reviewed papers in top journals and has been awarded numerous research grants from prestigious funding bodies such as the Hong Kong Research Grant Council.
He made significant advancements in both finance and biology through the development of algorithms and models. His work in financial mathematics includes dynamic portfolio selection, risk management, and online investment strategies. One of his key contributions is in adaptive online portfolio optimization, where he has developed innovative models such as the adaptive mean-variance model and adaptive online moving average methods, which have been successfully applied to real-world investment scenarios. These methods improve investment decision-making by balancing risk and return in dynamic, high-frequency trading environments. In computational biology, Professor Ching has focused on analyzing biological data and modeling complex systems like gene regulatory networks. He applies matrix computation techniques to study large-scale biological networks, helping to understand how biological systems function and evolve.
