- title = "Good News: Using News Feeds with Genetic Programming to Predict Stock Prices",
- author = "Fiacc Larkin and Conor Ryan",
- bibdate = "2008-04-15",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#LarkinR08",
- booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008",
- address = "Naples",
- month = "26-28 " # mar,
- publisher = "Springer",
- year = "2008",
- volume = "4971",
- editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino",
- isbn13 = "978-3-540-78670-2",
- pages = "49--60",
- series = "Lecture Notes in Computer Science",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1007/978-3-540-78671-9_5",
- size = "12 pages",
-
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.",
- notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008",
