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Comparison of Financial Models for Stock Price Prediction

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  • Mohammad Rafiqul Islam

    (Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, USA)

  • Nguyet Nguyen

    (Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, USA)

Abstract

Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric Brownian motion. Each of the methods is used to build predictive models using historical stock data collected from Yahoo Finance. Finally, output from each of the models is compared to the actual stock price. Empirical results show that the conventional statistical model and the stochastic model provide better approximation for next-day stock price prediction compared to the neural network model.

Suggested Citation

  • Mohammad Rafiqul Islam & Nguyet Nguyen, 2020. "Comparison of Financial Models for Stock Price Prediction," JRFM, MDPI, vol. 13(8), pages 1-19, August.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:8:p:181-:d:399004
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    References listed on IDEAS

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    Cited by:

    1. Mohammed Bouasabah & Oshamah Ibrahim Khalaf, 2023. "A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(3), pages 1-13, September.
    2. Chuen Yik Kang & Chin Poo Lee & Kian Ming Lim, 2022. "Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit," Data, MDPI, vol. 7(11), pages 1-13, October.
    3. Shaswat Mohanty & Anirudh Vijay & Nandagopan Gopakumar, 2022. "StockBot: Using LSTMs to Predict Stock Prices," Papers 2207.06605, arXiv.org, revised Jul 2022.
    4. Aigner, Philipp, 2023. "Identifying scenarios for the own risk and solvency assessment of insurance companies," ICIR Working Paper Series 48/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).

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