Open AccessStock Market Indices Direction Prediction: Time Series, Macro Economic Factors and Distributed Lag Models
Author(s) -
Divyaanshu Das
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41733
Subject(s) - econometrics , stock market , macro , stock market index , lag , distributed lag , time series , equity (law) , economics , stock exchange , predictive modelling , moving average , inflation (cosmology) , statistics , computer science , mathematics , finance , geography , computer network , context (archaeology) , physics , archaeology , theoretical physics , political science , law , programming language
A number of studies have been conducted to model the stock market indices using pure time series models or regression models based on macro economic variables. In this study, instead of focusing on modeling the actual levels of stock market indices I focus on predicting the direction (up/down) as investors who rely on technical analysis are more interested in the direction of stock market index than the actual prediction value. Therefore, in this study I look at best modelling approach for the direction prediction: time series (ARMA) or macro factor models or combination of both (ARDL). My study shows that macro factor models outperform for direction prediction as compared to ARMA or ARDL models. The study was performed on stock market direction prediction of stock indices of three South Asia countries: India, Pakistan and Malaysia. The macro economic factors that are considered for direction prediction are: Inflation, Unemployment and Exchange Rate monthly data from March 2016 to September 2021. Keywords: stock, equity, prediction, models, direction, ARMA, ARDL,macro