Predicting abnormal returns from news using text classification

By Ronny Luss, Alexandre d'Aspremont. 2008

157 citations recorded by the source corpus.

We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.

Subfields: Finance, Management Science and Operations Research

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Citations in the corpus

Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, methodology): “, support vector machines (SVMs) [3], [4], [9], [10] and multilayer perceptrons (MLPs) [11]–[14].”
Improving stock market prediction via heterogeneous information fusion (2017, background): “Various studies have found that ﬁnancial news can dramatically aﬀect the stock price [14, 15, 16, 17, 18, 19].”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, methodology): “However, previous work represents news documents mainly using simple features, such as bags-of-words, noun phrases, and named entities (Lavrenko et al., 2000; Kogan et al., 2009; Luss and d’Aspremont, 2012; Schumaker and Chen, 2009).”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, methodology): “Several applications of SVM classiﬁcation in big data analytics include text classiﬁcation [49], pattern matching [50], health diagnostics [51], and commerce.”
Detecting, quantifying and accessing impact of news events on Indian stock indices (2017, background): “In [18] Luss and Aspremont show that information extracted from news articles can be used to predict intraday price movements of financial assets using support vector machines.”
Applications of news analytics in finance: A review (2012, background): “…and is focused on using the news content directly in the context that the modeller is interested in. Lavrenko et al. (2000), Moniz, Brar, and Davis (2009), Peramunetilleke and Wong (2002) and Luss and d’Aspremont (2009) also use market-based measures in determining the ‘‘sentiment’’ of news.”
Stock Movement Prediction from Tweets and Historical Prices (2018, background): “This predictive dependency is a result of the fact that public information, e.g. a company scandal, needs time to be absorbed into movements over time (Luss and d’Aspremont, 2015), and thus is largely shared across temporally-close predictions.”
Knowledge-Driven Event Embedding for Stock Prediction (2016, methodology): “The baseline methods are three state-of-the-art news-based stock market prediction systems: Luss and d’Aspremont et al. (2012) propose using bags-of-words to represent news documents, and constructing the prediction model by using Support Vector Machines (SVMs); Ding et al. (2014) report a system…”
Measuring the Information Content of Financial News (2016, background): “In terms of corpora, financial news (Leinweber and Sisk, 2011; Xie et al., 2013; Luss and d’Aspremont, 2015; Ding et al., 2015), firm reports (Kogan et al.”
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, methodology): “• BW-SVM: bag-of-words and support machines (SVMs) prediction model (Luss and d’Aspremont, 2015).”