The dynamic relationship between stock market returns and macroeconomic variables: An empirical study from Bangladesh

Akter, Shabiha; Rana, Md. Shohel; Anik, Tanvir Hasan

doi:10.31039/jomeino.2020.4.1.3

Cited by 6 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, India VIX and gold may act as a hedge against crude oil and Nifty respectively. Using Johansen's cointegration, Akter et al (2020) reported that the macroeconomic variables namely the Exchange rate, Index of Industrial production and reserve have significant cointegration with the Dhaka Stock Exchange (DSE). However, they did not find any significant long-run relationship between the chosen macroeconomic variables and the DSE.…”

Section: Literature Review On Macroeconomic Variablesmentioning

confidence: 99%

“…However, some of the factors that exist in literature are a mix of the macroeconomic variables and global stock market variables. These are the Foreign Exchange rate (Srivastava, 2010; Akter, Rana, & Anik, 2020); Index of Industrial Production (Srivastava, 2010); Consumer Price Index; Long Term Interest Rates (Srivastava, 2010; Mohammed and Rumman, 2018); Gold (Jain & Biswal, 2016; Bouri, Jain, Biswal, & Roubaud, 2017); Crude oil prices (Basher, Haug, & Sadorsky, 2011), Indian Volatility Index (Shahani & Bansal, 2020) and Morgan Stanley Capital International (MSCI) World Index (Srivastava, 2010; Mensi, Hammoudeh, Yoon, & Balcilar, 2016). In addition to the above mentioned factors, this study considers MSCI emerging market index as a new variable for predicting the Indian stock market in pre and during COVID-19 periods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Goel

Som

2023

ECON

View full text Add to dashboard Cite

PurposeThis study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).Design/methodology/approachSecondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.FindingsThe findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.Originality/valueThe novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

show abstract

Section: Literature Review On Macroeconomic Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Goel

Som

2023

ECON

View full text Add to dashboard Cite

show abstract

“…Some of the factors that we have identified from the literature are a mix of macroeconomic variables and global stock market variables. These are the Foreign Exchange rate (FX; Srivastava, 2010;Akter et al, 2020); Index of Industrial Production (IIP; Srivastava, 2010;Camilleri et al, 2019); Consumer Price Index (CPI; Megaravalli et al, 2018;Areli Bermudez Delgado et al, 2018); Long Term Interest Rates (LTINT; Srivastava, 2010;Mohammed and Rumman, 2018); Bombay Stock Exchange (BSE) Sensex (Giri and Joshi, 2017;Baranidharan and Dhivya, 2020) and Morgan Stanley Capital International World Index (MSCI; Srivastava, 2010;Mensi et al, 2016). Therefore, to forecast the closing price of the Indian stock market (BSE Sensex), we consider input variables, namely, CPI, LTINT, MSCI World Index, Rupee-Dollar FX and IIP.…”

Section: Dynamic Prediction Of Indian Stock Marketmentioning

confidence: 99%

“…Using Johansen's cointegration, Akter et al (2020) reported that the macroeconomic variables, namely, the exchange rate, IIP and reserve have significant cointegration with the Dhaka Stock Exchange (DSE). However, the results of VECM reveal no significant long-run relationship exists between the chosen macroeconomic variables and the DSE.…”

Section: Literature Review On Macroeconomic Variablesmentioning

confidence: 99%

Dynamic prediction of Indian stock market: an artificial neural network approach

Goel

Singh

2021

IJOES

View full text Add to dashboard Cite

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

show abstract

“…The result showed a weak connection between macroeconomic variables and stock market returns. Akter et al (2020) examine the dynamic relationship between stock market and macroeconomic variables in Bangladesh, using Monthly data from June, 2003 to June, 2015. The DSE index, Exchange rate, Industrial production and Reserve are the study variables.…”

Section: Interest Rate and Stock Market Returnsmentioning

confidence: 99%

Impact of Public Debt on Inflation and Unemployment in Nigeria: An ARDL Vector Error Correction Model

Shuaibu¹,

Muhammad²,

Abdullahi³

et al. 2021

NIJEFR

View full text Add to dashboard Cite

Debt is an important source of government funds in developed and developing countries. In developed countries, debt is an important source of money for bridging the gap between government revenues and expenditures. This paper measures the impact of public debt on inflation and unemployment in Nigeria during the period 1985 to 2020. It uses annual data of 36 years range to conduct various types of econometric tests. It uses Autoregressive Distributive Lag model (ARDL) Error Correction Model (ECM) for the analysis of the data. Unit root tests and Granger causality tests were also conducted to test the efficacy and predictive capability of the model. The findings of the study show that long run relationship exists between public debt and unemployment in Nigeria. It shows that increase in public debt causes more unemployment, but that external debt causes more unemployment than domestic debt. But the results of cointegration analysis show absence of relationship between public debt and inflation. The paper recommends reduction in public debt and if at all government must borrow, then it shall give priority to domestic debt over foreign debt.

show abstract

The dynamic relationship between stock market returns and macroeconomic variables: An empirical study from Bangladesh

Cited by 6 publications

References 7 publications

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Dynamic prediction of Indian stock market: an artificial neural network approach

Impact of Public Debt on Inflation and Unemployment in Nigeria: An ARDL Vector Error Correction Model

Contact Info

Product

Resources

About