Intelligent asset allocation via market sentiment views

• المؤلفون الرئيسيون: Xing, Frank Z., Cambria, Erik, Welsch, Roy E.
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2020
• الموضوعات: Engineering::Computer science and engineering; Stock Markets; Covariance Matrices
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/140388
• المؤسسة: Nanyang Technological University

The sentiment index of market participants has been extensively used for stock market prediction in recent years. Many financial information vendors also provide it as a service. However, utilizing market sentiment under the asset allocation framework has been rarely discussed. In this article, we investigate the role of market sentiment in an asset allocation problem. We propose to compute sentiment time series from social media with the help of sentiment analysis and text mining techniques. A novel neural network design, built upon an ensemble of evolving clustering and long short-term memory, is used to formalize sentiment information into market views. These views are later integrated into modern portfolio theory through a Bayesian approach. We analyze the performance of this asset allocation model from many aspects, such as stability of portfolios, computing of sentiment time series, and profitability in our simulations. Experimental results show that our model outperforms some of the most successful forecasting techniques. Thanks to the introduction of the evolving clustering method, the estimation accuracy of market views is significantly improved.