Is Indian Stock Market Related with Exchange Rate and Inflation? An Empirical Test Using Time Series

2008 ◽  
Author(s):  
Paritosh Kumar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Empirical Test ◽  
Indian Stock Market

scholarly journals A Time Series Analysis of the Nexus Between Macroeconomic Fundamentals and Stock Prices in Nigeria

2018 ◽  
Vol 13 (2) ◽  
pp. 69-91
Author(s):  
Amassoma Ditimi ◽  
Bolarinwa Ifeoluwa
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Macroeconomic Factors ◽  
Series Analysis ◽  
Integration Test ◽  
Macroeconomic Fundamentals ◽  
The Government

AbstractSince macroeconomic fundamentals have been found to play a vital role for changes in the economy of a country. Consequently, the onus is on the appropriate regulatory authorities to take measures in making amendments in these policies to put the economy on the right development track. The aim of this study is to use time series analysis to empirically showcase the nexus between macroeconomic fundamentals and stock prices in Nigeria. The method used for this study was the Co-integration test and the EGARCH technique to estimate the possible influence of the selected macroeconomic fundamentals on stock prices. Volatility was captured by using quarterly data and estimated using GARCH (1,1) respectively. The study found there is a positive relationship between macroeconomic factors and stock prices in Nigeria. Therefore, the study recommends that the Federal authority should put in place policy measures that will enable the exchange rate to be relatively stabilized. This is because empirical evidence from studies has shown that exchange rate affects stock market prices. In addition, the government authority should ensure an enabling environment that would build the mindset of institutional investors in the Nigerian stock market due to the existence of information asymmetry problems among potential investors.

scholarly journals Optimizing LSTM for time series prediction in Indian stock market

2020 ◽  
Vol 167 ◽  
pp. 2091-2100 ◽  
Author(s):  
Anita Yadav ◽  
C K Jha ◽  
Aditi Sharan
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Prediction ◽  
Indian Stock Market

Examination of Long Memory in Indian Stock Market: A Sectoral Juxtaposition

2021 ◽  
pp. 231971452110402
Author(s):  
Ramashanti Naik ◽  
Y. V. Reddy
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Long Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Long Range Dependence ◽  
Dynamic Nature ◽  
Portfolio Managers ◽  
Indian Stock Market ◽  
Data Frequency

One of the situations encountered in time series analysis is long-range dependence, also known as Long memory. We investigated the presence of long memory in the Indian sectoral indices returns and investigated whether the long memory behaviour is affected by the data frequency. We applied the autoregressive fractionally integrated moving average (ARFIMA) models to 13 sectoral indices of the National Stock Exchange of India and examined the long memory in daily, monthly and quarterly return series. The results indicate the persistence in daily return series and anti-persistence in monthly and quarterly return series. Thus, we conclude that the frequency of data does have a significant effect on the behaviour of long memory patterns. The results will be helpful for present and potential investors, institutional investors, portfolio managers and policymakers to understand the dynamic nature of long memory in the Indian stock market.

Multifractal detrended fluctuation analysis of SENSEX fluctuation in the Indian stock market

2010 ◽  
Vol 88 (8) ◽  
pp. 545-551 ◽  
Author(s):  
Srimonti Dutta
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Detrended Fluctuation Analysis ◽  
Nonstationary Time Series ◽  
Anomalous Behaviour ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Indian Stock Market ◽  
Detrended Fluctuation

The fluctuation of SENSEX in the Indian stock market for the period Jan 2003–Dec 2009 is studied using the multifractal detrended fluctuation analysis (MFDFA) approach. The effect of the fall in the stock market in 2008 is also investigated. The data exhibits that the nonstationary time series of SENSEX fluctuations are multifractal in nature. An increase in the degree of multifractality prior to the anomalous behaviour in the SENSEX values is also observed. The increase in the degree of correlation for the period 2007–2009 is also responsible for the meteoric rise and the catastrophic fall in the values of SENSEX.

Do Macroeconomic Variables Impact the Indian Stock Market?

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Khalid Ul Islam ◽  
Mohsina Habib
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Returns ◽  
Causal Relationship ◽  
Granger Causality ◽  
Oil Prices ◽  
Granger Causality Test ◽  
Causality Test ◽  
Macroeconomic Variables ◽  
Indian Stock Market

This paper is intended to study the impact of various macroeconomic variables on Indian stock market. Based on the Arbitrage Pricing Theory (APT) propounded by Ross in 1976 and various other studies, a number of macroeconomic variables including, inflation, industrial production, exchange rate, money supply, interest rate, and oil price have been identified to have a significant impact on the stock market. We have applied the multivariate extension of the classical linear regression model computed on Ordinary Least Squares method and Granger Causality test to re-establish the relationship between macroeconomic variables and stock returns over a period of 10 years from 2005 to 2015 using monthly observations. The results of this study show that only exchange rate has a significant negative impact on stock returns. The other macroeconomic variables are not significantly affecting stock returns, however, their impact is in accordance with the economic theory. The Granger Causality test reveals absence of any causal relationship between stock returns and macroeconomic variables, except in case of oil prices, where we find a unidirectional causal relationship running from stock returns to oil prices. However, the Granger Causality results should not be taken in the conventional meaning of causality, but results merely identifying precedence.

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