Stock movement prediction with sentiment analysis based on deep learning networks

Yong Shi · Yuanchun Zheng · Kun Guo · Xinyue Ren

38 citations0/0 files storedConcurrency and Computation Practice and Experience

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Sentiment Analysis and Opinion Mining

Abstract

Abstract With the development of Internet and big data, it is more convenient for investors to share opinions or have a discuss with others via the web, which creates massive unstructured data. These data reflect investors' emotions and their investment intentions, and it will further affect the movement of the stock market. Although researchers have been attempted to use sentiment information to predict the market, the sentiment features used are driven by outdated emotion extraction systems. In this article, we proposed a new sentiment analysis system with deep neural networks for stock comments and applied estimated sentiment information to the stock movement forecasting. The empirical results showed that our deep sentiment classification method achieved a 9% improvement over the logistic regression algorithm, and provided an accurate sentiment extractor for the next predicting step. In addition our new hybrid features that mix stock trading data and sentiment information achieved 1.25% improvement among 150 Chinese stocks in the testing dataset. For American stocks, the sentiment information would reduced the predicting results. We found that emotion features extracted from comments are indeed effective for stocks with a higher price to book value and a lower beta risk value in China.

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References

2011Twitter mood predicts the stock market 2014Stock Price Prediction Using the ARIMA Model 2015Evaluating multiple classifiers for stock price direction prediction 2014News impact on stock price return via sentiment analysis 2016Sentiment analysis of Twitter data for predicting stock market movements 2016Deep learning for stock prediction using numerical and textual information 2016The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices 2013Automated news reading: Stock price prediction based on financial news using context-capturing features

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