A Hybrid Machine Learning System for Stock Market Forecasting

Authors

Lokesh Kumar, Babu Banarasi Das National Institute of Technology and Management
Anvita Pandey, Babu Banarasi Das National Institute of Technology and Management
Saakshi Srivastava, Babu Banarasi Das National Institute of Technology and Management
Manuj Darbari, Babu Banarasi Das National Institute of Technology and Management

Document Type

Article

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.

Recommended Citation

Kumar, Lokesh; Pandey, Anvita; Srivastava, Saakshi; and Darbari, Manuj (2011) "A Hybrid Machine Learning System for Stock Market Forecasting," Journal of International Technology and Information Management: Vol. 20: Iss. 1, Article 3.
DOI: https://doi.org/10.58729/1941-6679.1099
Available at: https://scholarworks.lib.csusb.edu/jitim/vol20/iss1/3