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A dynamic scenario‐driven technique for stock price prediction and trading

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  • Yash Thesia
  • Vidhey Oza
  • Priyank Thakkar

Abstract

It has always been a challenge to accurately forecast the behavior of a stock market due to its extremely nonlinear and dynamic nature. Numerous studies have shown that technical indicators describing stocks in conjunction with machine learning models can serve as useful tools for forecasting in the stock market. There are various challenges, and one of them is the choice of the right technical indicators and prediction models. It is believed that there is no optimal set of technical indicators that work well in all market scenarios in a dynamic environment such as the stock market. The statement also applies to different prediction models. There is no definite winner, and different settings can emerge as winners in different market scenarios. On this premise, we propose DSdT: a dynamic scenario‐driven technique for stock price prediction and trading strategy enhancement. The proposed novel technique uses the scenario recognition and integration module to identify and integrate the current market scenario into the forecasting pipeline, resulting in a scenario‐driven stock price prediction. We use a large set of technical indicators and a shallow neural network equipped with a gating mechanism to capture and integrate the current market scenario in the prediction process. Experiments are performed on 11 stocks of the Indian Stock Market. The proposed approach yields mean absolute percentage error (MAPE) of 1.67% compared with 2.4% of its closest nonscenario‐driven counterpart for the next day's stock price prediction task. A trading strategy is also devised using the proposed technique, and the returns are compared with different baselines. Results show that the devised trading strategy yields an approximate average return of 54% compared with 25% of the return obtained by the nearest benchmark.

Suggested Citation

  • Yash Thesia & Vidhey Oza & Priyank Thakkar, 2022. "A dynamic scenario‐driven technique for stock price prediction and trading," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 653-674, April.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:3:p:653-674
    DOI: 10.1002/for.2848
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    References listed on IDEAS

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    1. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
    2. Guosheng Hu & Yuxin Hu & Kai Yang & Zehao Yu & Flood Sung & Zhihong Zhang & Fei Xie & Jianguo Liu & Neil Robertson & Timothy Hospedales & Qiangwei Miemie, 2017. "Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions," Papers 1709.03803, arXiv.org, revised Feb 2018.
    3. O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
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    1. Juanjuan Wang & Shujie Zhou & Wentong Liu & Lin Jiang, 2024. "An ensemble model for stock index prediction based on media attention and emotional causal inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1998-2020, September.

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