Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques

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Author(s): Puneet Misra (University of Lucknow, Lucknow, India)and Siddharth Chaurasia (University of Lucknow, Lucknow, India)
Copyright: 2020
Volume: 13
Issue: 1
Pages: 20
Source title: Journal of Information Technology Research (JITR)
Editor(s)-in-Chief: Wen-Chen Hu (University of North Dakota, USA)
DOI: 10.4018/JITR.2020010109

Keywords: Information Resources Management / Information Science Reference / IT Research and Theory / Library & Information Science

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Abstract

Stock market movements are affected by numerous factors making it one of the most challenging problems for forecasting. This article attempts to predict the direction of movement of stock and stock indices. The study uses three classifiers - Artificial Neural Network, Random Forest and Support Vector Machine with four different representation of inputs. First representation uses raw data (open, high, low, close and volume), The second uses ten features in the form of technical indicators generated by use of technical analysis. The third and fourth portrayal presents two different ways of converting the indicator data into discrete trend data. Experimental results suggest that for raw data support vector machine provides the best results. For other representations, there is no clear winner regarding models applied, but portrayal of data by the proposed approach gave best overall results for all the models and financial series. Consistency of the results highlight the importance of feature generation and right representation of dataset to machine learning techniques.

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Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques

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