Exploiting Social Relations and Sentiment for Stock Prediction

By Jianfeng Si, Arjun Mukherjee, B. Liu, Sinno Jialin Pan, Qing Li, Huayi Li. 2014

82 citations recorded by the source corpus.

In this paper we first exploit cash-tags ("$" fol- lowed by stocks' ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We fur- ther show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For predic- tion, we propose to regress the topic-sentiment time-series and the stock's price time series. Ex- perimental results demonstrate that topic senti- ments from close neighbors are able to help im- prove the prediction of a stock markedly.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, methodology): “Finally, the sentiment time series and price time series are used for prediction [32].”
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, background): “…natural language processing, many methods have been proposed to explore additional information (mainly online text data) for stock forecasting, such as financial news (Xie et al., 2013; Ding et al., 2014), twitter sentiments (Si et al., 2013; Si et al., 2014), financial reports (Lee et al., 2014).”
Natural language based financial forecasting: a survey (2017, methodology): “[124] Number of tuples occurrences Naı̈ve Bayes & Association Rules Experimentally tuned k-NN [59] Trend possibility distribution n-gram Language model Conditional probability maximization [40] Tf-idf weighted key words Support Vector Machine Split-and-merge segmentation [2] Text classification Regr”
A New Model for Stock Price Movements Prediction Using Deep Neural Network (2017, methodology): “Many new methods have been proposed to explore additional information (mainly online text data) for the stock forecasting such as ﬁnancial news [9, 26], twitters sentiments [23], micro blogs [3].”
Deep learning for stock prediction using numerical and textual information (2016, background): “There has been a number of efforts for predicting the trend of stock prices on the basis of textual information [14], [15], [16], [17], [18].”