The relationship between South Asian stock returns and macroeconomic variables

Khan, Muhammad N.; Tantisantiwong, Nongnuch; Fifield, Suzanne; Power, David

doi:10.1080/00036846.2014.995360

Cited by 17 publications

(14 citation statements)

References 44 publications

(24 reference statements)

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“…However, industrial production, interest rates, and crude oil prices are indisputably three of the most commonly used macroeconomic indicators in the empirical investigation, and these same macro variables are utilized in the present study to examine their influence on the U.S. stock prices. Researchers like Khan et al (2015) argued for the importance of considering not just domestic macroeconomic variables but also global factors especially as economies like the U.S. become more integrated internationally. Consistent with this argument, industrial production and interest rates represent the U.S. domestic macroeconomic factors while the crude oil price constitutes a global factor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A large body of empirical work (including Humpe & Macmillan, 2009; Khan, Tantisantiwong, & Fifield, 2015; Patra & Poshakwale, 2006; Ratanapakorn & Sharma, 2007; Salisu, Swaray, & Oloko, 2019) have followed the pioneer work of Fama (1981) and Chen, Roll, and Ross (1986) to examine the behaviour of the stock market and its influence by a wide variety of economic variables, such as industrial output, interest rates, crude oil prices and inflation in both advanced and less developed nations. Much of the existing body of literature which investigates this relationship has employed standard linear econometric methods.…”

Section: Introductionmentioning

confidence: 99%

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U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

2020

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This paper explores the long‐run relationship and the associated short‐run dynamics between the U.S. stock market and three major macroeconomic fundamentals, namely the U.S. industrial production index, the U.S. 10‐year Treasury bond yield and the West Texas Intermediate oil price, for the time period covering 1985–2015. The quantile autoregressive distributed lag (QARDL) model presented by Cho et al. (2015) Journal of Econometrics, 188, 281–300, which combines the autoregressive distributed lag model of Pesaran and Shin (1998), Cambridge University Press, and Pesaran et al. (2001) Journal of Applied Econometrics, 16, 289–326, and the quantile regression methodology of Koenker and Bassett (1978), Econometrica, 46, 33–50, is used. This approach analyses the links among the variables over a range of quantiles which represent different states of the equity market. The findings show a significant quantile‐varying cointegration relationship between U.S. equity prices and the three selected macroeconomic factors. The U.S. industrial production index is found to have the largest long‐run influence on the equity market. These findings highlight the importance of market players as well as policymakers to develop a greater understanding and awareness of how macro variables impact stock prices under different stock market conditions, particularly in relation to optimal asset allocation and risk management strategies, among others.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

2020

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“…The outcome of this study presents that growth, inflation, and employment affects the total economic policy positively and which ultimately affects the stock return in the US market. Khan, Tantisantiwong, Fifield, and Power (2015) South Asian Countries (Bangladesh, India, Sri Lanka, Pakistan) This study finds that interest rate and inflation rate have a significant effect on Pakistan. For Bangladesh, regional economic activities can explain the stock return.…”

Section: Bangladeshmentioning

confidence: 81%

“…Then, Linear ARDL Bound testing has conducted to get the evidence of having a long-run relationship among the variables and to determine, which variable can consider as the best-fitted dependent variable. After that, lag length has been selected for each of the variables and followed by it, the main ARDL test has been done to find the long-run relationship among the volatility and particular variable (Khan et al, 2015;Raza, Jawaid, Afshan, & Abd Karim, 2015). After testing the long-run effect, the short-run study has conducted.…”

Section: Discussionmentioning

confidence: 99%

Nexus between Remittance, Nonperforming Loan, Money Supply, and Financial Volatility: An Application of ARDL

Qamruzzaman

Bardhan

Nasya

2020

International Journal of Applied Economics, Finance and Account

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The motivation of the study is to investigate the nexus between Remittance, Nonperforming loan, Money supply, and finical volatility for the period from 1976 to 2017. To do so, study apply unit root test for determining variables order of integration, Lag length criteria test to select optimal lag for empirical test and long-run cointegration test i.e. Autoregressive Distributed Lagged (ARDL). Referring to unit root tests, it is apparent that variables established integration in mixed order i.e. few variables are stationary at level and few variable are at first difference. The results of Bound testing ascertain that the presence of long-run association between financial volatility and selected macro fundaments. Referring to the long-term model estimation, it is palpable that magnitudes from macro fundaments to financial volatility established that money supply in the economy is responsible for financial instability on the other hand, remittance and financial development play critical role in maintaining financial stability in the economy.

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“…A study, conducted in 2015 in Indian, Bangladesh, Pakistan, and Sri Lanka using VAR and Principal Components Analysis (PCA) revealed that economic factors could not explain stock returns in India. However, economic activity directly explains stock returns of Bangladesh and indirectly explains stock returns of Pakistan and Sri Lanka (Khan et al, 2015).…”

Section: Economic Variables and Stock Marketmentioning

confidence: 99%

Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction

Nti

Adekoya

Weyori

2019

American Journal of Applied Sciences

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A firm's equity price on the stock-market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to examine the association between MVs and stock-price have carried out numerous studies, using time-series statistical analysis methods like Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH). However, these techniques are reported to suffer from limited predictive power and restrictive assumptions. Besides, in pursuit of ways to remedy these paucities and limitations within these techniques, some researchers have examined uncountable machine learning techniques for measuring the stock-markets trends and making trading decisions using macroeconomic variables. On the other hand, a higher percentage of these studies paid attention to the stock index prediction and neglected the diversity of MVs that influence different sector indices. In addressing the issues above, this study seeks to examine the degree of significance between different sectors stock-price and MVs and predict a 30-day head stock-price using Random Forest (RF) with an improve leave-one-out cross-validation tactic and Long Short-Term Memory Recurrent Neural Network (LSTMRNN). An empirical analysis of the proposed model over the Ghana Stock Exchange (GSE) exhibits high prediction accuracy and better mean absolute error compared with other time-series techniques. It can, therefore, be inferred from the fallouts that the proposed stock-market prediction with MVs, provides an efficient approach to automatic identification and extraction of MVs that affect diverse sector stock and offer an accurate prediction of a stock's future price.

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The relationship between South Asian stock returns and macroeconomic variables

Cited by 17 publications

References 44 publications

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

Nexus between Remittance, Nonperforming Loan, Money Supply, and Financial Volatility: An Application of ARDL

Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction

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