Intelligent Asset Allocation via Market Sentiment Views

Frank Xing · Erik Cambria · Roy E. Welsch

130 citations0/0 files storedIEEE Computational Intelligence Magazine

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Risk and Portfolio Optimization

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.

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References

1952PORTFOLIO SELECTION*2014A five-factor asset pricing model 2006Another look at measures of forecast accuracy 2011Twitter mood predicts the stock market 2004Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards 2000The M3-Competition: results, conclusions and implications 1994Performance Measurement in a Downside Risk Framework 1991Asset Allocation

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