scholarly journals The Impact of Investor Trading Behavior on Stock Return and Volatility in Korean Stock Market

2014 ◽  
Vol 10 (5) ◽  
pp. 679-694
Author(s):  
Hyung-Seon Ryu ◽  
Yang, Sung-Guk
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Trading Behavior ◽  
Korean Stock Market ◽  
Investor Trading ◽  
The Impact

The impact of closing mechanism changes: evidence from the Shanghai stock market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dan Ma ◽  
Chunfeng Wang ◽  
Zhenming Fang ◽  
Ziwei Wang
Keyword(s):  
Stock Market ◽  
Market Quality ◽  
Market Manipulation ◽  
Trading Behavior ◽  
Content Type ◽  
Price Manipulation ◽  
Closing Price ◽  
Investor Trading ◽  
The Impact ◽  
Closing Mechanism

PurposeThe purpose of this paper is to empirically examine the impact of closing mechanism changes on market quality, investor trading behavior and market manipulation in the Shanghai stock market.Design/methodology/approachA dummy variable is constructed indicating whether the closing mechanism is call auction or continuous auction. Market quality is measured from aspects of liquidity, volatility and price continuity; investor trading behavior is scaled by order timing and order aggressiveness, and a price deviation indicator is the proxy of manipulation. Using panel regression, this study examines the impact of closing mechanism changes based on intraday transaction data from the Shanghai stock market.FindingsThe conclusions are as follows: First, market quality improves after the closing mechanism is reformed in terms of liquidity, volatility and price continuity. Second, order strategy changes significantly in the closing call market, and investors trade more aggressively in the continuous trading period before closing. Third, the closing call mechanism restrains the closing price manipulation and thus prompts an efficient closing price.Originality/valueThis paper examines the policy effects of closing mechanism changes from aspects of market quality, trading behavior and price manipulation, providing pieces of evidence for trading mechanism design and market supervision in emerging markets.

Investor attention and market correction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhongdong Chen
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Behavior ◽  
Investor Attention ◽  
Information Effect ◽  
Content Type ◽  
Economic Significance ◽  
New Information ◽  
Positive Volume ◽  
The Impact

PurposeThis study disentangles the investor-base effect and the information effect of investor attention. The former leads to a larger investor base and higher stock returns, while the latter facilitates the dissemination of information among investors and impacts informational trading.Design/methodology/approachUsing positive volume shocks as a proxy for increased investor attention, this study evaluates the impacts of the investor-base effect and the information effect of investor attention on market correction following extreme daily returns in the US stock market from 1966 to 2018.FindingsThis study finds that the investor-base effect increases subsequent returns of both daily winner and daily loser stocks. The information effect leads to economically less significant return reversals for both the daily winner and daily loser stocks. These two effects tend to have economically more significant impacts on the daily loser stocks. The economic significance of these two effects is also related to firm size and the state of the stock market.Originality/valueThis study is the first to disentangle the investor-base effect and the information effect of increased investor attention. The evidence that the information effect facilitates the dissemination of new information and impacts stock returns contributes to the strand of studies on the impact of investor attention on market efficiency. This evidence also contributes to the strand of studies analyzing the impact of informational trading on stock returns. In addition, this study provides evidence for market overreaction and the subsequent correction. The results for up and down markets contribute to the literature on the investors' trading behavior.

Does investor personality moderate the relationship between information sources and trading behavior? Evidence from Chinese stock market

2017 ◽  
Vol 43 (5) ◽  
pp. 545-566 ◽  
Author(s):  
Muhammad Zubair Tauni ◽  
Zia-ur-Rehman Rao ◽  
Hong-Xing Fang ◽  
Minghao Gao
Keyword(s):  
Stock Market ◽  
Personality Traits ◽  
Chinese Stock Market ◽  
Sources Of Information ◽  
Trading Behavior ◽  
Content Type ◽  
Trading Frequency ◽  
Word Of Mouth Communication ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to investigate the impact of the key sources of information, namely, financial advice, word-of-mouth communication and specialized press, on trading behavior of Chinese stock investors. The study also analyzed if the association between the key sources of information and trading behavior is influenced by investor personality. Design/methodology/approach The authors adopted the Big Five personality framework and examined the survey results of individual stock investors (n=541) in China. Personality traits of investors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989). The authors performed probit regression analysis to evaluate the moderating influence of investor personality traits on the association between sources of information and stock trading behavior. Findings The results of the study confirm the previous findings that the key sources of information used by investors as a foundation of their financial choices have a significant influence on their trading behavior. The study also provides empirical evidence that investor personality traits moderate the relationship between the key sources of information and trading behavior. Financial advisors tend to increase the frequency of trading in investors with openness, extraversion, neuroticism and agreeableness personality traits, and tend to decrease the intensity of trading in investors with conscientiousness trait. On the other hand, financial information acquired from word-of-mouth communication is more likely to enhance trading frequency in extraverted and agreeable investors, and is more likely to reduce trading frequency in investors with openness, conscientiousness and neuroticism traits. Finally, the use of specialized press leads to more adjustment in portfolios of the investors with openness and conscientiousness traits than those with other personality traits. An alternative mediated model was not supported. Originality/value This research contributes to information search literature and behavioral finance literature and provides empirical evidence that the psychological characteristics of investors are significant predictors of the variations in information-trading link. The study offers new theoretical insights of investors’ behavior due to the characteristics of Chinese stock market which are unique from other stock markets in the world. To the authors’ best knowledge, no previous study has been conducted so far in Chinese stock market to explore variations with regards to the impact of the key sources of information on trading behavior by the Big Five investor personality and this paper seeks to fill this gap.

scholarly journals THE VOLATILITY TRANSMISSION OF MAIN GLOBAL STOCK'S RETURN TO INDONESIA

2017 ◽  
Vol 20 (2) ◽  
pp. 229-256
Author(s):  
Linda Karlina Sari ◽  
Noer Azam Achsani ◽  
Bagus Sartono
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Return ◽  
Global Financial Crisis ◽  
Symmetric Model ◽  
Return Volatility ◽  
Stock Return Volatility ◽  
Volatility Transmission ◽  
The Impact

Stock return volatility is a very interesting phenomenon because of its impact on global financial markets. For instance, an adverse shocks in one country’s market can be transmitted to other countries’ market through a particular mechanism of transmission, causing the related markets to experience financial instability as well (Liu et al., 1998). This paper aims to determine the best model to describe the volatility of stock returns, to identify asymmetric effect of such volatility, as well as to explore the transmission of stocks return volatilities in seven countries to Indonesia’s stock market over the period 1990-2016, on a daily basis. Modeling of stock return volatility uses symmetric and asymmetric GARCH, while analysis of stock return volatility transmission utilizes Vector Autoregressive system. This study found that the asymmetric model of GARCH, resulted from fitting the right model for all seven stock markets, provides a better estimation in portraying stock return volatility than symmetric model. Moreover, the model can reveal the presence of asymmetric effects on those seven stock markets. Other finding shows that Hong Kong and Singapore markets play dominant roles in influencing volatility return of Indonesia’s stock market. In addition, the degree of interdependence between Indonesia’s and foreign stock market increased substantially after the 2007 global financial crisis, as indicated by a drastic increase of the impact of stock return volatilities in the US and UK market on the volatility of Indonesia’s stock return.

scholarly journals Investor Attention, Market Dynamics, and Momentum in the Korean Stock Market

2020 ◽  
Vol 49 (4) ◽  
pp. 589-641
Author(s):  
Cheoljun Eom ◽  
Uk Chang ◽  
Byung Jin Kang ◽  
Woo Baik Lee ◽  
Jong Won Park
Keyword(s):  
Stock Market ◽  
Market Dynamics ◽  
Momentum Effect ◽  
High Turnover ◽  
Investor Attention ◽  
Momentum Profits ◽  
Korean Stock Market ◽  
Market Situation ◽  
The Impact ◽  
Loser Portfolio

This study examines the effects of investor attention on momentum in the Korean stock market. The results reveal significant negative momentum profits in stock groups with high investor attention (high turnover stocks), but insignificant results in those with low investor attention (low turnover stocks). Within high turnover stock groups, the winner portfolio has a declining price trend and insignificant performance, while the loser portfolio realizes significant positive performance through a substantial price increase in the future period. The momentum effect is highly dependent on the reversed performance of the loser portfolio. Second, the performance of the large overreaction stock group shows a more significant negative momentum effect compared to the low overreaction stock group, that is, the degree of overreaction significantly affects the momentum effect. Third, negative momentum profits are consistently observed regardless of the market dynamics. Specifically, more substantial and significant negative performance occurs in the transition market, where the market situation reverses between the past and future periods. Fourth, negative momentum profits are consistently identified even after controlling for the impact of common factors and volatility and liquidity into turnover. Our findings are qualitatively different from the characteristics of the traditional momentum effects generally reported in Western countries.

scholarly journals Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Hana Ghaneei
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Return ◽  
Stock Exchange ◽  
Markov Switching ◽  
Stock Index ◽  
Var Model ◽  
Content Type ◽  
Oil Market ◽  
The Impact

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

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.

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