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

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

Puneet Misra, Siddharth Chaurasia
Copyright: © 2020 |Volume: 13 |Issue: 1 |Pages: 20
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781799805458|DOI: 10.4018/JITR.2020010109
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MLA

Misra, Puneet, and Siddharth Chaurasia. "Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques." JITR vol.13, no.1 2020: pp.130-149. http://doi.org/10.4018/JITR.2020010109

APA

Misra, P. & Chaurasia, S. (2020). Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques. Journal of Information Technology Research (JITR), 13(1), 130-149. http://doi.org/10.4018/JITR.2020010109

Chicago

Misra, Puneet, and Siddharth Chaurasia. "Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques," Journal of Information Technology Research (JITR) 13, no.1: 130-149. http://doi.org/10.4018/JITR.2020010109

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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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