A Hybrid Machine Learning System for Stock Market Forecasting

Lokesh Kumar · Saakshi Srivastava · Manuj Darbari

48 citations0/0 files storedJournal of international technology and information management

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

A hybrid machine learning system based on Genetic Algorithm (GA) and Time Series Analysis is proposed. In stock market, a technical trading rule is a popular tool for analysts and users to do their research and decide to buy or sell their shares. The key issue for the success of a trading rule is the selection of values for all parameters and their combinations. However, the range of parameters can vary in a large domain, so it is difficult for users to find the best parameter combination. In this paper, we present the Genetic Algorithm (GA) to overcome the problem in two steps. First, setting a sub-domain of the parameters with GA. Second, finding a near optimal value in the sub domain with GA and Time Series Analysis in a very reasonable time.

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2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 1990Stock market prediction system with modular neural networks 1997Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach 2002Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index 1990Stock price pattern recognition-a recurrent neural network approach 1996“Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach

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2016Prediction of Stock Market using Data Mining and Artificial Intelligence

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