Correlating financial time series with micro-blogging activity

By Eduardo J. Ruiz, Vagelis Hristidis, Carlos Castillo, Aristides Gionis, Alejandro Jaimes. 2012

296 citations recorded by the source corpus.

We study the problem of correlating micro-blogging activity with stock-market events, defined as changes in the price and traded volume of stocks. Specifically, we collect messages related to a number of companies, and we search for correlations between stock-market events for those companies and features extracted from the micro-blogging messages. The features we extract can be categorized in two groups. Features in the first group measure the overall activity in the micro-blogging platform, such as number of posts, number of re-posts, and so on. Features in the second group measure properties of an induced interaction graph, for instance, the number of connected components, statistics on the degree distribution, and other graph-based properties. We present detailed experimental results measuring the correlation of the stock market events with these features, using Twitter as a data source. Our results show that the most correlated features are the number of connected components and the number of nodes of the interaction graph. The correlation is stronger with the traded volume than with the price of the stock. However, by using a simulator we show that even relatively small correlations between price and micro-blogging features can be exploited to drive a stock trading strategy that outperforms other baseline strategies.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, background): “For example, the relationship between Twitter activities and stock market is studied under a graph-based view [34].”
Stock Market Prediction through Technical and Public Sentiment Analysis (2015, background): “More recently, research has shown that public sentiment is correlated with stock market events [1], [2], [3].”
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, methodology): “Since the crowd in microblogs provides many individual comments/opinions that were not available before, businesses and organizations have begun to leverage microblogs to proﬁle customers, derive brand perception, gauge citizen sentiments, and predict the stock market [23, 34, 41, 53].”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “Large volumes of time-series sequences appear in almost every discipline, including astronomy, biology, meteorology, medicine, finance, robotics, engineering, and others [1, 5, 21, 23, 29, 43, 59, 62].”
Semantic Frames to Predict Stock Price Movement (2013, background): “Ruiz et al. (2012) correlate text with financial time series volume and price data.”
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”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, background): “Large volumes of time-series sequences appear in almost every discipline, including astronomy, biology, meteorology, medicine, finance, robotics, engineering, and others [1, 6, 25, 27, 36, 52, 70, 76].”
Sentiment analysis of Twitter data for predicting stock market movements (2016, background): “Ruiz et al. [7] have used time-constrained graphs to study the problem of correlating the Twitter micro-blogging activity with changes in stock prices and trading volumes.”
Exploiting Social Relations and Sentiment for Stock Prediction (2014, background): “The rationale behind is that micro-blogging activities have been shown to be highly correlated with the stock market (Ruiz et al., 2012; Mao et al., 2012).”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “For example, several papers have looked at predicting market movements [32, 4], asset values [56] and market events [48], or using Twitter, with a focus on correlating the mood of a population with the direction of financial markets [5, 53].”
Social signals and algorithmic trading of Bitcoin (2015, background): “Furthermore, analyses of Twitter discussions about companies can be applied in a portfolio strategy, yet its evaluation through backtesting leads to very moderate returns and their statistical significance is not assessed [38].”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, methodology): “Ruiz et al. (2012) studied the relationship between Twitter activities and stock market under a graph based view.”