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On the risk prediction and analysis of soft information in finance reports

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  • Tsai, Ming-Feng
  • Wang, Chuan-Ju

Abstract

We attempt in this paper to utilize soft information in financial reports to analyze financial risk among companies. Specifically, on the basis of the text information in financial reports, which is the so-called soft information, we apply analytical techniques to study relations between texts and financial risk. Furthermore, we conduct a study on financial sentiment analysis by using a finance-specific sentiment lexicon to examine the relations between financial sentiment words and financial risk. A large collection of financial reports published annually by publicly-traded companies is employed to conduct our experiments; moreover, two analytical techniques – regression and ranking methods – are applied to conduct these analyses. The experimental results show that, based on a bag-of-words model, using only financial sentiment words results in performance comparable to using the whole texts; this confirms the importance of financial sentiment words with respect to risk prediction. In addition to this performance comparison, via the learned models, we draw attention to some strong and interesting correlations between texts and financial risk. These valuable findings yield greater insight and understanding into the usefulness of soft information in financial reports and can be applied to a broad range of financial and accounting applications.

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  • Tsai, Ming-Feng & Wang, Chuan-Ju, 2017. "On the risk prediction and analysis of soft information in finance reports," European Journal of Operational Research, Elsevier, vol. 257(1), pages 243-250.
    • Handle: RePEc:eee:ejores:v:257:y:2017:i:1:p:243-250
    DOI: 10.1016/j.ejor.2016.06.069
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    1. Blasco, Natividad & Corredor, Pilar & Del Rio, Cristina & Santamaria, Rafael, 2005. "Bad news and Dow Jones make the Spanish stocks go round," European Journal of Operational Research, Elsevier, vol. 163(1), pages 253-275, May.
    2. Wong, W.K. & Xia, Min & Chu, W.C., 2010. "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Elsevier, vol. 207(2), pages 807-816, December.
    3. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    4. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    5. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    6. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    7. Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
    8. Laih, Yih-Wenn, 2014. "Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm," European Journal of Operational Research, Elsevier, vol. 232(2), pages 375-382.
    9. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    10. Balakrishnan, Ramji & Qiu, Xin Ying & Srinivasan, Padmini, 2010. "On the predictive ability of narrative disclosures in annual reports," European Journal of Operational Research, Elsevier, vol. 202(3), pages 789-801, May.
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