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2020 | 1 (55) | 69-79

Article title

Using text analysis for evaluating the behaviour of rates of return from the WIG20 index

Content

Title variants

PL
Wykorzystanie analizy tekstu do oceny zachowania stóp zwrotu indeksu WIG20

Languages of publication

EN

Abstracts

EN
The aim of the article is to indicate the possibility of using text analysis for the research of dynamics of the Polish capital market. The first part of the article notes the changes which took place within the data market in recent years and their impact on the discounting of information by stock market investors. The Information Effectiveness Hypothesis and the paradigm of behavioural finance are the basis of theoretical considerations. The second part presents the result of a study, the objective of which was to build an algorithm allowing the prediction of the WIG20 index rates of return based on text data. The test sample consisted of 901 papers published in the “Parkiet” magazine and 748 daily rates of return. The study was conducted using algorithms processing natural language and decision trees used for classification. The results of the study allowed an indication of a model in which the precision and accuracy indicators exceeded a score of 50%
PL
Celem artykułu jest wskazanie możliwości wykorzystania analizy tekstu do badań nad dynamiką polskiego rynku kapitałowego. W pierwszej części artykułu opisano zmiany, jakie w ostatnich latach nastąpiły na rynku danych, oraz ich wpływ na dyskontowanie informacji przez inwestorów giełdowych. U podstaw teoretycznych rozważań leży klasyczna hipoteza efektywności informacyjnej oraz paradygmat finansów behawioralnych. W drugiej części artykułu przedstawiono wyniki badania, którego celem było zbudowanie algorytmu umożliwiającego przewidywanie stóp zwrotu indeksu WIG20 na podstawie danych tekstowych. Próba badawcza składa się z 901 artykułów czasopisma Parkiet, które zawierały w tytułach sformułowanie „WIG20”, oraz 748 dziennych stóp zwrotu. Badanie przeprowadzono z wykorzystaniem algorytmów przetwarzania języka naturalnego oraz drzew decyzyjnych. Wskazano model, którego wskaźniki precyzji i dokładności przekraczały 50%.

Year

Issue

Pages

69-79

Physical description

References

  • Boudoukh, J., Feldman, R., Kogan, S., and Richardson, M. (2013). Which news moves stock prices? A Textual Analysis (NBER Working Paper Series, 18725), 1-45.
  • Bukovina, J. (2016). Social media big data and capital markets – An overview. Journal of Behavioral and Experimental Finance, (11), 18-26.
  • Butler, M., and Kešelj, V. (2009). Financial forecasting using character N-Gram analysis and readabili- ty scores of annual reports. In Y. Gao, and N. Japkowicz (Eds.), Advances in Artificial Intelligence (pp. 39-51). Canadian AI 2009. Lecture Notes in Computer Science, 5549. Berlin, Heidelberg: Springer.
  • Chan, S. W. K., and Chong, M. W. C. (2017). Sentiment analysis in financial texts. Decision Support Systems, (94), 53-64.
  • Cavanillas, J. M., Curry E., and Wolfgang W. (Eds.). (2016). New horizons for a data-driven economy. A roadmap for usage and exploitation of Big Data in Europe. Springer Open, Switzerland.
  • Chen, H., De, P., Hu, Y., and Hwang, B.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27(5), 1367-1403.
  • Chun, S. H., and Kim S. H. (2004). Data mining for financial prediction and trading: Application to single and multiple markets. Expert Systems with Applications, 26(2), 131-39.
  • Das, S. R. (2014). Text and context: Language analytics in Finance. Foundations and Trends® in Fi- nance, (8), 145-261.
  • De Oliveira, F.A., Nobre, C. N., and Zárate, L. E. (2013). Applying Artificial Neural Networks to pre- diction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras, Brazil, Expert Systems with Applications, 40(18), 7596-7606.
  • Dougal, C., Engelberg, J., García, D., and Parsons, C. A. (2012). Journalists and the stock market. Review of Financial Studies, 25(3), 640-679.
  • Dzielinski, M., and Hasseltoft, H. (2012). Aggregate news tone, stock returns, and volatility. SSRN Electronic Journal. (Working Paper), University of Zurich.
  • Fama, E. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575-1617.
  • Feng, L. (2010). Textual analysis of corporate disclosures: A survey of the literature. Journal of Accounting Literature, (29), 143-165.
  • Feuerriegel, S., and Gordon, J. (2018). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, (112), 88-97.
  • Groth, S. S., and Muntermann, J. (2011). An intraday market risk management approach based on textual analysis. Decision Support Systems, 50(4), 680-691.
  • Guresen, E., Kayakutlu, G., and Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
  • Heston, S. L., and Sinha N. R. (2016). News versus sentiment: Predicting stock returns from news stories. Finance and Economics Discussion Series, 1-35.
  • ISI Emerging Markets. (n.d.). Emerging Markets Information Service. Retrieved August 8, 2019 from www.emis.com/pl
  • Kara, Y., Boyacioglu, M., and Baykan, Ö. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319.
  • Kavšek, B. (2017). Using words from daily news headlines to predict the movement of stock market indices. Managing Global Transitions, 15(2), 109-121.
  • Kearney, C., and Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, (33), 171-185.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Pepato, C., and Micheletti, G. (2019). Second Interim Report The European Data Market Monitoring Tool: Key Facts & Figures, First Policy Conclusions, Data Landscape and Quantified Stories, IDC Italia srl, The Lisbon Council. Retrieved from https://datalandscape.eu/sites/default/files/report/D2.6_EDM_Second_Interim_Report_28.06.2019.pdf
  • Prajsna, P., and Sawa, M. (2018). Global Data Market Size 2017-2019, OnAudience. Retrieved from https://www.onaudience.com/files/OnAudience.com_Global_Data_Market_Size_2017-2019.pdf Reinsel, D., Gantz, J., and Rydning, J. (2017). Data Age 2025: The evolution of data to life-critical don’t focus on Big Data. Focus on the data that’s big. IDC White Paper.
  • Rostek, K., and Młodzianowski, P. (2017). Współzależność informacji sieciowych oraz zmian indeksów zachodzących na Giełdzie Papierów Wartościowych w Warszawie. Zeszyty Naukowe Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach, 42(15), 249-263.
  • Saloni, Z., Woliński, M., Wołosz, R., Gruszczyński, W., and Skowrońska, D. (2012). Słownik gramatyczny języka polskiego. Retrieved August 8, 2019 from http://sgjp.pl
  • Stowarzyszenie Inwestorów Indywidualnych. (2018). Czy polscy inwestorzy zapiszą się do PPK? Wyniki Ogólnopolskiego Badania inwestorów 2018. Retrieved August 8, 2019 from https://www.sii.org.pl
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139-1168.
  • Tetlock, P. C., Saar-Tsechansky, M., and Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance, LXIII(3), 1437-1467.
  • Wuthrich, B., Cho, V., Leung, S., and Zhang, J. (1998). Daily stock market forecast from textual web data.
  • IEEE International Conference on Systems, Man, and Cybernetics, Conference Proceedings, 1-6. Zubair, S., and Cios, K. J. (2015). Extracting news sentiment and establishing its relationship with the S&P500 Index. 48th Hawaii International Conference on System Sciences, Conference Proceedings, 969-975.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-52c63883-defa-4361-8eb6-18520ab9eb25
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