基地研究员宁瀚文副教授与合作者（Wenzhi Xi, Department of Financial Statistics, Zhongnan University of Economics and Law; Zhanfeng Li, Department of Financial Statistics, Zhongnan University of Economics and Law; Xinyuan Song, Department of Statistics, The Chinese University of Hong Kong）的论文 "Online portfolio selection with predictive instantaneous risk assessment" 在重要学术期刊Pattern Recognition在线发表。

       Abstract:

   Online portfolio selection (OPS) has received increasing attention from machine learning and quantitative finance communities. Despite their effectiveness, the pioneering OPS methods have several key limitations. First, price predictions are usually based on predetermined trends, which is inadequate for a fast-changing market patterns. Second, each asset is treated individually, ignoring the pervading relevance among the assets. Third, the risk terms are usually missing or inappropriate in optimizations. This paper proposes a novel OPS method, namely, the online low-dimension ensemble method, to overcome the limitations. Motivated by the stylized facts for the co-movements of assets, the financial market is regarded as a high-dimensional dynamical system (HDS), and a large number of low-dimensional subsystems (LDSs) are randomly generated from the HDS to extract the correlation information among the assets. The assets’ price predictions are first made using these LDSs and then aggregated to formulate the final prediction using ensemble learning techniques. Thanks to the particular merits brought by our predicting scheme, we also develop a novel high-dimensional covariance matrix estimation/prediction method for short-term data, efficiently assessing the instantaneous risk of the projected portfolios. Compared with state-of-the-art methods, our approach obtains more accurate predictions as the correlation information is fully exploited. With the predictive instantaneous risk assessment, a more appropriate optimization problem is proposed, substantially improving the OPS setting and leading to significantly better investment performance. Therefore, this study develops a flexible and promising approach to learning fast-changing market patterns and demonstrates that the high-dimensional feature of the market is a crucial information source for financial modeling with short-term data rather than a barrier in the conventional sense. Extensive experiments on real-world datasets are conducted to illustrate our method further.

    Keywords：Portfolio optimization; Online learning; High-dimensional covariance matrix; Ensemble learning; High-dimensional short-term data

 链接：https://doi.org/10.1016/j.patcog.2023.109872 

教师简介

宁瀚文，副教授，主持国家自然科学基金两项，国家社会科学基金一项，在科学出版社出版专著一部，论文发表于《中国科学》《统计研究》，IEEE T Neural Networks & Learning Systems、Pattern Recognition、Journal of the Franklin Institute等国内外顶尖期刊。今后的主要研究方向为机器学习，深度学习与经济，金融数据分析的交叉。