Automated news reading: Stock price prediction based on financial news using context-capturing features

By Michael Hagenau, Michael Liebmann, Dirk Neumann. 2013

333 citations recorded by the source corpus.

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

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Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “Hagenau et al. [10] extract a large scale of expressive features to represent the unstructured text data and employs a robust feature selection to enhance the stock prediction.”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “previous literature overviews [45, 15] for a comparison of predictive accuracy across different news sources.”
Sentiment analysis algorithms and applications: A survey (2014, background): “Table 1 contains the articles reference [4–7] and [ 12– 61 ].”
Text mining for market prediction: A systematic review (2014, methodology): “Some of the other works are using a technique called n-grams (Butler & Kešelj, 2009; Hagenau et al., 2013).”
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, methodology): “Hagenau et al. (2013) also achieved good results by applying Chi-square and Bi-normal separation feature selection with n-gram features.”
Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “Frequency divided by total number of words Mahajan et al. (2008) Latent Dirichlet Allocation (LDA) Extraction of twenty-five topics Binary Butler and Kešelj (2009) Character N-grams, three readability scores, last year’s performance Minimum occurrence per document.”
Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks (2016, neutral): “[3] [4],[5],[6] researched financial textual data with SVM.”
Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures (2017, background): “2013 [41] Word groups n-grams Chi-based feature selection Single words retrieved from corpus Full content vs.”
Improving Decision Analytics with Deep Learning: the Case of Financial Disclosures (2015, background): “The measurable eﬀect of ad hoc news on abnormal returns on the day of an announcement have been established by several authors [c. f. 27, 28, 25, 21].”
A survey of the applications of text mining in financial domain (2016, background): “Hagenau et al. (2013) by enhancing more expressive features to represent text from the market feedbacks, proved that robust feature selection helps to improve the accuracy as compared to complex features.”
Natural language based financial forecasting: a survey (2017, background): “Prior to it, some relevant discussions about news impact on stock markets can be spotted within papers, such as [67] and [43].”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Hagenau et al. (2013) employed bi-normal separation-based feature selection, enhanced by an SVM classifier, to predict stock-price changes signalled by German financial news with 71.8% precision.”