Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140388
Title: Intelligent asset allocation via market sentiment views
Authors: Xing, Frank Z.
Cambria, Erik
Welsch, Roy E.
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25-34. doi:10.1109/MCI.2018.2866727
Journal: IEEE Computational Intelligence Magazine
Abstract: 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.
URI: https://hdl.handle.net/10356/140388
ISSN: 1556-603X
DOI: 10.1109/MCI.2018.2866727
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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