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Financial Time Series Forecasting: Semantic Analysis Of Economic News

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  • Kateryna Kononova
  • Anton Dek

Abstract

The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequency of occurrence in the news of financial markets. For single-root words it has been left only common part that allows covering few words for one request. Neural networks were chosen for modeling and forecasting. To automate the process of extracting information from the economic news a script was developed in the MATLAB Simulink programming environment, which is based on the generated sampling of positive and negative words. Experimental studies with different architectures of neural networks showed a high adequacy of constructed models and confirmed the feasibility of using information from news feeds to predict the stock prices.

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  • Kateryna Kononova & Anton Dek, 2017. "Financial Time Series Forecasting: Semantic Analysis Of Economic News," Papers 1705.08545, arXiv.org.
    • Handle: RePEc:arx:papers:1705.08545
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    References listed on IDEAS

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    1. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    2. Leonid Kogan & Stephen A. Ross & Jiang Wang & Mark M. Westerfield, 2006. "The Price Impact and Survival of Irrational Traders," Journal of Finance, American Finance Association, vol. 61(1), pages 195-229, February.
    3. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
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