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Evaluation of News-Based Trading Strategies

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Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 217)

Abstract

The marvel of markets lies in the fact that dispersed information is instantaneously processed by adjusting the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we attempt to design trading strategies that are built on textual news in order to obtain higher profits than benchmark strategies achieve. Essentially, we succeed by showing evidence that a news-based trading strategy indeed outperforms our benchmarks by a 9.06-fold performance.

Keywords

  • Financial news
  • Decision support
  • Trading strategies
  • Text mining
  • Sentiment analysis

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Notes

  1. 1.

    Kindly provided by Deutsche Gesellschaft für Ad-Hoc-Publizität (DGAP).

  2. 2.

    Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin).

  3. 3.

    Using a proportional transaction fee is common in financial research. For example, other papers [17] mostly vary transaction costs mostly in the range of 0.1 % to 0.3 % or assume a fixed transaction fee [33] of U. S. $ 10 for buying and selling stocks respectively.

  4. 4.

    A frequent unit in finance is basis point (bps). Here, one unit is equal to 1/100th of 1 %, i.e 1 % \(=\) 100 bps.

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Feuerriegel, S., Neumann, D. (2015). Evaluation of News-Based Trading Strategies. In: Lugmayr, A. (eds) Enterprise Applications and Services in the Finance Industry. FinanceCom 2014. Lecture Notes in Business Information Processing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-28151-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-28151-3_2

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