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A Novel Multi-day Ahead Index Price Forecast Using Multi-output-Based Deep Learning System

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Soft Computing for Problem Solving

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 547))

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Abstract

Movement in the stock market or equity market can have unfathomable consequences on the economy and individual investors. A collapse in the stock market especially in the indexes has the potential to cause extensive economic disruption. Today’s smart AI has the capability to capture extreme fluctuations, irrational exuberance, and episodes of very high volatility. These sophisticated AI driven systems can detect such non-linearity with much-improved forecast results compared to conventional statistical methods. Prediction and analysis of index prices have greater importance in today’s economy. In this piece of work, we have experimented with three types of deep learning architectures, a simple feed-forward neural network (ANN), a long short-term memory network (LSTM), and a blend of convolutional neural networks with LSTMs (CNN-LSTMs). Along with open, high, low, close (OHLC) data, a set of 55 technical indicators have been considered based on their importance in technical analysis to predict the daily price for 5 different global indices. A random forest-based recursive feature elimination has also been used to obtain the most important technical indicators, and these results have been compared with all deep learning models for a horizon of 5 days ahead index price forecast.

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Acknowledgements

We would like to show our gratitude to Dr. Dileep A. D., Associate Professor School of Computing and Electrical Engineering, IIT Mandi and Dr. Manoj Thakur, Professor, School of Mathematical and Statistical Sciences, IIT Mandi, for sharing their pearls of wisdom with us during the course of this research.

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Authors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Debashis Sahoo & Kartik Sahoo

  2. University of Petroleum and Energy Studies, Dehradun, India

    Pravat Kumar Jena

Authors
  1. Debashis Sahoo
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  2. Kartik Sahoo
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  3. Pravat Kumar Jena
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Corresponding author

Correspondence to Debashis Sahoo .

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Editors and Affiliations

  1. School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Manoj Thakur

  2. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Samar Agnihotri

  3. School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Bharat Singh Rajpurohit

  4. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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Sahoo, D., Sahoo, K., Jena, P.K. (2023). A Novel Multi-day Ahead Index Price Forecast Using Multi-output-Based Deep Learning System. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_14

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