香港大學Waiki Ching教授應邀做學術報告
應偉德國際1946bv官網的邀請，香港大學Waiki Ching教授于2024年10月19日上午9點在中關村校區主樓317會議室做了題為《On Adaptive Online Mean-Variance Portfolio Selection Problems》的學術報告。報告會由郭思尼老師主持，學院眾多師生參加了本次報告會。
程教授圍繞在線投資組合選擇問題展開深入闡述，聚焦市場快速變化情境下如何借助先進技術和模型提升投資者決策的準確性。他指出，傳統投資組合理論如Markowitz的均值-方差模型雖構建了理想的風險與收益平衡框架，但在復雜多變的市場環境中顯得力不從心。為此，程教授引入適應性技術，提出兩種創新模型，為在線投資組合選擇提供了強有力的支持。
程教授首先介紹了在線投資組合選擇問題的背景，強調投資者需根據市場實時信息動態調整資產配置，而短期內精確預測未來資產回報并合理規避風險是當前研究的難點。針對這一問題，他提出了適應性在線移動平均方法（AOLPI），通過結合歷史數據和同行資產影響，動態調整資產預測中的衰減因子，提高預測準確性。實驗證明，AOLPI在多種市場環境下顯著優于傳統簡單移動平均（SMA）和指數移動平均（EMA）方法。隨后，程教授介紹了適應性均值-方差模型（AMV），該模型綜合考慮風險與收益的平衡，通過動態更新協方差矩陣捕捉資產間的風險關聯，并通過風險偏好參數靈活調整投資策略。AMV使投資者能夠根據最新市場數據動態管理風險，特別在高風險市場中展現顯著優勢。
程教授通過多組實際市場數據集驗證了AOLPI和AMV模型的有效性，并展示了兩者結合形成的AOLPIMV算法在提升投資組合優化效果方面的卓越表現。該算法在交易成本考慮下仍能保持較高回報，優于傳統在線投資組合選擇方法。程教授的研究為在線投資組合選擇提供了新的技術工具，幫助投資者在復雜的市場環境中做出更科學的決策，同時推動了金融科技領域的進一步發展。
報告結束后，程教授和與會師生展開了積極的討論交流。報告反響熱烈，得到了師生們的一致好評。
匯報人簡介：
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.
