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A Hybrid Machine Learning System For Stock Market Forecasting · Study

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ArticleA Hybrid Machine Learning System For Stock Market Forecasting

Rohit Choudhry2008

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A Hybrid Machine Learning System For Stock Market Forecasting

Rohit Choudhry · Kumkum Garg

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system.

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References

2003Financial time series forecasting using support vector machines 2001Application of support vector machines in financial time series forecasting 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 2002Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index 2001Financial Forecasting Using Support Vector Machines 2006A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets 1998Forecasting S&P 500 stock index futures with a hybrid AI system 2000Genetic Programming Prediction of Stock Prices

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Details

Year2008

Typearticle

Published2008-03-24

Languageen

Citations161

Files0 public / 0

ShelfStock Market Forecasting Methods

Identifiers

doi10.5281/zenodo.1071851

openalex_workW181010784

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