Abstract
This paper introduces a new data set for use in the financial prediction domain, that of quantified News Sentiment. This data is automatically generated in real time from the Dow Jones network with news stories being classified as either Positive, Negative or Neutral in relation to a particular market or sector of interest.
We show that with careful consideration to fitness function and data representation, GP can be used effectively to find non-linear solutions for predicting large intraday price jumps on the S&P 500 up to an hour before they occur. The results show that GP was successfully able to predict stock price movement using these news alone, that is, without access to even current market price.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10, 215–236 (1994/1996)
Kohzadi, N., Boyd, M.S., Kermanshahi, B., Kaastra, I.: A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing 10, 169–181 (1993/1996)
Yao, J., Tan, C.L.: A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 34, 79–98 (1997/2000)
Yao, J., Tan, C.L., Poh, H.: Neural Networks for technical analysis: a study on klci. International Journal of Theoretical and Applied Finance 2(2), 221–241(1998/1999)
Kaboudan, M.A.: Genetic Programming Prediction of Stock Prices. Computational Economics 16, 207–236 (1999/2000)
Becker, Y., Fei, P., Lester, A.M.: Stock Selection - An Innovative application of Genetic Programming Methodology’. In: US Active Equity Research, State Street Global Advisers,
Samanta, LeBaron.: Extreme Value Theory and Fat Tails in Equity Markets. In: Computing in Economics and Finance 140, Society for Computational Economics (2005)
Vukic, A.: Intraday Public Information The Frence Evidence Thesis (PhD). University of Fribourg (2004)
Hodrick and PrescotHodrick, Robert, and E.C. Prescott, Postwar U.S. Business Cycles: An Empirical Investiga-tion, Journal of Money, Credit, and Banking (1997)
Lawrenz, C., Westerhoff, D.: Modeling Exchange Rate Behavior with a Genetic Algorithm. Computational Economics 21, 209–229 (2000/2003)
Fama, E.: Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25, 383–417 (1970)
Kelly Jr, J.L.: A New Interpretation of Information Rate, Bell System Technical Journal, vol. 35, pp. 917–926 (1956)
Wilmott, P.: Paul Wilmott Introduces Quantitative Finance, 2nd edn. Wiley, John & Sons, Chichester (2006)
Tetlock, P.C.: Giving Content to Investor Sentiment: The Role of Media in the Stock Market. Journal of Finance 62, 1139–1168 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Larkin, F., Ryan, C. (2008). Good News: Using News Feeds with Genetic Programming to Predict Stock Prices. In: O’Neill, M., et al. Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78671-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-78671-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78670-2
Online ISBN: 978-3-540-78671-9
eBook Packages: Computer ScienceComputer Science (R0)