scholarly journals How is economic growth correlated to index growth?

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Aryan Chordia ◽  
Karan Gurbani
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Real World ◽  
Consumer Confidence ◽  
Gdp Growth ◽  
Stock Market Index ◽  
Moment Coefficient ◽  
Market Index ◽  
Coefficient Corrélation ◽  
Real World Situation

This study will mainly focus on the relation between GDP growth and stock market index growth through various methods like the Pearson product-moment coefficient correlation, comparing the theoretical world to the real-world situation, and finally by looking at consumer sentiments and confidence. This could help various analysts, economists, and even the general public. Through the course of our study, we find out various trends in this study, at some point, the results were contradicting themselves but then our last test made it all clear, which proves that there is no correlation between GDP growth and stock market index growth, but then both of them drives from consumer confidence and consumers future actions. 

NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

2007 ◽  
Vol 06 (01) ◽  
pp. 113-140 ◽  
Author(s):  
WEI HUANG ◽  
KIN KEUNG LAI ◽  
YOSHITERU NAKAMORI ◽  
SHOUYANG WANG ◽  
LEAN YU
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Neural Networks ◽  
Stock Market ◽  
Exchange Rates ◽  
Network Models ◽  
Stock Market Index ◽  
Neural Network Models ◽  
Market Index ◽  
Comparison Results

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

scholarly journals Factors driving IPO variability: evidence from Pakistan stock exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waqas Mehmood ◽  
Rasidah Mohd-Rashid ◽  
Chui Zi Ong ◽  
Yasir Abdullah Abbas
Keyword(s):  
Growth Rate ◽  
Foreign Direct Investment ◽  
Stock Market ◽  
Direct Investment ◽  
Gdp Growth ◽  
Stock Market Index ◽  
Treasury Bill ◽  
Content Type ◽  
Market Index ◽  
Gdp Growth Rate

PurposeThe objectives of this study are twofold. First, it intends to investigate the symmetric link between initial public offering (IPO) variability and the determinants of the stock market index, treasury bill rate, inflation, GDP growth rate and foreign direct investment. Second, this study intends to examine the asymmetric link between IPO variability and the aforementioned determinants, namely the stock market index, treasury bill rate, inflation, GDP growth rate and foreign direct investment.Design/methodology/approachData from 1992 to 2018 were gathered from the country of Pakistan in order to achieve the above objectives. Augmented Dickey–Fuller (ADF) and Phillips Perron (PP) unit root tests were employed to determine the data's stationarity properties. The Auto Regressive Distributive Lags (ARDL) model was utilized to examine the symmetric links, and the Non-Linear Auto Regressive Distributive Lag Model (NARDL) was employed to determine the asymmetric links. While the long-run co-integration was examined using the ARDL bound test, the short-run dynamics were tested using the error correction method (ECM).FindingsThe macroeconomic variables of the stock market index, treasury bill rate, inflation, GDP growth rate and foreign direct investment are found to pose significant short-run and long-run symmetric and asymmetric effects on IPO variability. These results indicate the significance of the aforementioned variables in enhancing IPO variability. The findings also demonstrate the typical reactions of inflation, GDP and FDI towards negative and positive shocks in IPO variability and inflation. This evidence implies that Pakistan's poor capital market development is reflected in the country's weak macroeconomic factors. At the same time, the reduced IPO variability in the country also reflects the lack of confidence among prospective issuers and investors due to Pakistan's weak macroeconomic indicators.Originality/valueThis is the first study of its kind to properly investigate the symmetric and asymmetric effects of the macroeconomic variables on Pakistan's IPO variability.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

scholarly journals Investors’ Reactions to CSR News in Family Versus Nonfamily Firms: A Study on Signal (In)credibility

2021 ◽  
pp. 104225872110104
Author(s):  
Naciye Sekerci ◽  
Jamil Jaballah ◽  
Marc van Essen ◽  
Nadine Kammerlander
Keyword(s):  
Stock Market ◽  
Event Study ◽  
Family Firms ◽  
Signaling Theory ◽  
Family Firm ◽  
Stock Market Index ◽  
Important Condition ◽  
Post Hoc Analysis ◽  
Market Index ◽  
Post Hoc

We study family firm status as an important condition in signaling theory; specifically, we propose that the market reacts more positively to positive, and more negatively to negative, CSR news (i.e., signals) from family firms than to similar news from nonfamily firms. Moreover, we propose that during recessions, the direction of these relationships reverses. Based on an event study of 1247 positive and negative changes in the CSR ratings for all firms listed on the French SFB120 stock market index (2003-2013), we find support for our hypotheses. Moreover, a post hoc analysis reveals that the relationships are contingent on whether a family CEO leads the firm.

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