Forecasting stock prices using Genetic Programming and Chance Discovery
AbstractIn recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 489.
Date of creation: 04 Jul 2006
Date of revision:
Forecasting; Chance discovery; Genetic programming; machine learning;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-CMP-2006-07-15 (Computational Economics)
- NEP-ETS-2006-07-15 (Econometric Time Series)
- NEP-FMK-2006-07-15 (Financial Markets)
- NEP-FOR-2006-07-15 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Edward Tsang & Sheri Markose & Hakan Er, 2005. "Chance Discovery In Stock Index Option And Futures Arbitrage," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., World Scientific Publishing Co. Pte. Ltd., vol. 1(03), pages 435-447.
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