This document analyzes the relationship between stock market liquidity and stock returns in 27 emerging equity markets from January 1992 to December 1999. It finds that stock returns are positively correlated with measures of market liquidity, including turnover ratio, trading value, and turnover-volatility multiple, in both cross-sectional and time-series analyses. This relationship holds even after controlling for other factors and contrasts with theories supported by studies of developed markets, where liquidity and returns are negatively correlated. The findings suggest emerging markets have a lower degree of integration with the global economy.
The research studies the impact of the exchange rate fluctuations of the local currency on the share dividends exchanged in the stock market, and stating whether there is a trace of the fluctuations occurring in the exchange rate on the fluctuations reflected on the stock returns in the stock market – during the political and economic crisis in Syria. The descriptive analytical approach was adopted to indicate whether there is any direct or indirect impact of fluctuations in the exchange rate of the pound (Lira) against the dollar on the exchange value of the Damascus Securities Exchange Index. The study community consists of all stock companies listed in Damascus Securities Exchange. It covers the total of 23 listed companies. It relied on the period from 1/7/2011 through 12/31/2013 to study the impact of exchange rate fluctuations on stock returns, where the crisis began on 18/03/2011, but reflections on economic life began to appear in mid-2011 when the severe fluctuations in the exchange rate and returns began as a result of lack of stability and economic siege Syria has been witnessing and the study stretched until the year 2013. The data is a sort of daily observations of each of the dependent and independent variable sending with 381 observations. The study reached the many results some of which include that there is an inverse weak between the Syrian pound exchange rate and Damascus Securities Exchange Index returns. The inefficiency of Damascus Securities Exchange Index on the weak level, where, as we have seen, this index is not subject to normal distribution and it is auto-correlated of the third degree and does not settle at the first level; instead, it settles at the first change.
Economic Integration of Pakistan: An Empirical Test of Purchasing Power Parityinventionjournals
This paper empirically analyses the substantiation of (PPP) purchasing power parity theory in Pakistan. For finding the associationin exchange rate’sprecariousness and inflation rate’sdisparity between Pakistan and its thirteen major trading partners, study used OLS method, and for long run relationship applied co-integration, error correction model and panel co-integration technique over the time span of 1972Q1- 2012Q3. OLS results are shown very small values of R 2 . But co-integration, Unit root test results and Panel tests’ results revealed the existence of long run equilibrium relationship between Pakistan and sample countries. The error correction terms also exposed and confirmed the speed of adjustment from disequilibrium to long run equilibrium condition at significant level.Panel unit root test and panel co-integration test by Pedroni also revealed that expected inflation rate differential have a positive andsignificant effect on exchange rate change between Pakistan and its trading partners during the sample period. The results also provided thestrong evidence thateconomic integration between foreign exchange markets and commodity markets among the sample countries is very high. For getting the proper fruits of globalization it is required to enhance the canvas of exports quantity and numbers of export items
The research studies the impact of the exchange rate fluctuations of the local currency on the share dividends exchanged in the stock market, and stating whether there is a trace of the fluctuations occurring in the exchange rate on the fluctuations reflected on the stock returns in the stock market – during the political and economic crisis in Syria. The descriptive analytical approach was adopted to indicate whether there is any direct or indirect impact of fluctuations in the exchange rate of the pound (Lira) against the dollar on the exchange value of the Damascus Securities Exchange Index. The study community consists of all stock companies listed in Damascus Securities Exchange. It covers the total of 23 listed companies. It relied on the period from 1/7/2011 through 12/31/2013 to study the impact of exchange rate fluctuations on stock returns, where the crisis began on 18/03/2011, but reflections on economic life began to appear in mid-2011 when the severe fluctuations in the exchange rate and returns began as a result of lack of stability and economic siege Syria has been witnessing and the study stretched until the year 2013. The data is a sort of daily observations of each of the dependent and independent variable sending with 381 observations. The study reached the many results some of which include that there is an inverse weak between the Syrian pound exchange rate and Damascus Securities Exchange Index returns. The inefficiency of Damascus Securities Exchange Index on the weak level, where, as we have seen, this index is not subject to normal distribution and it is auto-correlated of the third degree and does not settle at the first level; instead, it settles at the first change.
Economic Integration of Pakistan: An Empirical Test of Purchasing Power Parityinventionjournals
This paper empirically analyses the substantiation of (PPP) purchasing power parity theory in Pakistan. For finding the associationin exchange rate’sprecariousness and inflation rate’sdisparity between Pakistan and its thirteen major trading partners, study used OLS method, and for long run relationship applied co-integration, error correction model and panel co-integration technique over the time span of 1972Q1- 2012Q3. OLS results are shown very small values of R 2 . But co-integration, Unit root test results and Panel tests’ results revealed the existence of long run equilibrium relationship between Pakistan and sample countries. The error correction terms also exposed and confirmed the speed of adjustment from disequilibrium to long run equilibrium condition at significant level.Panel unit root test and panel co-integration test by Pedroni also revealed that expected inflation rate differential have a positive andsignificant effect on exchange rate change between Pakistan and its trading partners during the sample period. The results also provided thestrong evidence thateconomic integration between foreign exchange markets and commodity markets among the sample countries is very high. For getting the proper fruits of globalization it is required to enhance the canvas of exports quantity and numbers of export items
This paper empirically examines the role of uncertainty occurred by ‘news’ in Japanese financial markets. A GARCH-MIDAS model is used for estimation. It finds that news-based implied volatility performs well in predicting long-term aggregate market volatilities. A subsample analysis provides that the predictive power of news-based volatility is continuing, as most of the coefficients are positive and significant. So, in general, the news based implied volatility model is associated with high market volatility. Moreover, stock market prices go on rising, different effects that appeared in each subsample period. On the recent period, when Abenomics was conducted, the effect decreased. Also, the effect of exchange rates decrease in short time. When stock prices decrease, volatilities of the stock prices in the past period increase. There is some possibility that markets were too unstable about the movements because of the low prices. Also, the volatility of long-term interest rates increases when the interest rate declines in the recent period under Abenomics. Although interest rates have been quite low in both sample periods, the Bank of Japan (BOJ) started to manage long-term interest rates in the recent period, so market participants seem to begin noticing the movements.
American Journal of Multidisciplinary Research and Development is indexed, refereed and peer-reviewed journal, which is designed to publish research articles.
Financial integration between BRICS and developed stock marketsinventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Is the Saudi Stock Market Efficient? A case of Weak-form EfficiencyHamad Alzeera
Stock Market Efficiency, Weak-form market efficiency, Efficient Market Hypothesis, Random Walk Hypothesis, unit root test, auto correlation, runs test, Kingdom of Saudi Arabia
Links: http://www.iiste.org/Journals/index.php/RJFA/article/view/5647
http://www.iiste.org/Journals/index.php/RJFA/article/view/5647/5759
This study investigates the impact of the introduction of index options on emerging market volatility in the context of Malaysia. Company specific daily closing prices for 29 listed companies were examined to determine the conditional volatility shifts before and after the introduction of index options. Multiple window periods are examined to avoid year-end effects.The exponential generalized autoregressive conditional heteroskedasticity (EGARCH) (1.1) model is used to determine the conditional volatility shift before and after the introduction of index options in Malaysia. The findings of this study suggest that the introduction of index options reduced market volatility in the Malaysia equity market at the 0.01 level of statistical significance. Further, this study contributed to extant literature because it uses company-specific daily equity price data and no such previous study exists on the impact of index options for this important emerging market. The study will be useful for academics, researchers, domestic and foreign investors and policy-makers, among others.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
Predicting Stock Market Returns and the Efficiency Market HypothesisMysa Vijay
There has been growing interest on financial forecasting in recent years as accurate
forecasting of financial prices has become an important issue in investment decision making
Lu et al. (2009). It is argued that exchange rate market is very efficient (Ince and Trafalis
2006). By predicting the accurate results in return, it gives the opportunity for investors to
make profitable decisions for investing their money. As many of the researchers argue that
efficiency market hypothesis theory is not true.
Investors of the stock market are “rational” and they adapt easily to recent knowledge
regarding the stock market products, which implies investors opinion in the market follow the
effects of any information revealed. This efficient market hypothesis contains three different
levels of information sharing: the weak form, the semi-strong form, and the strong form Fama
and French (2009). Many trials have been made to calculate the movements of stock markets
using quantitative information (Andersen et al. 2007; Fama and French 2009; Nartea 2009),
but some other studies state that stock market movements cannot be captured by firm‟s
quantitative information Shleifer and Vishny (1997). This is because the actual market is not
as efficient as the expressed by the theory of efficiency market hypothesis.
We want to investigate the above addressed problem as we want to see if can predict stock
market returns and test the theory of efficiency market hypothesis. We want to see if we can
predict the stock market returns in future and see how accurate we are in predicting in it.
After predicting we want to test the theory on efficiency market hypothesis on the predicted
values, as the market is not as efficient as expressed by efficiency market hypothesis.
In order to reach our goal, we selected three papers as our base line papers. Our aim is follow
their methodologies and their algorithms and predict the stock market returns. We implement
and reproduce the efforts done by Lu et al. (2009), Khansa and Liginlal (2009) and Ince and
Trafalis (2006). We want to know how close the mentioned authors‟ results were to the actual
data. In addition to that, we also want to know how close we are to the actual data.
This paper empirically examines the role of uncertainty occurred by ‘news’ in Japanese financial markets. A GARCH-MIDAS model is used for estimation. It finds that news-based implied volatility performs well in predicting long-term aggregate market volatilities. A subsample analysis provides that the predictive power of news-based volatility is continuing, as most of the coefficients are positive and significant. So, in general, the news based implied volatility model is associated with high market volatility. Moreover, stock market prices go on rising, different effects that appeared in each subsample period. On the recent period, when Abenomics was conducted, the effect decreased. Also, the effect of exchange rates decrease in short time. When stock prices decrease, volatilities of the stock prices in the past period increase. There is some possibility that markets were too unstable about the movements because of the low prices. Also, the volatility of long-term interest rates increases when the interest rate declines in the recent period under Abenomics. Although interest rates have been quite low in both sample periods, the Bank of Japan (BOJ) started to manage long-term interest rates in the recent period, so market participants seem to begin noticing the movements.
American Journal of Multidisciplinary Research and Development is indexed, refereed and peer-reviewed journal, which is designed to publish research articles.
Financial integration between BRICS and developed stock marketsinventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Is the Saudi Stock Market Efficient? A case of Weak-form EfficiencyHamad Alzeera
Stock Market Efficiency, Weak-form market efficiency, Efficient Market Hypothesis, Random Walk Hypothesis, unit root test, auto correlation, runs test, Kingdom of Saudi Arabia
Links: http://www.iiste.org/Journals/index.php/RJFA/article/view/5647
http://www.iiste.org/Journals/index.php/RJFA/article/view/5647/5759
This study investigates the impact of the introduction of index options on emerging market volatility in the context of Malaysia. Company specific daily closing prices for 29 listed companies were examined to determine the conditional volatility shifts before and after the introduction of index options. Multiple window periods are examined to avoid year-end effects.The exponential generalized autoregressive conditional heteroskedasticity (EGARCH) (1.1) model is used to determine the conditional volatility shift before and after the introduction of index options in Malaysia. The findings of this study suggest that the introduction of index options reduced market volatility in the Malaysia equity market at the 0.01 level of statistical significance. Further, this study contributed to extant literature because it uses company-specific daily equity price data and no such previous study exists on the impact of index options for this important emerging market. The study will be useful for academics, researchers, domestic and foreign investors and policy-makers, among others.
IOSR Journal of Humanities and Social Science is an International Journal edited by International Organization of Scientific Research (IOSR).The Journal provides a common forum where all aspects of humanities and social sciences are presented. IOSR-JHSS publishes original papers, review papers, conceptual framework, analytical and simulation models, case studies, empirical research, technical notes etc.
Predicting Stock Market Returns and the Efficiency Market HypothesisMysa Vijay
There has been growing interest on financial forecasting in recent years as accurate
forecasting of financial prices has become an important issue in investment decision making
Lu et al. (2009). It is argued that exchange rate market is very efficient (Ince and Trafalis
2006). By predicting the accurate results in return, it gives the opportunity for investors to
make profitable decisions for investing their money. As many of the researchers argue that
efficiency market hypothesis theory is not true.
Investors of the stock market are “rational” and they adapt easily to recent knowledge
regarding the stock market products, which implies investors opinion in the market follow the
effects of any information revealed. This efficient market hypothesis contains three different
levels of information sharing: the weak form, the semi-strong form, and the strong form Fama
and French (2009). Many trials have been made to calculate the movements of stock markets
using quantitative information (Andersen et al. 2007; Fama and French 2009; Nartea 2009),
but some other studies state that stock market movements cannot be captured by firm‟s
quantitative information Shleifer and Vishny (1997). This is because the actual market is not
as efficient as the expressed by the theory of efficiency market hypothesis.
We want to investigate the above addressed problem as we want to see if can predict stock
market returns and test the theory of efficiency market hypothesis. We want to see if we can
predict the stock market returns in future and see how accurate we are in predicting in it.
After predicting we want to test the theory on efficiency market hypothesis on the predicted
values, as the market is not as efficient as expressed by efficiency market hypothesis.
In order to reach our goal, we selected three papers as our base line papers. Our aim is follow
their methodologies and their algorithms and predict the stock market returns. We implement
and reproduce the efforts done by Lu et al. (2009), Khansa and Liginlal (2009) and Ince and
Trafalis (2006). We want to know how close the mentioned authors‟ results were to the actual
data. In addition to that, we also want to know how close we are to the actual data.
Any business and economic applications of forecasting involve time series data. Re-gression models can be fit to monthly, quarterly, or yearly data using the techniques de-scribed in previous chapters. However, because data collected over time tend to exhibit trends, seasonal patterns, and so forth, observations in different time periods are re¬lated or autocorrelated. That is, for time series data, the sample of observations cannot be regarded as a random sample. Problems of interpretation can arise when standard regression methods are applied to observations that are related to one another over time. Fitting regression models to time series data must be done with considerable care.
Report earned 105% and is a complete valuation of the company based upon CAPM and the Dividend Discount Models. Includes regression analysis of macro variables, figures from conference calls and 10Ks, and a fair market stock price. (Not to be used as investment advice)
Does Liquidity Masquerade As Size FinalAshok_Abbott
he empirical results presented in this paper suggest a strong role for liquidity in explaining higher raw and excess returns realized by investors in less liquid stocks. Size effect has been studied extensively and it has been suggested that it may be a proxy for another unobserved factor. Our results strongly suggest that liquidity may be that unobserved factor explaining a large part but not all of the size premium.
Tangible market information and stock returns the nepalese evidence synopsisSudarshan Kadariya
This is a synopsis of the work done for the academic fulfillment purpose. The study have assumptions. The findings are suggested to related with its assumptions. I believe this work will help the financial / stock market in Nepal and it will also be accessible and share some features to the international financial market researchers.
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
The Granger causality model is used in the current study to analyze the short-run cause–effect relationship between two stock market indices between 2001 and 2021 using time series data of the daily closing prices of the BSE Sensex and S&P 500 indices listed in the Indian and US stock markets, respectively. The Granger causality model and the augmented Dickey–Fuller test for data stationarity were used in the study to examine the short-term causal link between two market indices during the time period. The outcomes demonstrated the connection between the Indian and US stock markets. The findings imply that both markets have a dynamic, bidirectional relationship. This study provides the investor’s essential inputs for investment decision-making and portfolio diversification. In the current era of globalization, the study is crucial because investors and fund managers now place a high priority on stock market integration. Through fund diversification across equity markets, this study subsequently makes it easier to reduce portfolio risk by providing useful insights on diversification strategies across the stock markets.
This article seeks to examine the impact of the Bangladesh’s stock market development on its economic growth from the period of 1989-2012. We have used Johansen Cointegration test to estimate the long-run equilibrium relationship between the variables and the Granger causality test was conducted in order to establish causal relationship, while the model was estimated using the error correction model (ECM). Johansen co-integration test results show that the Bangladesh’s stock market development and economic growth are co-integrated. This indicates that a long run relationship exists between stock market development and economic growth in Bangladesh. The causality test results suggest a unidirectional causality from stock market development to the economic growth. On the other hand, there is no “reverse causation” from economic growth to stock market development. The evidence from this study reveals that the activities in the stock market tend to impact positively on the economy. It is recommended therefore that stock market regulatory authority should therefore address policy issues that are capable of boosting the investors’ confidence through improved policy formulation and creation of awareness.
American Journal of Multidisciplinary Research and Development is indexed, refereed and peer-reviewed journal, which is designed to publish research articles.
Evaluation of the Development and Performance of Selected GCC and Non-GCC St...Mace Abdullah
This paper compares and contrasts the stock markets for countries of comparable size and development as and between the GCC and non-GCC countries. The paper implicitly observes what may be considered strengths and weakness as and between markets dominated by Islamic Finance principles and those that are more or less conventionally oriented.
Does Economic Growth Affect Capital Market Development In Nigeria? 1985 – 2016AJHSSR Journal
The goal of this paper is to assess the impact of economic growth affects capital market
development in Nigeria using annualised data from 1986-2016. We employed that Johansen cointegration
technique to determine if our variables are cointegrated. The error correction model (ECM) was employed to
estimate our dynamic short and long-run model. Various diagnostic tests were also conducted to confirm the
validity of our results. The results indicate that there is a long-run relationship between economic growth and
capital market development. The baseline estimator further showed that economic growth has significant
positive influence on capital market development. We also found that inflation has significant negative impact
on the capital market while money supply was found to have insignificant effect on the dependent variable. The
error correction term showed evidence of slow speed of adjustment toward long-run equilibrium, with deviation
from equilibrium corrected at the speed of 2.3 percent on annual basis. We conclude that economic growth
indeed drives capital market development in Nigeria. And were recommend that policies aimed at facilitating
economic activities should be pursued by the monetary authorities, the government and policymakers to further
enhance the development of Nigerian capital market
Whitepaper from CFTC Analyzing Correlations Between Equities And CommoditiesJohn Farrall, CFA
Research finding that the relation between the
returns on investable commodity and U.S. equity indices has not changed
significantly in the last fifteen years. Refutes the theory of a "market of one" where under stress all asset classes move together.
Determinants of equity share prices of the listed company in dhaka stock exch...MD. Walid Hossain
This is the finance academic project report.This report prepare by MD. WALID HOSSAIN, Patuakhali science and technology University, Faculty of business administration and management. i think that is helpful for business studies students.
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
how to sell pi coins effectively (from 50 - 100k pi)DOT TECH
Anywhere in the world, including Africa, America, and Europe, you can sell Pi Network Coins online and receive cash through online payment options.
Pi has not yet been launched on any exchange because we are currently using the confined Mainnet. The planned launch date for Pi is June 28, 2026.
Reselling to investors who want to hold until the mainnet launch in 2026 is currently the sole way to sell.
Consequently, right now. All you need to do is select the right pi network provider.
Who is a pi merchant?
An individual who buys coins from miners on the pi network and resells them to investors hoping to hang onto them until the mainnet is launched is known as a pi merchant.
debuts.
I'll provide you the Telegram username
@Pi_vendor_247
what is the best method to sell pi coins in 2024DOT TECH
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...beulahfernandes8
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
The Evolution of Non-Banking Financial Companies (NBFCs) in India: Challenges...beulahfernandes8
Role in Financial System
NBFCs are critical in bridging the financial inclusion gap.
They provide specialized financial services that cater to segments often neglected by traditional banks.
Economic Impact
NBFCs contribute significantly to India's GDP.
They support sectors like micro, small, and medium enterprises (MSMEs), housing finance, and personal loans.
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
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1. Emerging Markets Review 4 (2003) 1–24
Liquidity and stock returns in emerging equity
markets
Sang-Gyung Juna, Achla Maratheb, Hany A. Shawkyc,*
aSchool of Business, Hanyang University, Seoul, South Korea
bLos Alamos National Laboratory, Los Alamos, NM 87545, USA
cSchool of Business, University at Albany, SUNY, Albany, NY 12222, USA
Received 21 May 2002; received in revised form 29 November 2002; accepted 9 December 2002
Abstract
Using data for 27 emerging equity markets for the period January 1992 through December
1999, we document the behavior of liquidity in emerging markets. We find that stock returns
in emerging countries are positively correlated with aggregate market liquidity as measured
by turnover ratio, trading value and the turnover–volatility multiple. The results hold in both
cross-sectional and time-series analyses, and are quite robust even after we control for world
market beta, market capitalization and price-to-book ratio. The positive correlation between
stock returns and market liquidity in a time-series analysis is consistent with the findings in
developed markets. However, the positive correlation in a cross-sectional analysis appears to
be at odds with market microstructure theory that has been empirically supported by studies
on developed markets. Our findings regarding the cross-sectional relation between stock
returns and liquidity is consistent with the view that emerging equity markets have a lower
degree of integration with the global economy.
! 2002 Elsevier Science B.V. All rights reserved.
JEL classifications: F20; F21; G12; G15
Keywords: Market liquidity; Turnover ratio; Emerging markets; Stock market returns
*Corresponding author. Tel.: q1-518-442-4921; fax: q1-518-442-3045.
E-mail addresses: h.shawky@albany.edu (H.A. Shawky), sjun@hanyang.ac.kr (S.-G. Jun),
achla@lanl.gov (A. Marathe).
1566-0141/03/$ - see front matter ! 2002 Elsevier Science B.V. All rights reserved.
PII: S1566-0141Ž02.00060-2
2. 2 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
1. Introduction
The importance of emerging equity markets in the context of investment portfolios
and international diversification has received considerable attention. Six emerging
markets rank among the top 20 markets in the world in terms of capitalization.1
With respect to trading value, Taiwan, Korea, and Malaysia were among the 10
most active markets during 1998. Furthermore, trading in these three markets is not
concentrated in a few companies. Many emerging markets trade a large number of
domestic companies. For example, as of December 2000, there were approximately
6000 companies listed in India, second only to the US, Korea has more companies
listed than either France or Germany. Nevertheless, many emerging markets are still
very concentrated, with high trading costs and low trading volume.2
We investigate the time-series variation in aggregate liquidity for several emerging
equity markets and also examine the cross-sectional behavior of liquidity across
countries. Aggregate liquidity, as opposed to the cross-sectional analysis of individual
securities in traditional microstructure theory, is critical because it is related to the
important issue of whether liquidity is a priced factor in global equity markets. We
find that stock returns in emerging countries are positively correlated with market
liquidity as measured by turnover ratio, trading value, and turnover–volatility
multiple. The results hold in both cross-sectional and time-series analyses, and are
quite robust even after we control for world market beta, market capitalization and
price-to-book ratio.3
The positive correlation between stock returns and market liquidity in a time-series
analysis is consistent with the findings for developed markets. However, the
positive correlation between stock returns and liquidity in the cross-sectional analysis
appears to be at odds with market microstructure theory that has been empirically
supported by studies on developed markets.4 Evidently, our study identifies what
seems to be a unique characteristic of stock returns in emerging equity markets.
It is important to emphasize that the notion of liquidity for individual assets is
quite different from the notion of liquidity of an overall equity market. While supply
and demand conditions determine liquidity in both cases, the factors that characterize
the supply and demand functions for individual assets within a market are different
from the factors that characterize the liquidity of a country’s equity market. Whereas
unique individual security characteristics determine its relative liquidity, the liquidity
of a country’s equity market is largely determined by macroeconomic factors that
are systemic to the economy.5 Moreover, the assessment of liquidity in a given
1 These countries are Malaysia, South Africa, Mexico, Korea, Singapore and Thailand.
2 For example, of the 6000 companies listed in the Indian stock exchange, only 1500 trade on a daily
basis. Moreover, the top 200 companies account for almost 95% of daily traded volume.
3 These variables have been identified in the literature (Fama and French, 1995, 1996) as having
explanatory power for stock returns.
4 Section 2 discusses the relationship between liquidity and stock returns in greater detail.
5 For instance, legal, political as well as macroeconomic factors are likely to play an important role.
3. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 3
equity market relative to other markets is likely to have significant implications
regarding the flow of capital and hence growth and development of that market.
A potential explanation for the positive correlation between liquidity and emerging
stock market returns can be made from the perspective of lower level of global
market integration. While Longin and Solnik (1995) report an overall increase in
the correlation structure among developed markets, Bekaert and Harvey (1997) find
evidence for varying degrees of integration of emerging equity markets with the
world economy. If emerging markets are not fully integrated with the global
economy, lack of liquidity will not function as a risk factor, and thus cross-sectional
returns will not necessarily be lower for liquid markets. In this sense, our findings
are supportive of the view that emerging equity markets have a lower degree of
integration with the global economy.
The remainder of this paper is organized in four sections. Section 2 reviews the
existing literature on the relation between stock returns and market liquidity. Section
3 presents the data and descriptive statistics on 27 emerging equity markets for the
period January 1992 through December 1999. We present several measures of stock
market liquidity and examine their behavior over time. Section 4 presents the
empirical methodology and provides a discussion and interpretation of our empirical
findings. After addressing several estimation issues, we estimate the relation between
liquidity and stock returns using an ordinary least squares (OLS) regression.
Robustness tests are conducted using regional classifications and an estimation of a
multivariate vector autoregressive (VAR). The final section provides a brief summary
of the paper and some concluding remarks.
2. Market liquidity and stock returns
There is a large body of research that supports the view that the liquidity of
securities affects their expected returns. The influence of trading costs on required
returns examined by Amihud and Mendelson (1986), Brennan and Subrahmanyam
(1996), Jacoby et al. (2000) implies a direct link between liquidity and corporate
cost of capital. Those studies present a model showing that liquidity, marketability
or transactions costs influence investors’ portfolio decisions. Since rational investors
require a higher risk premium for holding illiquid securities, cross-sectional risk-adjusted
returns are lower for liquid stocks. This proposition has been empirically
supported in various studies on mature capital markets.
Amihud and Mendelson (1989) conduct cross-sectional analyses of US stock
returns and show that risk-adjusted returns are decreasing with respect to liquidity,
as measured by the bid-ask spread. Brennan et al. (1998) investigate the relation
between expected returns and several firm characteristics including market liquidity,
as measured by trading volume. They find a significant negative relation between
returns and trading volume for both NYSE and NASDAQ stocks, thus linking
expected returns and liquidity. Amihud et al. (1997) report that liquidity improve-ment
on the Tel Aviv Stock Exchange was associated with a positive and permanent
price appreciation. Datar et al. (1998) use turnover rate as a measure of liquidity,
4. 4 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
and provide evidence for a negative correlation between liquidity and stock
returns.6
Haugen and Baker (1996) report that the liquidity of stocks is one of several
common factors in explaining stock returns across global markets. They report that
the cross-sectional stock returns in developed markets have common determinants
from period to period and from country to country, and that the liquidity of stocks
is one of the important determinants of stock returns. Estrada (2001) shows that the
semi-deviation with respect to the mean is a useful variable in explaining the cross-section
of industry returns in emerging markets. He further indicates that the semi-deviation
might be a plausible variable to be used in a CAPM framework to
compute the cost of equity in emerging markets.
The economic development literature establishes a strong link between financial
markets development and economic growth. For instance, Atje and Jovanovic
(1993), Levine and Zervos (1998) and Levine (1997) suggest that well-functioning
stock markets enhance the liquidity of capital investment and thus promote long-run
economic growth. The role of stock markets is emphasized more strongly in
developing countries, where the need for funding profitable long-term investments
is stronger than in developed countries.
A number of recent papers examine the valuation effects of stock market
liberalization in emerging markets. They show evidence that stock market liquidity
positively predicts growth, capital accumulation, and productivity improvements,
and that liquid equity markets provide important requisites for economic growth.
Bekaert and Harvey (1997, 2000) show that liberalization tends to decrease
aggregate dividend yields and suggest that the price changes reflect a change in the
cost of capital rather than a change in earnings of firms. They further point out that
while capital market integration process reduces the cost of capital in emerging
markets, that reduction is far less than we expected.
Henry (2000a,b) provide the most detailed empirical evidence in support of the
hypothesis that stock market liberalization in emerging countries will cause the
country’s aggregate cost of capital to decline, leading to an equivalent increase in
the country’s equity price index. On average, in the 8-month period preceding
market liberalization, a country’s aggregate share price index experiences a 38%
increase in real dollar terms. Using data for the same time period but employing a
slightly different sample, Kim and Singal (2000) provide very similar evidence that
emerging stock market returns are abnormally high in the 8 months period leading
to liberalization events.
Given that stock market liquidity in emerging countries is positively related to
economic growth, liberalization policies, and the level of global integration, it is
quite reasonable to expect that markets with higher levels of aggregate liquidity
would also have higher stock valuations relative to other markets. The rationale
6 For instance, Amihud et al. (1990) report that during the stock market crash of October 19th 1987,
price declines were greater for illiquid stocks, and price recoveries were greater for liquid stocks.
Furthermore, returns on US Treasuries are also found to be increasing with respect to the bid-ask spread
as demonstrated in Amihud and Mendelson (1991), Kamara (1994). For a comprehensive examination
of stock market liquidity over time, see Jones (2000).
5. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 5
behind this argument can be supported either on the basis of the positive relationship
between liquidity and economic growth or on the basis of the observed positive
relation between an increase in market liberalization policies and overall market
liquidity. Using data for 27 emerging equity markets for the period January 1992
through December 1999, we provide evidence in support of this conjecture.
3. Data and measures of stock market liquidity
3.1. Data
The primary source for the data used in this study is the Emerging Market
Database, part of the International Financial Statistics, originally compiled and
maintained by the World Bank. Beginning with 1998, the Emerging markets database
is being maintained by Standard & Poor’s. We use monthly data for 27 emerging
equity markets covering the period January 1992 through December 1999.7 The
monthly returns on US equity indices were obtained from CRSP. We also used
regional Morgan Stanley World Index (MSCI), as a proxy for the returns on the
world market index. For comparability purposes, all the return data used in this
study are in terms of US dollars.
Table 1 presents a statistical snapshot on the 27 emerging markets examined in
this study. Each variable is recorded as of December 1999 to depict the present
situation of each of the markets examined.8 The first column gives the number of
listed companies in the major stock exchange in each country. The columns that
follow provide data on market capitalization, trading value in millions of US dollars,
turnover ratio, turnover to standard deviation (S.D.) ratio, monthly stock returns,
number of IFC stocks, the ratio of IFC stocks to total capitalization (column 9), the
ratio of IFC stock to total trading value (column 10), PE ratio, PB ratio, the average
dividend yield and the country’s currency exchange rate for the month.
The turnover ratio (column 5) is calculated as the ratio of trading value (column
3) over market capitalization (column 2). The S.D. of returns used in column 6
was calculated using a trailing twelve monthly returns for each of the emerging
equity markets.9 The IFC data (denoted as S&P IFCG beginning with year 1999)
in columns 8, 9 and 10 represent the same variables pertaining to the set of firms
that the World Bank would classify as sufficiently liquid or marketable to be
included in the emerging market index developed by the Bank.
Table 2 provides descriptive statistics on the variables that will be employed in
our regression estimation. As expected, all the variables in Table 2 appear to exhibit
a reasonable mean value, but with extreme values for the minimum and maximum.
7 In the Factbook of year 2000 on Emerging Stock Markets published by S&P, the number of
country’s that are identified by the International Finance Corporation (IFC) as emerging markets are
54. However, a trade-off between the length of the time-series needed and the number of markets to be
included in the analysis is unavoidable.
8 Data for Portugal was not available for the year 1999. The values reported for Portugal are as of
December 1998.
9 This methodology for estimating volatility was originally suggested by Schwert (1989).
7. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 7
Table 1 (Continued)
Country Number Market Trading Turnover Turnovery Mean Number IFC cap. IFC trade PyE PyB Dividend Exchange
of listed cap. value ratio S.D. monthly IFC (%) (%) ratio ratio yield rate
companies (mil. $) (mil. $) ratio return (%) stocks
Taiwan 462 375 991 92 725 25.80 3.09 9.40 106 67.80 58.60 52.50 3.40 0.60 31.35
Thailand 392 58 365 2630 4.80 0.39 14.20 64 74.20 66.40 y12.20 2.10 0.30 37.58
Turkey 285 112 716 18 524 21.20 0.81 79.80 53 78.80 59.50 34.60 8.90 1.10 542 400.00
Venezuela 87 7471 42 0.60 0.04 5.00 16 59.10 36.80 10.80 0.40 6.20 648.75
Zimbabwe 70 2514 20 0.80 0.12 11.10 22 68.90 65.00 10.80 3.00 2.50 37.95
This table presents a snapshot as of December 1999 of the 27 emerging markets examined in this study.
a As of December 1998.
8. 8 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 2
Descriptive statistics for emerging equity markets
Variable N Mean S.D. Minimum Maximum
Return (%) 2460 2.279 11.808 y40.100 135.200
MSCI (%) 96 1.195 3.611 y16.757 7.625
Turnover ratio 2460 5.085 6.826 0.000 61.800
Log trading value 2460 6.464 2.454 y1.204 12.094
TurnoveryS.D. 2163 0.588 0.800 0.000 11.623
PyB ratio 2436 2.188 1.316 0.300 20.000
Market cap. (bil. $) 2460 10.1290 1.585 5.398 13.933
This table provides descriptive statistics of pooled monthly data for all 27 emerging equity markets
over the period January 1992 through December 1999. Returns are monthly returns of stock indices,
MSCI is the monthly change in MSCI. The turnover ratio is the ratio of trading value to market capi-talization,
trading value is the natural log of trading value in million dollars, turnoveryS.D. is turnover
ratio divided by S.D. calculated using a trailing 12 month returns. The PyB ratio is the price-to-book-value
ratio, and market cap. is market capitalization value.
For instance, a mean monthly return of 2.279% is somewhat high, but still reasonable
for that period of time. However, a maximum value of 135% in a given month
appears unreasonable but it did occur.10 It should be noted that variables expressed
as a ratio are far less likely to suffer from extreme values than absolute value
numbers. For instance, the turnover ratio, turnover–volatility and PB ratio are likely
to behave better under statistical testing than the other non-ratio variables.
Table 3 presents the correlation coefficients among the three liquidity variables
along with returns and other market variables. It is difficult to discern any particular
pattern of association between the variables based on Table 3. As expected however,
the liquidity measures are highly correlated with each other. A particularly significant
Table 3
Correlation coefficients among variables
Return MSCI Turnover Trading Turnovery PyB Market
ratio volume S.D. ratio cap.
Return 1.00 0.31* 0.18* 0.06* 0.08 0.10* 0.01
MSCI 1.00 0.03 0.02 0.04 0.03 0.02
Turnover ratio 1.00 0.61* 0.85* 0.24* 0.35*
Log trading value 1.00 0.60* 0.27* 0.90*
TurnoveryS.D. 1.00 0.20* 0.41*
PyB ratio 1.00 0.22*
Market cap. 1.00
This table reports correlation coefficients among variables measured monthly across all 27 emerging
equity markets for the period January 1992 through December 1999. Return is monthly returns of stock
indices, MSCI is the monthly change in MSCI, turnover ratio is the ratio of trading value to market
capitalization, trading value is the natural log of trading value in million dollars, turnoveryS.D. is turnover
ratio divided by the S.D. of the trailing 12 month returns, PyB ratio is price-to-book-value ratio, and
market cap. is market capitalization value. Significance at the 5% level is indicated by *.
10 This value represents the monthly return for the market in China for August 1994.
9. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 9
correlation coefficient appears to be between the turnover ratio and stock returns.
Another important coefficient to note is the high level of correlation between trading
value and market capitalization. This high correlation between trading value and
size of the market underscores the importance of controlling for size when this
variable is used to measure liquidity.
3.2. Measures of stock market liquidity
Chordia et al. (2001) examine the time-series behavior of liquidity for stocks on
the NYSE. They suggest several aggregate measures of market liquidity as well as
several firm-specific variables related to bid-ask spreads as proxy for liquidity.
Unfortunately, none of these bid-ask spread variables are likely to be available in
an international context. Aggregate measures of market liquidity on a monthly basis
were obtained from the Emerging Markets Database published annually by the
World Bank.
Trading value of a given security is an increasing function of its liquidity, other
things being equal (Amihud and Mendelson, 1986). Aggregated over the whole
market, trading value provides a measure of a market’s liquidity. Trading values are
measured in million of US dollars using the exchange rate of each country. A more
comparable measure of liquidity across markets is the turnover ratio. While such a
measure does not directly capture the costs of buying and selling securities at quoted
prices, averaged over a long time period, the turnover ratio is likely to vary with
the ease of trading, hence with market liquidity.
Another less commonly used measure of market liquidity is the turnover–
volatility ratio. To construct this measure we divide the turnover ratio by the S.D.
of stock market returns. More liquid markets should be capable of handling high
volumes of trading without large price swings. This measure is essentially a
volatility-adjusted measure of a market’s turnover ratio. In the context of emerging
markets with relatively high levels of market volatility, this measure may be more
appropriate to use in estimating the fundamental relation between liquidity and
equity returns.
3.3. Market liquidity over time
The very nature of classifying an equity market as an ‘emerging market’ implies
that it is gaining in quality and efficiency as time goes by. In fact, the work of
Bekaert and Harvey (1997) clearly points out that the behavior of emerging markets
is changing significantly over time with respect to their degree of integration with
the global economy. It is, therefore, important to examine the possible changes in
the liquidity of these markets over time and to explore the impact of such changes
on equity returns.
To examine the time-series behavior of the liquidity variables, we run OLS
regressions for the three liquidity variables against a time trend. Table 4 presents
the estimated coefficients obtained from a panel regression using data for all of the
27 emerging markets for the entire sample period (January 1992 through December
10. 10 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 4
Time trend of liquidity variables
Dependent variable
Turnover ratio Trading value TurnoveryS.D.
Intercept 3.898* 5.605* 0.478*
(18.721) (56.013) (20.103)
Time variable 0.024* 0.017* 0.002*
(8.244) (20.367) (5.916)
Adj. R-squared 0.249 0.370 0.182
This table shows the results of time-series regression analysis to three liquidity variables, turnover
ratio (turnover), trading value (trade) and turnover-to-volatility ratio (turnoveryS.D.). We regress three
liquidity variables on period variable for each emerging market, and report average regression coefficients
and adjusted R-squared values. Significance at the 1% level is indicated by *.
1999). The results are quite compelling. All three liquidity variables are shown to
exhibit an increasing trend over time. This indicates that as a group, emerging
equity markets have, over the nineties, experienced increased levels of liquidity.
In order to better understand the behavior aggregate liquidity over time, we
standardize the three liquidity variables by dividing each series by the initial monthly
observation for each country. We then calculate the averages of the standardized
variables for each month across all of the 27 emerging markets and plot them in
Fig. 1. It is evident from the graph that all three liquidity measures have increased
over time implying an overall enhanced liquidity for emerging equity markets over
that period. This observed increase in the liquidity of emerging markets is likely to
have important implications for both the equity returns of these markets as well as
their behavior relative to the global economy over that time period.
While, in general, we document a significant increase in liquidity for emerging
markets as a group, it is important to note that not all markets have experienced the
same degree of liquidity improvements. To underscore the differences between the
various markets, we regress the liquidity variables against a time dummy separately
for each emerging market. Table 5 reports the estimated parameters along with their
t-statistic and the R-squared for each country. As indicated by significant positive
coefficients, improvements in liquidity over the period are observed for Brazil,
Greece, Hungary, India, Nigeria, Pakistan, Portugal, S. Africa, Taiwan, Turkey and
Zimbabwe. On the other hand, significant deterioration in liquidity over the same
period is observed for Argentina, Columbia, Jordan, Mexico, Peru, Sri Lanka,
Thailand and Venezuela. The remaining countries show mixed or statistically
insignificant changes in their liquidity over time.
4. Methodology and empirical results
4.1. Panel data regressions
To estimate the relation between aggregate liquidity and stock returns, we first
need to address several estimation issues in our time-series data. Using data for
11. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 11
Fig. 1. Time trend of liquidity variables. This graph shows the time trend of the three liquidity variables averaged for all of the 27 emerging markets for
the period January 1992 through December 1999. Each of the liquidity variables is standardized by dividing the series by the first period value for each
country. The plot shows the average of each variable in each month across countries.
12. 12 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 5
Time trend of liquidity variables for each emerging market
Country Liquidity variables Country Liquidity variables
Turnover Trade TurnoveryS.D. Turnover Trade TurnoveryS.D.
Argentina y0.033** y0.001 y0.001 Mexico y0.009* y0.005** y0.002*
(y5.563) (y0.478) (y0.442) (y2.327) (y3.370) (y2.236)
0.240 y0.008 y0.010 0.044 0.098 0.045
Brazil 0.013** 0.019** 0.004* Nigeria 0.005** 0.034** 0.002**
(2.717) (9.772) (2.492) (8.386) (11.866) (4.215)
0.063 0.499 0.058 0.422 0.595 0.166
Chile 0.003 0.0098* 0.000 Pakistan 0.261** 0.033** 0.023**
(1.643) (4.258) (0.664) (10.699) (22.229) (9.183)
0.018 0.153 y0.007 0.544 0.838 0.498
China y0.034 0.033** 0.013* Peru y0.037** 0.006* y0.003**
(y0.609) (8.348) (2.337) (y7.043) (2.429) (y4.331)
y0.008 0.453 0.058 0.369 0.056 0.198
Colombia y0.005** 0.004 y0.002** Philippine 0.015** 0.017** 0.000
(y3.019) (1.857) (y5.184) (4.412) (7.394) (0.752)
0.079 0.025 0.235 0.163 0.361 y0.005
Czech 0.013 0.001 y0.013** Poland y0.121** 0.028** 0.002
(1.342) (0.178) (y6.849) (y8.721) (9.311) (1.407)
0.013 y0.017 0.489 0.475 0.508 0.013
Greece 0.097** 0.052** 0.008** Portugal 0.048** 0.034** 0.012**
(13.330) (26.021) (4.955) (5.943) (14.018) (4.964)
0.650 0.877 0.219 0.293 0.702 0.247
Hungary 0.129** 0.084** 0.012** S. Africa 0.038** 0.028** 0.004**
(14.177) (28.341) (12.854) (16.766) (24.752) (5.300)
0.707 0.906 0.695 0.798 0.896 0.311
India 0.048** 0.022** 0.008** Sri Lanka y0.008* y0.007* y0.001
(8.100) (11.713) (10.854) (y2.382) (y2.211) (y1.512)
0.405 0.589 0.582 0.053 0.045 0.018
Indonesia 0.016** 0.016** y0.004** Taiwan 0.101** 0.017** 0.023**
(2.895) (5.494) (y2.673) (2.895) (8.395) (3.267)
0.072 0.235 0.068 0.072 0.422 0.103
Jordan y0.030** y0.011** y0.004** Thailand y0.059** y0.014** y0.006**
(y7.567) (y5.344) (y3.684) (y3.979) (y6.778) (y4.818)
0.372 0.225 0.130 0.135 0.321 0.209
Korea 0.146** 0.009** y0.007* Turkey 0.063** 0.024** 0.001
(5.617) (3.993) (y2.209) (4.243) (12.682) (0.638)
0.243 0.136 0.044 0.152 0.627 y0.007
Malaysia y0.018 y0.001 y0.015** Venezuela y0.012** y0.005 y0.002**
(y1.699) (y0.159) (y5.735) (y3.038) (y1.473) (y4.211)
0.019 y0.010 0.275 0.080 0.012 0.166
Zimbabwe 0.008** 0.028** 0.001**
(5.690) (8.875) (3.513)
0.248 0.450 0.119
For each country, we regressed a liquidity variable on a time variable, and report parameter estimate
for the time variable. Values in parenthesis are the t-statistic and the values below them are the adjusted
R-squared value of the model. Significance at the 1% and 5% level is indicated by ** and *, respectively.
13. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 13
stock returns, liquidity measures, and other explanatory variables without appropriate
adjustments may cause several potential sources of estimation biases. First, the
potential presence of a time trend to liquidity may create a spurious correlation if
both the dependent and independent variables share a trend. Second, serial correlation
in stock returns and liquidity has been widely documented. Third, serial patterns in
returns and liquidity have also been shown to exist for mature markets.
To account for these potential estimation issues, we filter all variables by
regressing on monthly dummies, a time trend, the square of a time trend, and a
financial-crisis dummy.11 We then use the adjusted series to estimate the relationship
between liquidity and stock returns using OLS residuals from the regression.12 Fig.
2 shows the adjusted series of liquidity variables. Compared to Fig. 1, which shows
the unadjusted series of values, Fig. 2 shows that adjusted liquidity variables have
been reasonably filtered. All empirical tests in Section 4 use the adjusted values of
variables (stock returns, liquidity measures, book-to-market, and changes in
exchange rates).
In order to establish the time-series relationship between liquidity and the stock
returns for emerging equity market, we use panel data for all 27 markets and across
the entire sample period. Using monthly return observations for all emerging
countries over the period January 1992 through December 1999, we regress market
returns and market-model-adjusted returns on the three liquidity measures. Table 6
presents the estimated regressions. The first three columns are the estimated
parameters for three regression models (M1, M2 and M3) each using an alternative
liquidity variable (turnover, trading value and turnoveryS.D., respectively). All three
models use monthly returns as the dependent variable.
The second set of three regression models (M4, M5 and M6) shown in Table 6
differ from the earlier three models only in the use of world market beta adjusted
returns as the dependent variable instead of monthly returns. Based on World Bank
classifications, we classified the 27 countries into four regional groups: Asia, Middle
East and Africa, Europe, and Latin America. World market betas were estimated for
each country using the relevant regional MSCI. The beta coefficient for each country
was estimated using the following market model regressions:
ritsaiqbirMtq´it, (1)
where rit is the return on country i’s index in month t and rMt is the monthly return
on regional MSCI, ai and bi are constant coefficients and ´it are the residuals. The
market model is estimated for each country over the whole sample period, January
1992 through December 1999. We then calculated the market-model-adjusted returns
as:
ARitsrityŽaiqbirMt. (2)
11 A financial crisis dummy takes the value of 1 for years 1997, 1998 and 1999, and 0 otherwise.
12 We checked for the stationarity of the adjusted series using the augmented Dickey–Fuller Test, and
found that the hypothesis of a unit root is rejected at the 1% significance level.
14. 14 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Fig. 2. Adjusted liquidity variables. This graph shows the de-trended liquidity variables averaged for all of the 27 emerging markets for the period January
1992 through December 1999. The three variables are filtered by regressing on a time trend, the square of a time trend, a financial crisis dummy and
monthly dummies.
15. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 15
Table 6
Panel data regressions
Dependent var.sStock returns Dependent var.sMarket model adj. returns
M1 M2 M3 M4 M5 M6
Intercept y0.587 y1.544 y1.567 0.735 y0.162 y0.512
(0.507) (y1.294) (y1.24) (0.644) (y0.137) (y0.417)
MSCI 0.477** 0.507** 0.531**
(11.215) (11.569) (11.719)
Turnover 0.703** 0.688**
(14.262) (14.147)
Log trading value 1.750** 1.679**
(6.542) (6.357)
TurnoveryS.D. 2.841** 2.739**
(6.350) (6.178)
Exchange rate 0.097** 0.109** 0.089** 0.105** 0.116** 0.096**
(4.000) (4.348) (3.563) (4.398) (4.679) (3.867)
Country dummies Not reported Not reported Not reported Not reported Not reported Not reported
Adj. R-squared 0.164 0.109 0.119 0.074 0.013 0.016
No. observations 2433 2433 2163 2433 2433 2163
Using monthly return observations for 27 emerging countries over the period January 1992 through December 1999, we regress returns and market-model-
adjusted returns on three liquidity measures, turnover, trading value and turnoveryS.D. The first three columns are the estimated parameters for three
regression models (M1, M2 and M3) each using monthly returns as the dependent variable. The second set of three regression models (M4, M5, M6) differ
from the earlier three models only in the use of world market beta adjusted returns as the dependent variable instead of monthly returns. Significance at
the 1% and 5% level is indicated by ** and *, respectively.
16. 16 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
for each month t for each country i, where the parameters ai and bi are estimated
by the market model in Eq. (1).
The regression results show that all three liquidity variables have a positive
correlation with stock returns even after controlling for world market returns and
exchange rates. The estimated regression coefficients for all of the three liquidity
measures are positive and statistically significant at the 1% significance level. In
the panel data regressions, we did not include the price to book ratio or market
capitalization, because these variables are implicitly incorporated in stock prices and
thus will cause spurious correlation with stock returns.13
The estimated coefficients using the risk-adjusted returns are similar to the
estimates using market returns. As shown in Table 6, the estimated parameters for
models M4, M5 and M6 indicate that market-model adjusted returns are positively
correlated with all measures of market liquidity. The estimated coefficients are all
significant at the 1% level. Among the three measures of market liquidity used, the
turnover ratio is shown to be the most effective explanatory variable for stock
market returns.
4.2. Across-country effects: period-by-period regressions
Having established a positive relation between stock returns and liquidity, we
now focus on the question of whether this positive correlation between stock returns
and market liquidity is driven by cross-sectional effects or by time-series effects.
To examine whether countries with higher levels of market liquidity would also
have higher stock valuation, we conducted Fama and MacBeth (1973) regressions
for each month. The Fama–MacBeth estimation procedure should reduce the
problem of serial correlation in the residuals of the OLS regression. For each of the
96 months in our sample period, we regressed stock returns on the liquidity
measures.
Table 7 provides the average regression coefficients and the adjusted R-squared
values for all of the six regression models discussed above. The t-values for the
coefficients are estimated by
¯b
ts
syyNy1
where and s is the mean and S.D. of the regression coefficients, respectively, ¯b
and N is 96, the total number of months in the sample.
The regression coefficients in Table 7 show that in general, stock market returns
are positively correlated with all three measures of market liquidity. Similar results
are also obtained for the relation between the liquidity variables and market-model
adjusted returns. It is important to note that while all three liquidity measures are
13 Those variables are included in cross-sectional Fama–MacBeth regressions, which are reported in
Table 7.
17. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 17
Table 7
Period-by-period regressions (across-country effect)
Dependent var.sMarket model adj.
Dependent var.sStock returns returns
M1 M2 M3 M4 M5 M6
Intercept y1.327 y0.676 y0.233 y0.362 y0.383 y0.274
(y0.661) (y1.330) (y0.439) (y0.856) (y0.887) (y0.593)
Turnover 0.180** 0.149**
(3.074) (2.632)
Log trading value 0.664* 0.361
(2.355) (1.354)
TurnoveryS.D. 0.253 0.711
(0.559) (1.609)
Market cap. y0.265 y0.971* y0.020 y0.413* y0.714 y0.359*
(y1.398) (y2.178) (y0.106) (y2.267) (y1.645) (y2.095)
PyB ratio 0.553* 0.622** 0.531 0.735** 0.815** 0.679**
(2.187) (2.474) (1.859) (3.153) (3.518) (2.590)
Exchange rate 0.243** 0.218* 0.171 0.009 y0.015 y0.053
(2.699) (2.344) (1.732) (0.095) (y0.161) (-0.513)
Adj. R-squared 0.183 0.176 0.149 0.153 0.142 0.130
For each of the 96 months in our sample period, we conduct Fama–MacBeth regressions of stock
returns on the liquidity measures. This table provides the mean regression coefficients and the adjusted
R squared values for all of the six regression models that are explained in Table 6. Significance at the
1% and 5% level is indicated by ** and *, respectively.
positively related with returns, only the turnover ratio variable is statistically
significant at the 1% level for both returns and adjusted returns.
4.3. Within-country effects: country-by-country regressions
We now examine the within-country effects of liquidity. For each of the 27
emerging countries, we regressed the monthly time-series stock returns on the three
alternative liquidity measures. Table 8 reports the mean regression coefficients and
the adjusted R-squared values for all of the six regression models.
The estimated regression coefficients in Table 8 show that all three measures of
market liquidity are positively correlated with stock returns as well as with market-model
adjusted returns. While all three liquidity measures show a positive relation
to stock returns, only the turnover ratio and the turnover–volatility ratio are
statistically significant at the 1% level for both market returns and adjusted returns.
The trading value measure does not exhibit a statistically significant relation with
returns, most likely because of the significant differences in the size of these
markets.
18. 18 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 8
Country-by-country regressions (within-country effect)
Dependent var.sMarket model adj.
Dependent var.sStock returns returns
M1 M2 M3 M4 M5 M6
Intercept 2.008 y4.097* 0.896 1.381 y4.347** 0.103
(0.740) (y2.233) (0.512) (0.575) (y2.938) 0.059
MSCI 0.626** 0.635 0.640**
(6.575) (6.474) (7.345)
Turnover 1.533 1.508**
(5.194) (5.504)
Log trading value 3.616** 3.470**
(5.453) (6.108)
TurnoveryS.D. 5.582** 4.168**
(4.222) (3.325)
Exchange rate y2.626 y5.014** y1.485 y2.594 y5.068** y1.709
(y1.667) (y3.185) (y1.141) (y1.688) (y3.750) (y1.303)
Adj. R-squared 0.180 0.181 0.169 0.113 0.119 0.121
For each of the 27 emerging countries, we regressed the monthly time-series stock returns on the three
alternative liquidity measures. This table reports the mean regression coefficients and the adjusted R
squared values for all of the six regression models. Significance at the 1% and 5% level is indicated by
** and *, respectively.
4.4. Robustness tests: causality analysis and regional classification
While we document a strong contemporaneous relation between measures of
market liquidity and equity returns, we do not in any way suggest that there is a
causal relation between the two variables. To examine if there might be a causal
relation between these two variables and what the direction of the causality might
be, we conduct a multivariate Granger (1969) causality test between liquidity and
stock returns using a VAR model comprised of liquidity, stock return, price-to-book,
market capitalization, regional MSCI, and exchange rate change. We average these
variables across countries in each of the four regions, and then perform separate
multivariate VARs for each region.
Let xts(return, liquidity, MSCI, Mkt Cap, PB, exchange rate Change)9. Then a
general VAR (p) representation can be expressed as
xtscqF1xty1qF1xty2qΔqF1xtypqet (3)
19. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 19
where
wf Δf z 11j 1nj
Fjsxn n |, js1,2,«,p yfn1jΔfnnj~
csŽc ,c ,«,c .9 1 2 n
esŽe ,e ,«,e .9 t 1t 2t nt
SV, tss TU
EŽet.s0; EŽete9s.sT V0, t/s
Each of the three liquidity variables along with both the market returns and
abnormal returns are alternatively used in the analysis. When the abnormal returns
are used in the model, we exclude the regional MSCI in the xt vector because the
effect of world market is already reflected in estimating abnormal returns. We
choose two lags (ps2, 2 months) for our analysis. The results of the VAR causality
analysis are reported in Table 9.
The results in Table 9 shows that trading values (natural log of trading values in
$) Granger-cause stock returns in Asia, and that stock returns Granger-cause trading
values in all regions. The abnormal returns, however, are Granger-caused by trading
values only in Middle East and Africa. Furthermore, turnover ratio or turnover-to-volatility
measure does not have any significant causality relationship with either
returns or abnormal stock returns. The result implies that the causality relationship
between liquidity and stock return is not significant, and that the positive relationship
between liquidity and stock return comes from a contemporaneous relation between
the two variables.
The second robustness test was done for regional classifications. There is reason
to suspect that there might be some regional or geographical similarities or
associations within markets. For instance, one might expect that emerging markets
that are within a particular region such as Latin America or Europe to have similar
characteristics. In this subsection we examine this proposition. In the interest of
brevity, and also because it proved to be the most significant variable in earlier
regressions we shall report results only for the turnover ratio as the liquidity measure
in this segment of the analysis.
For each emerging market, Table 10 reports the regression coefficients for Model
1 and Model 4, which use the turnover ratio as the liquidity measure. The first entry
of each cell is the regression coefficient, followed by the t-value, and then the
adjusted R-squared value. The estimates in Table 10 show that the positive correlation
20. 20 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 9
Multivariate Granger causality test
Region Liquidity Return Liquidity causes
return
Return causes
liquidity
Test stat. P-value Test stat. P-value
Asia Turnover Return 1.671 0.194 1.195 0.308
Abnormal 1.565 0.215 0.298 0.743
Trade Return 4.056 0.021* 3.479 0.035*
Abnormal 0.960 0.387 2.296 0.107
TurnoveryS.D. Return 0.766 0.468 0.028 0.973
Abnormal 0.681 0.509 0.322 0.726
M. East and Africa Turnover Return 0.989 0.376 2.836 0.064
Abnormal 1.106 0.335 0.997 0.373
Trade Return 0.804 0.451 5.130 0.008**
Abnormal 1.060 0.351 3.376 0.039*
TurnoveryS.D. Return 0.255 0.776 0.863 0.426
Abnormal 0.483 0.619 0.426 0.655
Latin America Turnover Return 0.441 0.645 1.131 0.327
Abnormal 0.367 0.694 0.764 0.469
Trade Return 0.578 0.563 6.381 0.003**
Abnormal 1.043 0.357 4.156 0.019
TurnoveryS.D. Return 0.626 0.538 0.610 0.546
Abnormal 0.046 0.956 0.871 0.422
Europe Turnover Return 1.927 0.152 0.821 0.443
Abnormal 1.177 0.313 0.340 0.713
Trade Return 0.972 0.382 3.619 0.031*
Abnormal 1.195 0.307 1.799 0.171
TurnoveryS.D. Return 0.030 0.971 1.017 0.366
Abnormal 0.020 0.980 1.442 0.243
This table reports results of multivariate Granger causality tests between liquidity and stock returns
using a VAR model comprised of liquidity, stock returns, price-to-book, market capitalization, regional
MSCI, and exchange rate change. We implement the VAR by averaging these variables across countries
in each of the four regions, and then perform separate VARs for each region. Significance at the 1%
and 5% levels are indicated by ** and *, respectively.
between market liquidity and stock returns are not driven by any one specific
country or geographical region. There are no apparent patterns within or across
regions with respect to either the significance of the coefficients or the strength of
the relation as measured by R-squared.
5. Summary and conclusions
Using data for 27 emerging equity markets for the period January 1992 through
December 1999, we find that stock returns in emerging countries are positively
correlated with market liquidity as measured by turnover ratio, trading value as well
as turnover–volatility multiple. The results hold in both cross-sectional and time-series
analyses, and are quite robust even after we control for world market beta,
21. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 21
Table 10
Country-by-country regressions with geographical classification
Region Country M1 M4
Asia China 0.272* 0.307**
2.345 2.769
0.140 0.148
India 2.635** 2.707**
3.898 4.037
0.122 0.152
Indonesia 1.562** 1.601**
2.625 2.740
0.170 0.090
Korea 0.474** 0.441**
3.969 3.837
0.396 0.167
Malaysia 1.235** 1.122k**
3.568 3.241
0.286 0.157
Pakistan 0.494** 0.544**
3.116 3.407
0.066 0.085
Philippines 2.973** 2.739**
2.751 2.549
0.246 0.072
Sri Lanka 5.487** 5.511**
3.218 3.252
0.166 0.154
Taiwan 0.551** 0.480**
5.231 4.560
0.344 0.215
Thailand 0.969** 1.082**
3.983 4.526
0.252 0.191
M. East and Africa Jordan 0.628* 1.060**
2.054 3.670
0.053 0.163
Nigeria 1.660 1.888*
1.951 2.271
0.084 0.086
S. Africa 2.366 0.465
1.432 0.275
0.371 0.046
Turkey 1.307** 1.301**
3.209 3.102
0.205 0.174
Zimbabwe 2.906* 3.032*
2.052 2.179
0.111 0.066
Latin America Argentina y0.564 y0.323
y1.462 y0.841
0.352 0.099
Brazil 5.516** 5.015**
22. 22 S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24
Table 10 (Continued)
Region Country M1 M4
3.595 3.196
0.325 0.298
Chile y0.224 0.467
y0.249 0.517
0.149 0.033
Colombia 0.928 1.362
0.790 1.129
y0.012 y0.013
Mexico 0.835 0.776
1.401 1.345
0.323 0.038
Peru y0.033 y0.044
y0.041 y0.055
0.096 0.065
Venezuela 2.443 2.331
1.915 1.849
0.130 0.083
Europe Czech 1.403* 1.313*
2.040 1.979
0.063 0.014
Greece 1.686** 1.494**
3.547 3.197
0.266 0.179
Hungary 1.221 1.098
1.878 1.696
0.235 0.047
Poland 2.691** 2.581**
3.773 3.665
0.285 0.227
Portugal y0.022 0.360
y0.054 0.968
0.425 0.172
For each emerging market, this table reports the regression coefficients for Model 1 and Model 4 in
Table 8, which uses the turnover ratio as the liquidity measure. The first entry of each cell is the
regression coefficient followed by the t-value, and then the adjusted R-squared. Significance at the 1%
and 5% levels are indicated by ** and *, respectively.
market capitalization and price-to-book ratio. The positive correlation between stock
returns and market liquidity in a time-series analysis is consistent with the findings
in developed markets. However, the positive correlation in the cross-sectional
analysis appears at odds with market microstructure theory that has been empirically
supported by studies on developed markets.
The positive correlation found between stock returns and liquidity in the cross-sectional
analysis is also supportive of the view that emerging equity markets have
a lower degree of integration with the global economy. The degree of integration of
a given equity market with the global economy has important implications for
international portfolio diversification. Bekaert and Harvey (1997) provide evidence
23. S.-G. Jun et al. / Emerging Markets Review 4 (2003) 1–24 23
on the degree of integration of emerging equity markets with the world economy,
and note that this degree of integration varies significantly over time.
An important component of our study was to examine the time-series behavior
of liquidity for emerging equity markets. We document a significant rise in the
overall level of liquidity of emerging equity markets over the period 1992–1999. If
enhanced market liquidity is tantamount to increased economic development and
hence a stronger relationship to the global economy, our findings are consistent with
previous literature that suggests an increasing correlation structure between emerging
equity markets and the global economy.
Whereas our study focuses on long-run effects of emerging stock markets, which
cannot be solely attributed to information effects from trading activity, our overall
findings are consistent with the high-liquidity return premium hypothesis in stock
prices, originally suggested by Ying (1966) and later extended by Gervais et al.
(2001). They show that increases (decreases) in daily trading volume tend to be
followed by a rise (fall) in the stock price. Their results imply that trading activity
contains new information about the future evolution of stock prices.
While we document a strong contemporaneous relation between measures of
market liquidity and equity returns, this does not imply that there is a causal relation
between the two variables. To examine if there might be a causal relation between
liquidity and stock return, and what the direction of the causality might be, we
conducted a Granger causality test using a multivariate VAR model for four regions
pertaining to emerging equity markets. Neither of the variables examined was found
to significantly Granger cause the other variables in any consistent way across all
markets.
Our findings have important implications to both policy makers and portfolio
managers. The direct and highly significant relationship between equity returns and
liquidity in emerging markets should prompt policy makers to implement policies
that will enhance liquidity and promote growth. Portfolio managers on the other
hand, should incorporate the liquidity characteristics of individual markets as they
consider their global portfolio allocation strategies.
Acknowledgments
We thank Lester Hadsell, Javier Estrada (the editor), David Smith and an
anonymous referee for helpful comments and suggestions on earlier drafts of this
manuscript.
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