Forecasting stock market prices using mixed ARIMA model: a case study of Indian pharmaceutical companies

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to pre...

Full description

Saved in:
Bibliographic Details
Published in:Investment management & financial innovations 2021-01, Vol.18 (1), p.42-54
Main Authors: Kumar Meher, Bharat, Thonse Hawaldar, Iqbal, Spulbar, Cristi, Birau, Ramona
Format: Article
Language:English
Subjects:
Akaike Information Criterion
augmented Dickey-Fuller test
India
investment
Pharmaceutical industry
prediction
risk
Securities markets
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.
ISSN:1810-4967
1812-9358
DOI:10.21511/imfi.18(1).2021.04