scholarly journals Impact of Foreign Fund Flows on Volatile Indian Stock Market

2021 ◽  
Vol 10 (1) ◽  
pp. 28-32
Author(s):  
Monojit Dutta ◽  
Amalendu Bhunia
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Wald Test ◽  
Series Data ◽  
Fund Flows ◽  
Test Results ◽  
Integration Test ◽  
Indian Stock Market ◽  
Relationship Of ◽  
The Impact

This study examines the impact of foreign funds flows in terms of FDI and FPI on Indian stock market. Foreign fund flows in and out of Indian stock markets are now an ample portion of the market activity because FDI influences the growth directly and FPIs investment affects the growth indirectly by improving equity market performance of the host country. This study is based on yearly time series data for the period from 1992-93 to 2019-20. While analysing the data, descriptive statistics, correlation analysis, ADF unit root test, co integration and Wald test have been used. There is a positive relationship of FDI and FPI with Sensex. The volatility of Sensex is influenced by prior volatility and foreign fund flows. The Johansen co integration test results indicate that all the variables have a long-run relationship of the same order. The Wald test results confirm that there is a significant short-run causal relationship of FDI and FPI with Indian stock market. These findings suggest that volatility of Indian stock market and foreign flows have increased over the period of study.

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

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.

scholarly journals Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

2019 ◽  
Vol 9 (2) ◽  
pp. 1402-1417
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Good News ◽  
Series Data ◽  
Stock Market Index ◽  
Causality Test ◽  
Lm Test ◽  
The Impact

In general, stock market indices are widely interrelated to the other global markets to detect the impact of diversification opportunities. The present research paper empirically examines randomness and long term equilibrium affiliation amongst the emerging stock market of India and Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The researcher employs by the way, Run test, Pearson’s correlation test, Johnsen’s multivariate cointegration test, VECM and Granger causality test with reference to post-September 2008 Global financial crisis. The test results of the above finds that Nifty 50 and BSE Sensex is significantly cointegrated either among themselves or with MIST countries particularly during the post-September Global financial crisis. No random walk is found during the study period. The ADF (Augmented DickeyFuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. Symmetric and asymmetric volatility behaviors are studied using GARCH, EGARCH and TARCH models in order to test which model has the best forecasting ability. Leverage effect was apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms GARCH (1,1) as the best suited model for estimating volatility andforecasting stock market index.

scholarly journals HINTERLAND MEDAN CITY BETWEEN BINJAI CITY: A GRAVITATION APPROACH

2019 ◽  
Vol 17 (1) ◽  
pp. 94
Author(s):  
Muhammad Nasir
Keyword(s):  
Confidence Level ◽  
Time Series Data ◽  
Impulse Response Function ◽  
Unit Root Test ◽  
Gravity Models ◽  
Series Data ◽  
Causality Test ◽  
Integration Test ◽  
Relationship Of ◽  
The City

Regional economy explains that there is an urban hierarchical relationship, cities that have higher hierarchy will serve cities that are below it as well as cities that are in hierarchy under supplying cities that are in the hierarchy above them, so there is a gravitational relationship between the two. This study aims to determine the gravitational relationship of Medan city to the hinterland of the city of Binjai. Furthermore, this study also wants to explain its influence on economic growth in both cities. This study uses time series data from 1990-2016, taken from North Sumatera BPS test equipment and analysis tools used are descriptive statistics, gravity models, unit root test, co-integration test, optimal lag, VECM, granger causality test, impulse response function and variance decomposition. The results showed that the city of Medan has a gravity style greater than the gravitational style of the city of Binjai. This is because the city of Medan has a larger area, population, income per capita compared to the city of Binjai. The VECM estimation results show that the gravitational variable in the city of Binjai in lag -1 and lag-2 has a positive and significant effect on the economy of Medan city with a confidence level of 95%. Then the economic variable of the city of Binjai itself in lag-1, the population of the city of Medan in lag-2 and the gravity of the city of Medan in lag-2 had a positive and significant effect on the economy of Binjai city with a confidence level of 95%. While the variable population of Binjai city in lag -1 and residents of the city of Medan in lag -1 negatively affected the economy of Binjai city with a confidence level of 95%.

scholarly journals Trade Openness and Economic Growth in Indonesia

2020 ◽  
Vol 10 (2) ◽  
pp. 163-170
Author(s):  
Khoirul Ifa ◽  
Moh. Yahdi
Keyword(s):  
Economic Growth ◽  
International Trade ◽  
Time Series Data ◽  
Trade Openness ◽  
Generalized Method Of Moments ◽  
Series Data ◽  
Test Results ◽  
Rapid Economic Growth ◽  
The Impact

Economic growth and international trade are related to one another. International trade stimulates long-term economic growth. The more trade activities in a country, the more rapid economic growth; this trade is a key component of development in a country, its contribution is felt with the increasing economic growth in several countries. The purpose of this study looks at the impact of trade openness on economic growth in Indonesia in 1986-2017. This research is a quantitative study using time series data from 1986-2017, research data obtained from the world bank, data analysis techniques using the GMM method to see the impact of trade openness on economic growth. The test results using the Generalized Method of Moments analysis method show that all variables significantly influence the dynamics of economic growth in Indonesia. This result is proven by the t-statistic probability value, which shows a smaller value compared to the t-table value. Then the value also has a probability of less than α. It can be concluded that the variables of trade, FDI, inflation, and the number of workers have a significant effect on economic growth in Indonesia.

scholarly journals Monetary Policy Shocks and Manufacturing Output in Nigeria (1981-2018)

2020 ◽  
Vol 7 (1) ◽  
pp. 27-37
Author(s):  
Yinka Sabuur Hammed
Keyword(s):  
Monetary Policy ◽  
Time Series Data ◽  
Policy Instrument ◽  
Series Data ◽  
Policy Makers ◽  
Long Run ◽  
Integration Test ◽  
Policy Shocks ◽  
Monetary Policy Shock ◽  
The Impact

This study empirically investigates the impact of monetary policy shock on the manufacturing output in Nigeria using time series data covering the period between 1981 and 2018. Co-integration test was used to establish the long run relationship among the variables and Structural Vector Auto-Regressive model was employed to test for the shocks. It was found that shock to broad money supply would bring about positive and significant impact on the manufacturing output while the impact of shock to interest rate was found to be negative and insignificant. This study however concludes that shock to broad money is the main monetary policy instrument which can bring about positive change to manufacturing output in Nigeria. This paper then suggests that government and policy makers should primarily focus on this variable in their implementation of unanticipated monetary policy.

scholarly journals Does Macroeconomic Indicators Influence Stock Price Behavior? Evidence from Nigerian Stock Market

2019 ◽  
Vol 6 (2) ◽  
pp. 26
Author(s):  
Peter Ego Ayunku
Keyword(s):  
Stock Market ◽  
Significant Influence ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Price Behavior ◽  
Market Growth ◽  
Integration Test

This paper investigate whether macroeconomics indicators influences stock price behavior in Nigerian stock market, using an annual time series data spanning from 1985-2015. The study employed some econometric tools such as Augmented Dicker Fuller (ADF) Unit Root test, Johansen’s co integration test, Vector Error Correction Model (VECM) to analyze the variables of interest. The study found out that Money Supply (MS) has an inverse but statistically significant  influence on stock prices in Nigerian stock market also Treasury Bill Rate (TBR) has an inverse and statistically insignificant influence on stock market prices. While on the other hand, Market Capitalization (MCAP) has a positive and statistically significant influence on stock prices while Exchange Rate (EXR) has positive but statistically insignificant relationship with stock prices in the Nigerian Stock Market. In view of the above, the study recommends amongst others that monetary authorities should try as much as possible to implement sound macroeconomic policies that would enhance stock market growth and development in Nigeria. 

Bull and Bear Phases: An Empirical Perusal of Indian Stock Market NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Sunaina Kanojia ◽  
Neha Arora
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Time Series Data ◽  
Series Data ◽  
Bear Market ◽  
Bull Markets ◽  
Indian Stock Market ◽  
Increases In Risk ◽  
Considerable Support

In general, any one known to stock market is acquainted with the phenomenon of bull and bear phases, but whether the traders or investors put air to these phases while making a decision to buy, sell, or stay invested. The present paper attempts to identify and analyze the two most popular market phases, i.e. bull and bear, for better investment decisions with the use of Bry and Boschan Algorithm and time series data. Further, it seeks to analyze the distributional characteristics of the variances in stock returns and search evidence of asymmetries, if any, in volatility under different market conditions which may help to shed light on the bull and bear phases of Indian equity market. The study arrange for evidence that in bull markets, stock prices run far ahead of earnings and for fairly long periods of time. The paper indicates 12 bull and bear phases in the Sensex and Nifty during the sample period of 19 years with the associated factors responsible for the shift of bull and bear market phases. The results provide considerable support for the view that markets choose to ignore adverse possibilities and react with zest to favorable possibilities and market declines can partly be explained by increases in risk.

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 Estimating the Impact of Human Capital Underutilization on the Productivity and Economic Growth in India

2021 ◽  
Author(s):  
Vijaya Kumar M ◽  
Balu B
Keyword(s):  
Economic Growth ◽  
Human Capital ◽  
Time Series Data ◽  
Negative Relationship ◽  
Series Data ◽  
International Labor Organization ◽  
Distributed Lag ◽  
Integration Test ◽  
The Impact ◽  
Adf Test

Abstract This study investigated the effect of human capital underutilization on productivity and economic growth. It has used time-series data accessed from the International Labor Organization (ILO) and World Bank database. This paper estimated the relationship between the underutilization of human capital on productivity and economic growth by applying the econometric tests like Augmented Dickey-Fuller (ADF) Test, Johansen Integration Test, and the Autoregressive Distributed Lag (ARDL) model. The results revealed that in the long run human capital underutilization has a negative relationship on GDP and labor productivity and it does not in the short run. The study recommends that specific policy legislations in the Indian labor markets are required for addressing the problem of human capital underutilization and thereby accelerating the economic growth and productivity for the current and future generations.

scholarly journals Impact of Global Oil Shocks on the Russian Stock Market

2021 ◽  
Vol 92 ◽  
pp. 07037
Author(s):  
Igor Lukasevich ◽  
Ludmila Chikileva
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Value Added ◽  
Stock Index ◽  
Series Data ◽  
Var Model ◽  
Oil Shocks ◽  
Russian Market ◽  
Russian Stock Market ◽  
The Impact

Research background: The study focuses on modeling assessment of oil shocks impact on the Russian stock market. Purpose of the article: The purpose of the study is to determine the impact of oil prices abrupt changes on the Russian stock market, its quantitative and temporal specifications. The study consists of two interrelated sections. The first section includes the results of statistical processing of initial data, calculation of their key characteristics and preliminary analysis. The second section of the study is devoted to modeling the assessment of the impact of oil shocks on the behavior of the Russian market RTS stock index. Methods: Based on an extensive sample of daily price values for Brent North sea oil and the Russian stock index RTS for the period from 1997 to May 2020, the study was conducted using models vector auto regression (VAR-model). Findings &Value added: The VAR model was developed and tested to assess the impact of oil shocks on the Russian stock market. Unlike the results of other studies, it is shown that the Brent oil price variance explains only about 10% of the RTS index yield variance in long-term time intervals. The low correlation of time series data and time limit of the impact of oil shocks on the Russian market have been revealed. According to the results of the study, the market recovery takes about 2 months, then the stock index returns to the ‘historical’ range of average ± standard deviation.

Sign in / Sign up

Export Citation Format

Share Document