•  
  •  
 
Business Review

Author ORCID Identifier

Rabia Sabri ORCID: 0000-0002-9135-2137

Abstract

This research is focused on the stock exchanges of Sri Lanka, Pakistan, and Bangladesh due to their significant presence in the Asian region and the unique challenges, opportunities, and presence in these frontier markets. The study assesses both traditional stationary models, Autoregressive Integrated Moving Average (ARIMA) and Theta traditional stationary models, with the contemporary deep learning models Long Short-Term Memory (LSTM) with 1D Convolutional Neural Network (CNN) support. The study covers the sample period of historical data from 2020–2023 to include the in and out of sample forecasting of 2024–2025. The time series comprises linear and nonlinear datasets to capture a wider range of factors influencing the market. The evaluation metrics are used to balance the prediction model's accuracy and the intricate dynamics of the markets. The conventional time series models hold the advantages of their interpretability and computational efficiency, but the combined effect of CNN-LSTM exhibits significantly superior accuracy in predictions. Integrating advanced techniques with traditional statistical methods provides a more comprehensive and accurate forecast to capture the complex Intricacies of stock indices. The ensembling model approach can improve predictive performance and help stabilize the market ecosystem.

Keywords

Stock Market forecasting, Frontier market, univariate, multivariate, ARIMA, Theta Deep learning models, Ensemble Learning Techniques

DOI

10.54784/1990-6587.1656

Journal of Economic Literature Subject Codes

C32, C45, C52, C53, D53, G15, G17

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Published Online

November 11, 2024

Share

COinS

Publication Stage

Online First

Submitted

28-05-2024

Revised

17-07-2024

Published

08-11-2024

 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.