Comparison of Financial Models for Stock Price Prediction

Mohammad Rafiqul Islam and Nguyet Nguyen
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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

JRFM, 2020, vol. 13, issue 8, 1-19

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.

Keywords: stock price prediction; auto-regressive integrated moving average; artificial neural network; stochastic process-geometric Brownian motion; financial models (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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