Application of ensemble of recurrent neural networks for forecasting of stock market sentiments
DOI:
https://doi.org/10.24136/eq.2018.001Keywords:
artificial intelligence, ensembles, sentiments, stock market, investors’ behaviorAbstract
Research background: Research and measurement of sentiments, and the integration of methods for sentiment analysis in forecasting models or trading strategies for financial markets are gaining increasing attention at present. The theories that claim it is difficult to predict the individual investor?s decision also claim that individual investors cause market instability due to their irrationality. The existing instability increases the need for scientific research.
Purpose of the article: This paper is dedicated to establishing a link between the individual investors? behavior, which is expressed as sentiments, and the market dynamic, and is evaluated in the stock market. This article hypothesizes that the dynamics in the market is unequivocally related to the individual investor?s sentiments, and that this relationship occurs when the sentiments are expressed strongly and are unlimited.
Methods: The research was carried out invoking the method of Evolino RNN-based prediction model. The data for the research from AAII (American Association of Individual Investors), an investor sentiment survey, were used. Stock indices and sentiments are forecasted separately before being combined as a single composition of distributions.
Findings & Value added: The novelty of this paper is the prediction of sentiments of individual investors using an Evolino RNN-based prediction model. The results of this paper should be seen not only as the prediction of the connection and composition of investors? sentiments and stock indices, but also as the research of the dynamic of individual investors? sentiments and indices.
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References
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Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3). doi: 10.1111/j.1540-6261.2004.00662.x.
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Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49. doi: 10.3386/w5926.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11. doi: 10.1016/j.jempfin.2002.12.001.
Butler, K. C., & Malaikah, S. J. (1992). Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia. Journal of Banking and Finance, 16(1). doi: 10.1016/0378-4266(92)90085-E.
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Heiden, S., Klein, C., & Zwergel, B. (2013). Beyond fundamentals: investor sentiment and exchange rate forecasting. European Financial Management, 19(3). doi: 10.1111/j.1468-036X.2010.00593.x.
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Ho, K-Y., & Wang, W. W. (2016). Predicting stock price movements with news sentiment: an artificial neural network approach. Artificial Neural Network Modelling. Studies in computational intelligence, 628. doi: 10.1007/978-3-319-28495-8_18.
Huang, C. L., & Goo, Y. J. (2008). Are happy investors likely to be overconfident? Emerging Markets Finance and Trade, 44(4). doi: 10.2753/REE1540-496X440403.
Jagongo, A., & Mutswenje, V. S. (2014). A survey of the factors influencing investment decisions: the case of individual investors at the NSE. International Journal of Humanities and Social Science, 4(4).
Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: heuristics and biases. Science, 185. doi: 10.1126/science.185.4157.1124.
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2). doi: 10.2307/1914185.
Kavussanos, M., & Dockery, E. (2001). A multivariate test for stock market efficiency: the case of ASE. Applied Financial Economics, 11(5). doi: 10.1080/09603100010013006.
Lucey, B. M., & Dowling, M. (2005). The role of feelings in investor decision making. Journal of Economic Surveys, 19(2). doi: 10.1111/j.0950-0804. 2005.00245.x.
Maghrebi, N., Holmes, M. J. & Oya, K. (2014). Financial instability and the short-term dynamics of volatility expectations. Applied Financial Economics, 24(6). doi: 10.1080/09603107.2014.881966.
Maknickas, A., & Maknickien?, N. (2012). Influence of data orthogonally: on the accuracy and stability of financial market predictions. 4th International Joint Conference on Computational Intelligence. 5?7 October, 2012, Barcelona, Spain.
Maknickiene, N., & Maknickas, A. (2013a). Financial market prediction system with Evolino neural network and Delphi method. Journal of business economics and management, 14(2). doi: 10.3846/16111699.2012.729532.
Maknickiene, N., & Maknickas, A. (2013b). Investigation of prediction capabilities using RNN ensembles. 5th International Joint Conference on Computational Intelligence.. 20?22 September 2013, Vilamoura, Portugal.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1).
Oanda (2017). Data services for businesses and investors. Retrieved from https://www.oanda.com/.
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26. doi:10.1007/s10489-006-0001-7.
Plakandaras, V., Papadimitriou, T., Gogas, P., & Diamantaras, K. (2015). Market sentiment and exchange rate directional forecasting. Algorithmic Finance, 4(1-2). doi: 10.3233/AF-150044.
Rechenthin, M., Street, W. N., & Srinivasan, P., (2013). Stock chatter: using stock sentiment to predict price direction. Algorithmic Finance, 2(3-4). doi: 10.3233/AF-13025.
Rutkauskas, A. V., & Ramanauskas, T. (2009). Building an artificial stock market populated by reinforcement-learning agents. Journal of Business Economics and Management, 4(10). doi: 10.3846/1611-1699.2009.10.329-341.
Ryu, D., Kim, H. Y., & Yang, H. (2017). Investor sentiment, trading behavior, and stock returns. Applied Economics Letters, 24. doi: 10.1080/13504851.2016. 1231890.
Sahi, S. K. (2012). Neurofinance and investment behaviour. Studies in Economics and Finance, 29(4). doi: 10.1108/10867371211266900.
Seo, S. W. & Kim, J. S. (2015). The information content of option-implied information for volatility forecasting with investor sentiment. Journal of Banking & Finance, 50. doi: 10.1016/j.jbankfin.2014.09.010.
Sharda R., Delen D., & Turban E. (2013). Business intelligence: a managerial perspective on analytics. Prentice Hall Press Upper Saddle River.
Sheila, C. D. (2010). Cognition, market sentiment and financial instability. Cambridge Journal of Economics, 35. doi: 10.1093/cje/beq029.
Shu, H. C., & Chang, J. H. (2015). Investor sentiment and financial market volatility. Journal of Behavioral Finance, 16(3). doi: 10.1080/15427560.2015. 1064930.
Simon, H. A. (1957). A behavioral model of rational choice. Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York, NY: Wiley.
Smales, L. A. (2017). The importance of fear: investor sentiment and stock market returns. Applied Economics, 49(34). doi: 10.1080/00036846.2016.1259754.
Wang, B., Huang, Y., Yuan, Z., & Li, X. (2016). A multi-granularity fuzzy computing model for sentiment classification of Chinese reviews. Journal of Intelligent & Fuzzy Systems, 30(3). doi: 10.3233/IFS-151853.
Wang, T., Gong, Q., Ren, W., Wang, Y., Luo, X., & Li, Q. (2013). A sentiment-based hybrid model for stock return forecasting. 17th Pacific Asia Conference on Information Systems. 18?22 of June, 2013, Jeju Island, Korea.
Wurgler, J. (2007). Investor sentiment data (annual and monthly). Retrieved from http://people.stern.nyu.edu/jwurgler/.
Yang, H., Ryu, D., & Ryu, D. (2017). Investor sentiment, asset returns and firm characteristics: evidence from the Korean Stock Market. Investment Analysts Journal, 46(2). doi: 10.1080/10293523.2016.1277850.
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3). doi: 10.1111/j.1540-6261.2004.00662.x.
Anusakumar, S. V., Ali, R., & Hooy, C. W. (2017). The effect of investor sentiment on stock returns: insight from emerging Asian markets. Asian Academy of Management Journal of Accounting and Finance, 13(1). doi: 10.21315/aamjaf2017.13.1.7.
Aboody, D., Hughes, J., & Liu, J. (2002). Measuring value relevance in a (possibly) inefficient market. Journal of accounting research, 40(4). doi: 10.1111/1475-679X.00078.
Baker, M., & J. Wurgler. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4). doi: 10.3386/w10449.
Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104. doi: 10.1016/j.jfineco.2011 .11.002.
Bank, M., & Brustbauer, J. (2014). Investor sentiment in financial markets. Retrieved from http://www.uibk.ac.at/ibf/mitarbeiter/bank/investor-sentiment-in-financial-markets.pdf.
Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49. doi: 10.3386/w5926.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11. doi: 10.1016/j.jempfin.2002.12.001.
Butler, K. C., & Malaikah, S. J. (1992). Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia. Journal of Banking and Finance, 16(1). doi: 10.1016/0378-4266(92)90085-E.
Cohen, J. D. (2005). The vulcanization of the human brain: a neural perspective on interactions between cognition and emotion. Journal of Economic Perspectives, 19(4). doi: 10.1257/089533005775196750.
Cosma, G., & Acampora, G. (2016). Neuro-fuzzy sentiment analysis for customer review rating prediction. Sentiment Analysis and Ontology Engineering: an environment of computational intelligence. Springer International. doi: 10.1007/978-3-319-30319-2_15.
Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2). doi: 10.1111/j.1540-6261.1970.tb00518.x.
Gallagher, L. A., & Taylor, M. P. (2002). Permanent and temporary components of stock prices: evidence from assessing macroeconomic shocks. Southern Economic Journal, 69(2). doi: 10.2307/1061676.
Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1. doi: 10.1146/annurev-statistics-062713-085831.
Gneiting, T., & Ranjan, R. (2013). Combining predictive distributions. Electronic Journal of Statistics, 7. doi: 10.1214/13-EJS823.
Haugen, R. A. (2001). The Inefficient Stock Market. Pearson.
Heiden, S., Klein, C., & Zwergel, B. (2013). Beyond fundamentals: investor sentiment and exchange rate forecasting. European Financial Management, 19(3). doi: 10.1111/j.1468-036X.2010.00593.x.
Heston, S. L., & Sinha, N. R. (2015). News versus sentiment: predicting stock returns from news stories. Robert H. Smith School Research Paper.
Ho, K-Y., & Wang, W. W. (2016). Predicting stock price movements with news sentiment: an artificial neural network approach. Artificial Neural Network Modelling. Studies in computational intelligence, 628. doi: 10.1007/978-3-319-28495-8_18.
Huang, C. L., & Goo, Y. J. (2008). Are happy investors likely to be overconfident? Emerging Markets Finance and Trade, 44(4). doi: 10.2753/REE1540-496X440403.
Jagongo, A., & Mutswenje, V. S. (2014). A survey of the factors influencing investment decisions: the case of individual investors at the NSE. International Journal of Humanities and Social Science, 4(4).
Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: heuristics and biases. Science, 185. doi: 10.1126/science.185.4157.1124.
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2). doi: 10.2307/1914185.
Kavussanos, M., & Dockery, E. (2001). A multivariate test for stock market efficiency: the case of ASE. Applied Financial Economics, 11(5). doi: 10.1080/09603100010013006.
Lucey, B. M., & Dowling, M. (2005). The role of feelings in investor decision making. Journal of Economic Surveys, 19(2). doi: 10.1111/j.0950-0804. 2005.00245.x.
Maghrebi, N., Holmes, M. J. & Oya, K. (2014). Financial instability and the short-term dynamics of volatility expectations. Applied Financial Economics, 24(6). doi: 10.1080/09603107.2014.881966.
Maknickas, A., & Maknickien?, N. (2012). Influence of data orthogonally: on the accuracy and stability of financial market predictions. 4th International Joint Conference on Computational Intelligence. 5?7 October, 2012, Barcelona, Spain.
Maknickiene, N., & Maknickas, A. (2013a). Financial market prediction system with Evolino neural network and Delphi method. Journal of business economics and management, 14(2). doi: 10.3846/16111699.2012.729532.
Maknickiene, N., & Maknickas, A. (2013b). Investigation of prediction capabilities using RNN ensembles. 5th International Joint Conference on Computational Intelligence.. 20?22 September 2013, Vilamoura, Portugal.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1).
Oanda (2017). Data services for businesses and investors. Retrieved from https://www.oanda.com/.
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26. doi:10.1007/s10489-006-0001-7.
Plakandaras, V., Papadimitriou, T., Gogas, P., & Diamantaras, K. (2015). Market sentiment and exchange rate directional forecasting. Algorithmic Finance, 4(1-2). doi: 10.3233/AF-150044.
Rechenthin, M., Street, W. N., & Srinivasan, P., (2013). Stock chatter: using stock sentiment to predict price direction. Algorithmic Finance, 2(3-4). doi: 10.3233/AF-13025.
Rutkauskas, A. V., & Ramanauskas, T. (2009). Building an artificial stock market populated by reinforcement-learning agents. Journal of Business Economics and Management, 4(10). doi: 10.3846/1611-1699.2009.10.329-341.
Ryu, D., Kim, H. Y., & Yang, H. (2017). Investor sentiment, trading behavior, and stock returns. Applied Economics Letters, 24. doi: 10.1080/13504851.2016. 1231890.
Sahi, S. K. (2012). Neurofinance and investment behaviour. Studies in Economics and Finance, 29(4). doi: 10.1108/10867371211266900.
Seo, S. W. & Kim, J. S. (2015). The information content of option-implied information for volatility forecasting with investor sentiment. Journal of Banking & Finance, 50. doi: 10.1016/j.jbankfin.2014.09.010.
Sharda R., Delen D., & Turban E. (2013). Business intelligence: a managerial perspective on analytics. Prentice Hall Press Upper Saddle River.
Sheila, C. D. (2010). Cognition, market sentiment and financial instability. Cambridge Journal of Economics, 35. doi: 10.1093/cje/beq029.
Shu, H. C., & Chang, J. H. (2015). Investor sentiment and financial market volatility. Journal of Behavioral Finance, 16(3). doi: 10.1080/15427560.2015. 1064930.
Simon, H. A. (1957). A behavioral model of rational choice. Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York, NY: Wiley.
Smales, L. A. (2017). The importance of fear: investor sentiment and stock market returns. Applied Economics, 49(34). doi: 10.1080/00036846.2016.1259754.
Wang, B., Huang, Y., Yuan, Z., & Li, X. (2016). A multi-granularity fuzzy computing model for sentiment classification of Chinese reviews. Journal of Intelligent & Fuzzy Systems, 30(3). doi: 10.3233/IFS-151853.
Wang, T., Gong, Q., Ren, W., Wang, Y., Luo, X., & Li, Q. (2013). A sentiment-based hybrid model for stock return forecasting. 17th Pacific Asia Conference on Information Systems. 18?22 of June, 2013, Jeju Island, Korea.
Wurgler, J. (2007). Investor sentiment data (annual and monthly). Retrieved from http://people.stern.nyu.edu/jwurgler/.
Yang, H., Ryu, D., & Ryu, D. (2017). Investor sentiment, asset returns and firm characteristics: evidence from the Korean Stock Market. Investment Analysts Journal, 46(2). doi: 10.1080/10293523.2016.1277850.
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Published
2018-03-31
How to Cite
Maknickiene, N., Lapinskaite, I., & Maknickas, A. (2018). Application of ensemble of recurrent neural networks for forecasting of stock market sentiments. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(1), 7–27. https://doi.org/10.24136/eq.2018.001
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