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Predicting abnormal returns from news using text classification

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  • Ronny Luss
  • Alexandre D'Aspremont

Abstract

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.

Suggested Citation

  • Ronny Luss & Alexandre D'Aspremont, 2015. "Predicting abnormal returns from news using text classification," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 999-1012, June.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:6:p:999-1012
    DOI: 10.1080/14697688.2012.672762
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    References listed on IDEAS

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    Cited by:

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    2. Banerjee, Ameet Kumar & Dionisio, Andreia & Pradhan, H.K. & Mahapatra, Biplab, 2021. "Hunting the quicksilver: Using textual news and causality analysis to predict market volatility," International Review of Financial Analysis, Elsevier, vol. 77(C).
    3. Ummara Mumtaz & Summaya Mumtaz, 2023. "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis," Papers 2311.06273, arXiv.org.
    4. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    5. Ramit Sawhney & Shivam Agarwal & Vivek Mittal & Paolo Rosso & Vikram Nanda & Sudheer Chava, 2022. "Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models," Papers 2206.06320, arXiv.org.
    6. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    7. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    8. Yang, Ann Shawing, 2020. "Misinformation corrections of corporate news: Corporate clarification announcements," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    9. Audrino, Francesco & Tetereva, Anastasija, 2019. "Sentiment spillover effects for US and European companies," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
    10. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    11. Farnoush Ronaghi & Mohammad Salimibeni & Farnoosh Naderkhani & Arash Mohammadi, 2021. "COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction," Papers 2101.02287, arXiv.org, revised Jul 2021.
    12. Xi Zhang & Jiawei Shi & Di Wang & Binxing Fang, 2018. "Exploiting Investors Social Network for Stock Prediction in China's Market," Papers 1801.00597, arXiv.org.
    13. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.

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