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A Comparative Predicting Stock Prices using Heston and Geometric Brownian Motion Models

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  • H. T. Shehzad
  • M. A. Anwar
  • M. Razzaq

Abstract

This paper presents a novel approach to predicting stock prices using technical analysis. By utilizing Ito's lemma and Euler-Maruyama methods, the researchers develop Heston and Geometric Brownian Motion models that take into account volatility, interest rate, and historical stock prices to generate predictions. The results of the study demonstrate that these models are effective in accurately predicting stock prices and outperform commonly used statistical indicators. The authors conclude that this technical analysis-based method offers a promising solution for stock market prediction.

Suggested Citation

  • H. T. Shehzad & M. A. Anwar & M. Razzaq, 2023. "A Comparative Predicting Stock Prices using Heston and Geometric Brownian Motion Models," Papers 2302.07796, arXiv.org.
    • Handle: RePEc:arx:papers:2302.07796
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    References listed on IDEAS

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    3. Arunangshu Biswas & Anindya Goswami & Ludger Overbeck, 2017. "Option Pricing in a Regime Switching Stochastic Volatility Model," Papers 1707.01237, arXiv.org, revised Jan 2018.
    4. Yanhong Zhong & Guohe Deng, 2019. "Geometric Asian Options Pricing under the Double Heston Stochastic Volatility Model with Stochastic Interest Rate," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    5. Shankhyajyoti De & Arabin Kumar Dey & Deepak Gauda, 2020. "Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network," Papers 2007.00254, arXiv.org.
    6. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
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    Cited by:

    1. Amit K. Sinha, 2024. "Obtaining Accurate Gold Prices," Commodities, MDPI, vol. 3(1), pages 1-12, March.

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