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.
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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
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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
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