Stock price prediction using genetic algorithms and evolution strategies

Sonal Sable · Upendra Singh

45 citations0/0 files storedInternational Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Evolutionary Algorithms and Applications

Abstract

Stock market is a very challenging and an interesting field. In this paper, we are trying to predict the target prices of the stocks for the short term. We are predicting the target priceof script individually for eight different scripts. For each script, six attributes are used which help us to find, whether the prices are going up or down. The evolutionary techniques used for this experiment are the genetic algorithms and evolution strategies. By using these algorithms, we are trying to find the connection weight for each attribute, which helps us in predicting the target price of the stock. An input for each attribute is given to a sigmoid function after it is amplified based on its connection weight. The experimental results show that using this approach, predicting the stock price is promising. In each case, the algorithms were able to predict with an accuracy of at least 70.00.

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References

2003Financial time series forecasting using support vector machines 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2008Surveying stock market forecasting techniques – Part II: Soft computing methods 1973The Behavior of Stock Prices on Fridays and Mondays 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2009Cross-correlations between volume change and price change 2010Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches 2008Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach

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