scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals Daily Stock Movements Through the Lens of Event and News: Evidence from the Pakistan Stock Exchange

2021 ◽  
Author(s):  
Shahid Raza ◽  
Baiqing Sun ◽  
Pwint Kay Khine
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
News Daily ◽  
Stock Market Index ◽  
Daily Newspaper ◽  
Monthly Basis ◽  
Market Index ◽  
The Impact

This study will investigate different signals and events/news that determined the stock market's movements. As we know, many factors affect the stock market on a daily, weekly, and monthly basis, e.g., rate of interest, exchange rate, and oil prices, etc. Our research will investigate the impact of daily events/news in the KSE-100 index due to several policies announced and events/news in the country because the daily movements in the stock market can be determined only by different signals and events/news. Time series data is collected daily for particular reasons from "The News" (Daily Newspaper, Sunday edition) from 2010 to 2019. The results of this study show that political and global news affects the stock market index ferociously. For investors, the investment in blue chips is not less than a safe haven. When day-to-day transactions are concerned, there is always a higher panic attack than the herd behaviour in the stock exchange. Investors tend to make prompt responses to negative rather than positive news, which makes them risk averters. Our finding also confirmed that the ARCH/GARCH model is better than the simple OLS method concerning stock market upheaval.

scholarly journals Cointegration analysis of stock market index and exchange rate: The case of Serbian economy

2021 ◽  
pp. 59-71
Author(s):  
Milena Marjanović ◽  
Ivan Mihailović ◽  
Ognjen Dimitrijević
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Market Index ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run ◽  
The Exchange Rate ◽  
Adf Test ◽  
Long Run Equilibrium

Since the late 90's, the existence and direction of causality between the capital market and foreign exchange market have attracted significant attention of theoretical and empirical researchers. This is because both of these financial variables have an indisputable role in the development of each country's economy. In this paper we use Johansen procedure and Granger causality test to examine the existence and direction of short-run and long-run dynamics between the leading stock market index BELEX15 and RSD/EUR exchange rate in Serbia. Using ADF test we find that both series are integrated of order one, and since the value of Johansen trace statistics confirmed the existence of cointegration, we have proceeded with estimation of the VECM model. According to our VECM model, the BELEX15 index adjusts to the long-run equilibrium relationship at a rate of 11.72% in each period, while the exchange rate adjusts to the long-run equilibrium relationship at a rate of 2.73%. We also find that there is unidirectional causality and that the market index influences the exchange rate movements in the short-run in terms of Granger.

scholarly journals Socioeconomic Stability and Variability in Stock Market Prices: A Case Study of Karachi Stock Exchange

2018 ◽  
Vol 6 (4) ◽  
pp. 428-440
Author(s):  
Rafaqat Ali ◽  
Rana Ejaz Ali Khan
Keyword(s):  
Stock Market ◽  
Structural Breaks ◽  
Time Series Data ◽  
Stock Exchange ◽  
Price Volatility ◽  
Series Data ◽  
Conditional Heteroscedasticity ◽  
Market Prices ◽  
Long Run ◽  
Short Run

The study attempted to identify the factors that responsible for variability in stock market prices in Karachi Stock Exchange particularly focusing on socioeconomic stability in the country. The socioeconomic stability is measured by an index including social, economic and political dimensions of stability. Annual time series data for the years 1973-2012 is utilized, and Phillips & Perron (PP) test is employed for stationarity. Autoregressive Conditional Heteroscedasticity and Generalized Conditional Heteroscedasticity (ARCH/GARCH) technique are used for volatility in stock market prices. For the structural breaks, Chow test is applied. Finally, the study utilized the Autoregressive Distributed Lag (ARDL) approach to estimate the long-run and short-run dynamic relationship. The results indicate that inflation, exchange rate, and foreign direct investment positively influence the stock price volatility. Socioeconomic stability negatively affects the volatility in stock market prices in both short-run and long-run. The country should improve socioeconomic stability by attaining economic, social and political standards in the country.

A Sectoral Analysis of the Role of Stock Market Development on Economic Growth: Empirical Evidence from Indian Economy

2017 ◽  
Vol 18 (4) ◽  
pp. 911-923 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run

The present study examines the relationship between Indian stock market and economic growth from a sectoral perspective using quarterly time-series data from 2003:Q4 to 2014:Q4. The results of the autoregressive distributed lag (ARDL) approach bounds test confirm the existence of a cointegrating relationship between sector-specific gross domestic product (GDP) and sector-specific stock indices. The empirical results reveal that sector-specific economic growth are significantly influenced by changes in the respective sector-specific stock price indices in the long run as well as in the short run. Apart from that, the control variables, such as trade openness and inflation, act as the instrument variables in explaining the variations in the sector-specific GDP of the economy. The results of Granger causality test demonstrate unidirectional long-run as well as short-run causality running from sector specific stock prices to respective sector GDP. The findings suggest that economic growth of the country is sensitive to respective sub-sector stock market investments. The findings highlight the reasons for cyclical and counter-cyclical business phase for the overall economy.

scholarly journals Impact of Macroeconomic and Demographic Variables on the Stock Market: Evidence from Tunisian Crisis

2016 ◽  
Vol 8 (8) ◽  
pp. 194
Author(s):  
Sirine Ben Yaâla ◽  
Jamel Eddine Henchiri
Keyword(s):  
Stock Market ◽  
Money Supply ◽  
Inflation Rate ◽  
Demographic Variables ◽  
Second Order ◽  
Stock Market Index ◽  
Order Difference ◽  
Market Index ◽  
Long Run ◽  
Short Run

<p>This study aims to analyze the long-run as well as the short-run relationship between macroeconomic, demographic variables and the Tunisian stock market for the period subsequent to the financial crisis. Monthly data over the period 2008-2014 and ARDL model have been employed. Results indicate that the Tunisian stock market index, macroeconomic and demographic indicators are cointegrated and, therefore, a long-run relationship exists between them. The long-run coefficients suggest that budget deficit, inflation rate and number of unemployed graduates had a negative effect, otherwise, money supply and number of non-resident entries had positive effect on the Tunisian stock market. Moreover, results from the error correction model show that the Tunisian stock market index is influenced positively by money supply and second order difference of the number of unemployed graduated and negatively by first and second order difference of money supply, inflation rate, first order difference of number of non-resident entries and number of unemployment graduates.</p>

Long Term Effects of Major Macroeconomic Variables on the Brazilian Stock Market: A nonlinear ARDL application

2021 ◽  
Vol 9 (2) ◽  
pp. 289-299
Author(s):  
MARCELO MELO ◽  
WELIGTON GOMES
Keyword(s):  
Interest Rate ◽  
Stock Market ◽  
Government Debt ◽  
Long Term Effects ◽  
Stock Market Index ◽  
Monthly Data ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
The Interest Rate

This research used NARDL methodology to investigate relevant macroeconomic variables influence on the Brazilian stock market index. We used monthly data from January/2000 to July/2020 and the six macroeconomic variables investigated are described as follows: net government's debt/GDP (DEBT), exports (EXPORT), consumer confidence (ICC), liquidity ratio (M4_PIB), interest rate (SELIC) besides the stock market index (IBOV). All monthly data were collected from IPEADATA. The main conclusions are that there is long run effect of IBOVESPA due to a decrease of government debt is clear and statistically significant, the long run effect in the liquidity ratio also affects IBOVESPA index. Moreover, the most outstanding result was the long run effect of decrease in the interest rate over the IBOVESPA index. Sustainable reductions in the interest rate would consistently stimulate the stock market index. Research outcomes also indicate that long run asymmetries of government debt, liquidity ratio and interest rate are reliable and statistically significant.

scholarly journals Stock market development and economic growth in Tanzania: an ARDL and bound testing approach

2020 ◽  
Vol 5 (3) ◽  
pp. 187-206
Author(s):  
Saganga Mussa Kapaya
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Time Series Data ◽  
Market Development ◽  
Series Data ◽  
Content Type ◽  
Stock Market Development ◽  
Testing Procedures ◽  
Long Run ◽  
Short Run

Purpose The purpose of this paper is to contribute to empirical evidence by recognizing the importance of stock markets in the financial system and consequently its causality to economic growth and vice versa. Design/methodology/approach The study used the autoregressive distribute lag model (ARDL) with bound testing procedures, the sample covered quarterly time-series data from 2001q1 to 2019q2 in Tanzania. Findings The results suggest that stock market development have both negative and positive causality for both short-run dynamics and long-run relationship with economic growth. Economic growth is found to only cause and relate negatively to liquidity both in the short-run and in the long-run. The results show predominantly a unidirectional causality flow from stock market development to economic growth and finds partial causality flow from economic growth to stock market development, as represented by stock market turnover which proxied liquidity. Originality/value The use of quarterly data to reflect more realistically the dynamics of the variables because yearly data may sometimes cover-up specific dynamics that may be useful for prediction and policy planning. The study uses indices to capture general aspects within the stock market against economic growth as an intuitive way to aggregate the stock market development effects.

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

scholarly journals The Determinant of Indonesian Stock Returns’ Volatility: Evidence from Islamic and Conventional Stock Market

2021 ◽  
Vol 6 (3) ◽  
pp. 277-296
Author(s):  
Septiana Indarwati ◽  
Agus Widarjono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Money Supply ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Macroeconomic Indicators

Islamic stock market is apparently different from the conventional stock market due to the prohibition of unlawful goods and excessive risk-taking behavior. This study explores the extent to which the Indonesian Islamic and conventional stock returns' volatility responds to the macroeconomic indicators. This study employs Jakarta Islamic Index (JII) and Indonesian Stock Exchange (IDX) and uses monthly time-series data covering 2001: M1 - 2019: M12. The volatility of stock returns is measured using Generalized Autoregressive Conditional Heteroskedasticity (GARCH). By employing the Autoregressive Distributed Lag Model (ARDL), the results validate the evidence of the long-run relationship between the stock market's volatility and macroeconomic variables. A rising in money supply and an economic upturn reduce the volatility of conventional stock returns but only an expansionary money supply diminishes the volatility of Islamic stock returns. Conversely, high inflation and sharp depreciation of the Rupiah boost the stock returns' volatility. The results further show an interesting finding that the Islamic stock market's volatility is more responsive to changes in macroeconomic indicators than the volatility of their counterpart conventional stock market. Policymakers should take strict rules during the worst economic conditions to minimize the negative impact of the instability of macroeconomic variables.

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