STOCK MARKET DIFFERENCES IN CORRELATION-BASED WEIGHTED NETWORK

2011 ◽  
Vol 22 (11) ◽  
pp. 1227-1245 ◽  
Author(s):  
JANGHYUK YOUN ◽  
JUNGHOON LEE ◽  
WOOJIN CHANG
Keyword(s):  
Information Technology ◽  
Regression Analysis ◽  
Stock Market ◽  
Stock Return ◽  
Market Volatility ◽  
Weighted Network ◽  
Korean Stock Market ◽  
Industry Sectors ◽  
Major Industry ◽  
Weighted Network Analysis

We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

An information flow among industry sectors in the Korean stock market

2014 ◽  
Vol 65 (12) ◽  
pp. 2140-2146 ◽  
Author(s):  
Gabjin Oh ◽  
Tamina Oh ◽  
Hoyong Kim ◽  
Okyu Kwon
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Korean Stock Market ◽  
Industry Sectors

The Effect of Operation and Market Value Efficiency on the Korean Stock Market

2015 ◽  
Vol 23 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Soo-Hyun Kim
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Market Value ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Cross Sectional ◽  
Efficiency Measures ◽  
Korean Stock Market ◽  
Low Efficiency ◽  
Using Data

This paper investigates the relationship between output to input efficiency and stock return predictability in the Korean stock market. We measure the efficiency using Data Envelopment Analysis with independent outputs of sales and market value data. Sales efficiency measures the operational efficiency whereas market value efficiency measures the efficiency evaluated by the investors. Through our empirical analysis, it is found that low efficiency stocks in either measures tend to have higher future returns. However, if both efficiency measures are employed at the same time there exists a strong tendency that high operation efficient and low market value efficient stocks generate larger future returns. We find that DEA analysis for efficiency can process a cross-sectional stock return predictability in the Korean stock market.

scholarly journals DYNAMICS OF FOREIGN INSTITUTIONAL INVESTMENTS AND EQUITY RETURNS IN INDIA

2016 ◽  
Vol 4 (6) ◽  
pp. 1-7
Author(s):  
Rajdeep Singh ◽  
Kanwaljeet Singh ◽  
Prabhjot Kaur
Keyword(s):  
Information Technology ◽  
Regression Analysis ◽  
Stock Market ◽  
Equity Returns ◽  
International Flow ◽  
Simple Regression Analysis ◽  
Indian Stock Market ◽  
The Relationship ◽  
Simple Regression ◽  
Focus Point

India has become a focus point and an attractive hub for foreign investor’s post 199. This international flow of capital was facilitated by increased globalization and the growth of information technology which has blurred national borders. Thus FII flows in India have continuously grown in importance post 1991. This paper examines the trend of FII flow in India from 2001 and 2015 and also examines the relationship between FII and the two important barometers of the Indian stock market, i.e., S&P BSE Sensex and CNX Nifty. The impacts of FII on the proxies for stock market, i.e., Sensex and nifty have been studied by employing simple regression analysis using E-views.

scholarly journals Impact of Stock Market-specific and Macro-economic Variables on Stock Return

2019 ◽  
Vol 3 (1) ◽  
pp. 56-66
Author(s):  
Shiba Prasad Sapkota
Keyword(s):  
Regression Analysis ◽  
Stock Market ◽  
Stock Return ◽  
Trade Openness ◽  
Important Variable ◽  
Economic Variables ◽  
Long Run ◽  
Behavioral Aspects ◽  
Short Run ◽  
The Impact

The study was aimed to examine the impact of stock market-specific variables and macro-economic variables on stock return. It has analyzed the data of 25 countries for a period of 22 years from 1995 to 2016. The major three tests; regression analysis, co-integration analysis, and causality have been examined. The results have shown from the evidence of regression analysis and causality that the impact of the stock market-specific and macro-economic variables have been varied as per the different situation and country. Whereas, the impact of stock market-specific and macro-economic variables have been found to be consistent in the long run. Therefore, the conclusion was drawn that in the long run the relationship between stock return and stock market-specific, as well as macro-economic variables can be generalized but in the short run better not to generalize. The findings have shown that stock market-specific variables have better explaining and predicting power than macro-economic variables. In the case of stock market-specific variables, size and stock traded turnover ratio have found equally important to understand the behavior of sock return. In the case of macro-economic variables, GDPGR has found the most important variable followed by money supply, exchange rate, interest rate, trade openness, and inflation rate respectively. Finally, it has been concluded that the behavioral aspects of the investors have been missing. So, the financial theories incorporating the behavioral aspects would explain and predict better rather than economic physiognomy only.

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