A quantitative stock prediction system based on financial news

By Robert P. Schumaker, Hsinchun Chen. 2009

217 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, background): “One main data source is the news articles [10, 11, 23, 24, 25].”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “The components are adapted from the systems described in [12, 8, 9, 10, 11].”
Media-aware quantitative trading based on public Web information (2014, methodology): “To gauge howwell the proposed trader, MAQT, performs in capturing stock movements, we choose two evaluation metrics as suggested by Schumaker and Chen [30]: directional accuracy and closeness.”
A Tensor-Based Information Framework for Predicting the Stock Market (2016, methodology): “Typically, a news article is represented as a “bag of words” (term vector) to capture ﬁnancial standings [Schumaker and Chen 2009b], and social media are processed using sentiment analysis to obtain social sentiments [Bollen et al. 2011].”
Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, methodology): “In order to avoid the conflict and follow the same method in Schumaker et al. [36] , d 1 is eliminated in our system.”
The effect of news and public mood on stock movements (2014, methodology): “Prior relevant studies [34,35,40,41] mainly focused on stock exchanges in the United States, particularly the New York Stock Exchange (NYSE).”
Empirical analysis: stock market prediction via extreme learning machine (2014, methodology): “In this way, we extend the work [31] by testing one prediction window to six different ones.”
Tensor-Based Learning for Predicting Stock Movements (2015, unknown): “…and Titman 1993), whereas modern behavioral finance is interested in the shortterm shock of sentimental factors and event-specific factors (Bollen, Pepe, and Mao 2011; Frank and Antweiler 2004; Gilbert and Karahalios 2010; Schumaker and Chen 2009b; Tetlock, Saar-Tsechansky, and Macskassy 2008).”
News-based trading strategies (2016, background): “Several papers [16, 17, 18] from the DSS domain elaborate on a general system design, but do not go as far as comparing approaches to trading signals and evaluating the accuracy of the resulting decisions within a ﬁnancial portfolio.”