Comparison of financial models for stock price prediction
This paper is about predicting the stock price using three different methods
Time series analysis of daily stock data and building predictive models are complicated. This project 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.
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BibTeX citation:
@article{rafiqul_islam2020,
author = {Rafiqul Islam, Mohammad and Nguyen, Nguyet},
publisher = {MDPI},
title = {Comparison of Financial Models for Stock Price Prediction},
journal = {Journal of Risk and Financial Management},
date = {2020-08-14},
url = {https://www.mdpi.com/1911-8074/13/8/181},
doi = {10.3390/jrfm13080181},
langid = {en}
}
For attribution, please cite this work as:
Rafiqul Islam, Mohammad, and Nguyet Nguyen. 2020. “Comparison of
Financial Models for Stock Price Prediction.” Journal of Risk
and Financial Management, August. https://doi.org/10.3390/jrfm13080181.