SEARCH ARTICLE

13 Pages : 118-127

http://dx.doi.org/10.31703/gssr.2020(V-III).13      10.31703/gssr.2020(V-III).13      Published : Sep 2020

Status Quo, Monetary Loss-Aversion and Forecasting - An Approach to Investment During Amygdala Damages & Asymmetry

    The research essay aims to understand investor's ability to forecast having the perception of status quo and monetary loss-aversion in the situation of amygdala damages and asymmetry during decisions regarding stock's investment and use of several techniques to make efficient investment decisions based on optimal forecasting. The objectives of this study are to inquire about the irrationalities in investors at the time of stock's investment, having status quo and monetary loss-averse bias of investors at the time of amygdala damages and asymmetry and find-out the ways to deal with these situations. A qualitative research style was used for data collection for the subject study. Partially-organized discussions were arranged to get information in detail. A sample of 15 experienced stock marketers and brokers and 35 investors from the Pakistan stock exchange were selected for this study. This inquiry found the definite type of edgy and biased investor's attitude in the market and also found their solutions. This study perceptibly peaks the ways to deal with stress and biasness through optimal forecasting techniques and some other suggestions.

    Status Quo, Monetary Loss-Aversion, Forecasting, Investment
    (1) Muhammad Awais
    Assistant Professor, Department of Economics & Finance, Foundation University Islamabad, Pakistan.
    (2) Sadaf Kashif
    Assistant Professor, Department of Business Administration, Iqra University Islamabad, Pakistan.
    (3) Asif Raza
    Head of Business Operations, DPL (Pvt) Ltd. Islamabad, Pakistan

07 Pages : 64-71

http://dx.doi.org/10.31703/gssr.2024(IX-IV).07      10.31703/gssr.2024(IX-IV).07      Published : Dec 2024

AI-Powered Decomposition Techniques for Economic Forecasting

    Time series analysis and decomposition are crucial in examining economic data as they uncover elements such as trends, and seasonal influences, within the data. However, some approaches have difficulty in accommodating complex, high-dimensional data. In this research, we investigate the possibilities of utilizing artificial intelligence (AI) tools, specifically, machine learning (ML) and deep learning (DL) for better timeliness and accuracy of economic forecasting. In some instances, it was shown how recent AI models can improve the data analysis of economic indicators (GDP, inflation, stock indices) through the accurate depiction of non-linear trends and changing seasonals. Model enhancements using AI also result in significant improvement in the accuracy of economic forecasts and provide more detailed and useful time series decomposition for economists and policymakers. This paper is a step towards more extensive use of artificial intelligence in econometric analysis and provides evidence on the feasibility of such in practical econometric studies.

    Time Series Decomposition, Artificial Intelligence, Machine Learning, Deep Learning, Economic Forecasting
    (1) Afzal Mahmood
    Assistant Professor, Institute of Management Sciences (Pak AIMS) Lahore, Punjab, Pakistan.
    (2) Asmat N. Khattak
    Associate Professor, Head of Department of Management Sciences, Institute of Management Sciences (Pak AIMS) Lahore, Punjab, Pakistan.
    (3) Kanwal Zahra
    Associate Professor, Head of Department, Business School, University of Central Punjab, Lahore, Punjab, Pakistan.