scholarly journals Investor Behavior: Does Tax Avoidance and Liquidity Preference Culture Drive Equity Prices in Pakistan

2020 ◽  
Vol 2 (2) ◽  
pp. 63-91
Author(s):  
Rubeena Tashfeen ◽  
Saad Ullah ◽  
Abubaker Naeem

The present study investigates market-wide herding of stock market, industry indices of Pakistan, China and USA, A-cross border herding of Pakistan stock market with Chinese stock market and USA stock market. With Cross-Sectional-Absolute-Deviation, to check whether geographical distance matters to influence the stock markets or not and USA is its major influential, cannot be ignored. Market-wide herding in Pakistan is found only during 2004 and 2008 and A-cross border herding for Pakistan is only found from the USA which support asset pricing model and market efficiency. Pakistan market do not herd around China, this negates geographical distance matters, and influence in determining investor behaviour in stock markets. It is revealed, Pakistan stock market does not observe as much herding behaviour in stock investment as other markets (USA and China), so it can be said that Pakistan stock exchange index which is representative of Pakistan Stock market is efficiently operating in contest of Herding.

2020 ◽  
Vol 2 (2) ◽  
pp. 1-1
Author(s):  
Syed Ali Arslan ◽  
Rukhsana Bibi ◽  
Attiya Yasmin Javid

The present study investigates market-wide herding of the stock market industry indices of Pakistan, China, and the USA, and cross-border herding of Pakistan stock market with the Chinese stock market and USA stock market. With Cross-Sectional-Absolute-Deviation, this study checks whether geographical distance matters in influencing the stock markets or not and if the USA is it's major influential and cannot be ignored. Market-wide herding in Pakistan is found only during 2004 and 2008, and across border herding for Pakistan is only found from the USA, which supports the asset pricing model and market efficiency hypotheses. Pakistan market does not herd around China- this negates that geographical distance matters and influences in determining investor behavior in stock markets. It is also revealed that the Pakistan stock market does not observe as much herding behavior in stock investment as other markets (such as the USA and China), so it can be said that the Pakistan Stock market is efficiently operating in the context of herding. JEL Classification: G02, G11, G14, G1


2019 ◽  
Vol 8 (4) ◽  
pp. 9358-9362

The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.


2020 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Ki-Hong Choi ◽  
Seong-Min Yoon

This paper investigates herding behavior and the connection between herding behavior and investor sentiment. We apply a Cross-Sectional Absolute Deviation (CSAD) approach and the quantile regression method to capture herding behavior in the KOSPI and KOSDAQ stock markets. The analysis results are outlined as follows. First, we find that herding behavior is exhibited during down-market periods in the KOSPI and KOSDAQ stock markets. However, we show that adverse herding behavior occurs in low-trading volume and low-volatility periods. Second, according to the results of the quantile regression, herding behavior is found in the low and high quantiles of the KOSPI and KOSDAQ stock markets. However, adverse herding behavior is also found, which means that investors herd in extreme market conditions. Third, the relationship between investor sentiment and herding behavior is analyzed through regression and quantile regression, and investor sentiment is confirmed to be one of the important factors that can cause herding behavior in the Korean stock market.


2018 ◽  
Vol 10 (3(J)) ◽  
pp. 203-219 ◽  
Author(s):  
Kalugala Vidanalage Aruna Shantha

This paper examines the herding phenomenon in the context of a frontier stock market, the Colombo Stock Exchange of Sri Lanka, employing the cross - sectional absolute deviation methodology to daily frequencies of data for the period from April, 2000 to September, 2016. The results show significant changes in magnitude and pattern of herding over different episodes of the market. The herd behavior is strongly presence irrespective of the direction of the market movement in the 2000 - 2008 period, during which investments in the stock market is affected by the country’s political instability resulting from the civil war. The evidence also shows herd behavior during the period of market bubble whereas negative herding in the market crash period. However, it becomes less likely to occur during the period after the market crash. The lower tendency to herd during the post- market crash period supports the Adaptive Market Hypothesis, implying that investors are likely to realize the irrationality of herding and learn to be more rational as a consequence of significant losses experienced during the period of the market crash. Accordingly, these findings suggest that period- specific characteristics of the market and the associated psychological effects to investors such as overconfidence and panics would cause changes to their beliefs and behavior, the experiences of which would subsequently produce learning effect to minimize their irrationality in decision making.


Author(s):  
Xunfa Lu ◽  
Fredrick Oteng Agyeman ◽  
Ma Zhiqiang ◽  
Mingxing Li ◽  
Agyemang Akwasi Sampene ◽  
...  

Examining the contemporaneous causality between Chinese and Ghanaian stock markets before and amidst the coronavirus disease 2019 (COVID-19) pandemic is of immense interest to many stakeholders in making effective and efficient decisions. This study investigates why the two stock markets’ fluctuations seem to move in tandem despite a broader economic phenomenon. Shanghai Stock Exchange and Ghanaian Stock Exchange composite indices data were used for this study spanning 2011-2020. The Granger causality and transfer entropy are applied to investigate the mean transmission. The Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) model portrays the dynamic correlation and the ARMA model is used to fit the log-returns of the two indices. Results show that the Chinese stock market has a substantial causal effect on the Ghanaian stock market based on transfer entropy with the second order of lag while there is a considerable causality from the stock market of Ghana to the Chinese stock market through the third and fifth orders of lags. This implies the asynchronous return transmission between Chinese and Ghanaian stock markets. Moreover, the long term volatility connection significantly impacts the two markets, but the short-term volatility pattern does not heavily affect the markets based on the DCC-GARCH model. The best-fitted model for the log returns of two stock markets is ARMA (1,1). This study recommends that policymakers and investors adopt diversification as a resort to financial management.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vijay Kumar Shrotryia ◽  
Himanshi Kalra

PurposeThe present study looks into the mimicking behaviour in both normal and asymmetric scenarios. It, then, considers the contagion between the USA and the BRICS stock markets. Finally, it examines herd behaviour in the wake of a major banking policy change concerning the bloc under study.Design/methodology/approachThe current empirical analysis employs daily, weekly and monthly data points to estimate relevant herding parameters. Quantile regression specifications of Chang et al. (2000)'s dispersion method have been applied to detect herd activity. Also, dummy regression specifications have been used to examine the impact of various crises and strategically crucial events on the propensity to herd in the BRICS markets. The time period under consideration ranges from January 2011 until May 2019.FindingsThe relevant herding coefficients turn insignificant in most cases for normal and asymmetric scenarios except for China and South Africa. This can be traced to the anti-herding behaviour of investors, where individuals tend to diverge from the consensus. However, turbulence makes all stock markets to show some collective trading except Russia. Further, the Chinese stock market seems immune to the frictions in the US stock market. Finally, the Indian and South African markets witness significant herding during the formation of a common depository institution.Practical implicationsMost stock markets seem to herd during turbulence. This revelation is of strategic importance to the regulators and capital market managers. They have to be cautious during crises periods as the illusion of being secured with the masses ends up creating unprecedented frictions in the financial markets.Originality/valueThe present study seems to be the very first attempt to test the relevant distributions' tails for convergent behaviour in the BRICS markets.


2019 ◽  
Vol 5 (3) ◽  
pp. 673-690 ◽  
Author(s):  
Nora Amelda Rizal ◽  
Mirta Kartika Damayanti

Indonesia Stock Exchange provides Islamic stocks for Muslim investors who want toinvest, with the first Islamic stock index in Indonesia being Jakarta Islamic Index or JIIthat consists of thirty of the most liquid Islamic stocks. The market capitalization of JIItends to increase every year. This paper examines the presence of herding behavior inemerging Islamic stock market of Indonesia using daily return of Indonesia CompositeIndex and JII from October 6, 2000 to October 5, 2018. Herding behavior could generallytrigger shifting market prices from equilibrium values. Herding behavior may beidentified from the relation between stock return dispersion and market return. Stockreturn dispersion is measured using Cross Sectional Absolute Deviation or CSAD.Generalized Auto Regressive Conditional Heteroskedasticity or GARCH method isused to detect herding behavior. GARCH does not see heteroskedasticity as a problem,instead uses it to make a model. The result indicates that herding behavior exist inIslamic stock market of Indonesia. Asymmetric herding occurs in Indonesia Islamicstock market where herding behavior exists during falling market condition only.


Author(s):  
Nabil Sifouh ◽  
Khdija Oubal

The purpose of this paper is to test empirically, if during the period 2002-2017, the volatility of the Moroccan stock market could be linked to mimetic behavior of investors. On a sample made up of 22 firms listed on the Casablanca stock exchange, we adopted an estimate of this behavior according to the measure of cross sectional absolute deviation CSAD to show that there is no solid evidence on the presence of mimicry at least for the period considered in this study.


1999 ◽  
Vol 38 (4II) ◽  
pp. 777-786 ◽  
Author(s):  
Attiya Y. Javed ◽  
Ayaz Ahmed

Stock markets are highly reactive to internal and external developments. News of major events take no time to impact, the Stock Exchange that quite often serves as a barometer of the good and bad for the market. The importance of particular events and their effect on the stock market has been a subject of study in financial literature. Such studies attempt to assess the extent to which stock markets’ performance stray’s from the normal around the time of the occurrence of subject events. The stock market crash in the USA of October 1987 and related crash in the Far East later in January 1998 led to several studies of the event.


2020 ◽  
Vol 13 (10) ◽  
pp. 226
Author(s):  
Imran Yousaf ◽  
Shoaib Ali ◽  
Wing-Keung Wong

This study employs the Vector Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (VAR-AGARCH) model to examine both return and volatility spillovers from the USA (developed) and China (Emerging) towards eight emerging Asian stock markets during the full sample period, the US financial crisis, and the Chinese Stock market crash. We also calculate the optimal weights and hedge ratios for the stock portfolios. Our results reveal that both return and volatility transmissions vary across the pairs of stock markets and the financial crises. More specifically, return spillover was observed from the US and China to the Asian stock markets during the US financial crisis and the Chinese stock market crash, and the volatility was transmitted from the USA to the majority of the Asian stock markets during the Chinese stock market crash. Additionally, volatility was transmitted from China to the majority of the Asian stock markets during the US financial crisis. The weights of American stocks in the Asia-US portfolios were found to be higher during the Chinese stock market crash than in the US financial crisis. For the majority of the Asia-China portfolios, the optimal weights of the Chinese stocks were almost equal during the Chinese stock market crash and the US financial crisis. Regarding hedge ratios, fewer US stocks were required to minimize the risk for Asian stock investors during the US financial crisis. In contrast, fewer Chinese stocks were needed to minimize the risk for Asian stock investors during the Chinese stock market crash. This study provides useful information to institutional investors, portfolio managers, and policymakers regarding optimal asset allocation and risk management.


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