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Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique

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Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics (ICETCE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 985))

Abstract

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 33 different combinations of technical indicators to predict the stock prices. The paper has two objectives, first is the technical indicator feature selection and identification of the relevant technical indicators by using Boruta feature selection technique. The second objective is an accurate prediction model for stocks. To predict stock prices we have proposed ANN (Artificial Neural Network) Regression prediction model and model performance is evaluated using metrics is Mean absolute error (MAE) and Root mean square error (RMSE). The experimental results are better than the existing method by decreasing the error rate in the prediction to 12%. We have used the National Stock Exchange, India (NSE) data for the experiment.

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Acknowledgment

This work has been supported by the Visvesvaraya Ph.D Scheme for Electronics and IT (Media Lab Asia), the departments of MeitY, Government of India. The Task carried out at the Department of Information Technology, NITK Surathkal, Mangalore, India.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology, Karnataka, Surathkal, India

    Nagaraj Naik & Biju R. Mohan

Authors
  1. Nagaraj Naik
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  2. Biju R. Mohan
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Corresponding author

Correspondence to Nagaraj Naik .

Editor information

Editors and Affiliations

  1. College of Engineering, Iowa State University, Ames, IA, USA

    Arun K. Somani

  2. National University of Singapore, Singapore, Singapore

    Seeram Ramakrishna

  3. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Anil Chaudhary

  4. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Chothmal Choudhary

  5. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Basant Agarwal

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Naik, N., Mohan, B.R. (2019). Optimal Feature Selection of Technical Indicator and Stock Prediction Using Machine Learning Technique. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_22

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  • DOI: https://doi.org/10.1007/978-981-13-8300-7_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8299-4

  • Online ISBN: 978-981-13-8300-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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