Emerging Stock Market Returns Research Articles

Calendar anomalies in stock market returns: Evidence from Middle East countries

Using GJR-GARCH (1,1) techniques, we investigate seasonality in stock market returns of seven Middle East countries. We investigate the calendar time anomalies of the day-of-the-week, the-month-of-the-year, the turn-of-the-month effect and the half-month effect. All stock markets of our sample exhibit significant evidence on all or most of the considered anomalies. The paper contributes to our understanding of calendar anomalies by providing an out-of-sample test through considering stock markets and sample periods which have not been examined thoroughly and sufficiently before. In addition, the paper provides further insights and evidence on the nature, existence and persistence of the seasonal patterns in emerging stock market returns. Overall, our results contradict the efficient market hypothesis (EMH). The results suggest that calendar anomalies remain prominent challenge to the EMH and its implications.

Machine learning and the cross-section of emerging market stock returns

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although we find that both linear and machine learning models show higher predictability for stocks associated with higher limits to arbitrage, we also show that this effect is less pronounced for non-linear models. Furthermore, significant net returns can be achieved when accounting for transaction costs, short-selling constraints, and limiting our investment universe to big stocks only.

Do emerging stock markets offer an illiquidity premium for local or global investors?

This paper examines the role of illiquidity as a determinant of stock returns in emerging markets by (i) presenting a comparative analysis from two popularly utilized liquidity proxies, i.e. the price impact (PI) measure of Amihud (2002) and the zero-return (ZR) measure of Lesmond et al. (1999); and (ii) testing for illiquidity effects, both in a panel of 20 emerging stock markets and at the local level for each market. We show that the choice of the liquidity proxy plays an important role on the assessment of liquidity effects in emerging stock market returns. While we confirm the presence of a significant and positive illiquidity premium in both the panel tests and at the individual market level, we observe stronger results when illiquidity is captured by the zero-return measure. We also observe that the illiquidity premium has diminished over time, in line with the evidence that suggests capital market anomalies have attenuated over time. The findings suggest that the excess returns associated with illiquidity are more likely driven by mispricing than compensation for risk and emerging stock markets still offer ample opportunities for both global and local investors towards active investment strategies that can exploit liquidity patterns in these markets.

Machine Learning and The Cross-Section of Emerging Market Stock Returns

Machine Learning and The Cross-Section of Emerging Market Stock Returns

When bad news is good news: Geopolitical risk and the cross-section of emerging market stock returns

Using a news-based gauge of geopolitical risk, we study its role in asset pricing in global emerging markets. We find that changes in risk positively predict future stock returns. The countries with the highest increase in geopolitical uncertainty outperform their counterparts with the lowest change by up to 1% per month. The anomaly is not explained by other established asset pricing effects and remains robust to many considerations. We link the observed phenomenon with investor overreaction to geopolitical news driven by the availability bias.

IMPACTS OF GLOBAL AND LOCAL COVID-19 CASES ON EMERGING STOCK MARKETS

In this study, we examine the both local and global Covid-19 deaths and confirmed cases impact on stock markets return. We use the daily data from 26 countries, which are classified as emerging financial markets. According to the findings, a decrease in emerging stock market returns is not only due to local confirmed cases, but also from global confirmed cases. Our analysis also suggests that while an increase in total confirmed cases within the local variables leads to more decrease, total deaths within the global variables cause more decline in returns. When we consider the change in the stock market returns brought about by both local and global level confirmed cases rates and death rates increase, all global level variables (new confirmed cases, total confirmed deaths, new confirmed cases) except total confirmed cases lead to more decline. In other words, even if the countries control the covid-19 locally, the negative global impact on the stock market will continue.

Impact of ESG Factors on Emerging Market Stock Returns

Empirical evidence indicates that environmental, social, and governance (ESG) practices are associated with a company’s financial performance, but little has been mentioned about investors’ attention toward stock performance. In this article, the authors conduct a test of the relationship between ESG performance disclosures and a company’s financial or capital market performance. They choose a stratified sample of large-cap companies across eight sectors drawn from the S&P 100 from 2016–2021. The changes in ESG ratings—namely, for governance and the environment—exhibit a small but significant impact on the stock’s performance. Their results show that a few ESG disclosures are positively related and dominate over the usual company-level determinants of return on assets. They find corporate governance practices to be more significant than climate change disclosures. These outcomes are robustly demonstrated on use cases of top stocks with investor safety recommendations. The results could have useful implications for investors, fund managers, and regulators.

What Drives Emerging Stock Market Returns? A Factor-Augmented VAR Approach

ABSTRACT This paper explores the dynamic relationship between global economic factors and emerging stock returns within a factor-augmented VAR model. I find that favorable global growth and stock market shocks have significant positive effects on emerging equity returns, whereas global uncertainty and US dollar exchange rate shocks cause a substantial fall in the returns. Global oil shocks lead to a transient increase in emerging stock returns, followed by a gradual decline. Variance decomposition analysis implies that the global uncertainty shock is the most important in the short run, explaining more than 30% of the fluctuation in emerging stock returns, while the US dollar exchange rate shock becomes the most critical in the long run, explaining more than 40%. These findings have crucial implications for international investors, as well as for policymakers in emerging market economies.

When Bad News is Good News: Geopolitical Risk and the Cross-Section of Emerging Market Stock Returns

Using a news-based gauge of geopolitical risk, we study its role for asset pricing in global emerging markets. We find that changes in risk positively predict future stock returns. The countries with the highest increase in geopolitical uncertainty outperform their counterparts with the lowest change by up to 1% per month. The anomaly is not explained by other established asset pricing effects and remains robust to many considerations. We link the observed phenomenon with investor overreaction to geopolitical news driven by the availability bias.

The cross-section of emerging market stock returns

Using monthly stock returns from 28 emerging market countries and a total sample period of 21 years, we investigate the predictive power of a broad set of factors. We document that the factor definitions of the Fama and French (2015) five-factor model are less robust compared to alternative factor definitions. In contrast, the anomalous returns associated with cash flow-to-price, gross profitability, composite equity issuance, and momentum are pervasive as they show up in equal- and value-weighted portfolio sorts as well as in cross-sectional regressions. In contrast to financial theory and in line with previous findings, we do not find a positive cross-sectional relationship between risk and return. Finally, return forecasts derived from the alternative factor definitions are superior in their out-of-sample predictive ability to the ones derived from the five-factor model.

The Cross-Section of Emerging Market Stock Returns

Using monthly stock returns from 28 emerging market countries and a total sample period of 21 years, we investigate the predictive power of a broad set of factors. We document that the factor definitions of the Fama and French (2015) five-factor model are less robust compared to alternative factor definitions. In contrast, the anomalous returns associated with cash flow-to-price, gross profitability, composite equity issuance, and momentum are pervasive as they show up in equal- and value-weighted portfolio sorts as well as in cross-sectional regressions. In contrast to financial theory and in line with previous findings, we do not find a positive cross-sectional relationship between risk and return. Finally, return forecasts derived from the alternative factor definitions are superior in their out-of-sample predictive ability to the ones derived from the five-factor model.

The non-persistent relationship between foreign equity flows and emerging stock market returns across quantiles

We compare the performance of two state-of-the-art predictive regression methods of IVX-Wald (Kostakis et al., 2015), IVX-Quantile regression (Lee, 2016) with the traditional OLS in examining the relationship between foreign equity flows and emerging stock market returns. By doing so, we take into account not only the potential persistence in foreign equity flows, but also the exceptional behavior of the extreme foreign flow episodes. We find a robust positive relationship between equity flows and contemporaneous stock returns among emerging stock markets (especially in Asia), but little evidence for intertemporal return predictability.

Rational–Irrational Investor Sentiments and Emerging Stock Market Returns: A Comparison from Turkey

This study investigates the dynamic relationship between rational and irrational consumer-business sentiments and stock returns in an emerging stock market, Turkey. Consumer and business sentiments are divided into two components: rational and irrational sentiments. Then, the dynamic interactions and the impact of the sentiments on stock returns are examined. The fundamental economic variables used in the study consist of business conditions, economic risk premium, country risk, exchange rate risk, country growth rate, inflation rate, and terms of trade. The results show that Istanbul Stock Exchange (ISE)-100 index returns are positively and significantly affected by the rational sentiments of both consumers and businesses. JEL Classification: G02, G12, G150

Impact of oil volatility shocks on global emerging market stock returns

PurposeThe purpose of this paper is to investigate whether the crude oil volatility index (OVX) plays any key role in explaining the trend in emerging market stock returns from a global standpoint.Design/methodology/approachAt the empirical stage, different forms of the GARCH-jump model have been estimated.FindingsThe findings confirm the effects of OVX on equity returns. In addition, the results document that there exist time-varying jumps in the stock market returns. Besides, the impacts of OVX shocks appear to be symmetric. The analysis further shows that the magnitude of OVX impact is marginally bigger than that of the conventional oil price shocks.Originality/valueSince various financial assets are traded on the basis of oil and equity markets, investors, for instance, could use the findings of this study for taking proper investment decisions and gaining better portfolio diversification benefits. Additionally, policymakers could utilize the results to develop effective measures and strategies in order to minimize the oil price risk.

Influence of Oil Price Volatility of Developed Countries on Emerging Countries Stock Market Returns by Using Threshold Based Approach

This study reveals the nonlinear relationship between oil price volatility of the developed countries and emerging stock market returns. We analyzed the effects of oil price volatility of the developed markets i.e. India, United States and United Kingdom on the emerging stock market returns. We used VAR, Granger Causality, impulse responses and logistic transition based autoregressive model (LSTR) in two groups. In group one, we considered US oil price volatility with six emerging countries and regions stock market returns i.e. France, Spain, Malaysia, Japan, Singapore and Taiwan. In group two, we considered oil price with respect to India with the same six emerging countries stock market returns. The data covers the daily closing prices for seven years from 2011 to 2017 for emerging countries and for oil prices the data we use West Texas Intermediate (WTI) spot price of crude oil for US and India. This study helps the investors to understand the impact of oil prices of US and Indian market with respect to emerging markets and whether to identify the dependency of emerging stock markets returns on the oil prices of US and India. All the analysis was performed by using R and EVIEWS software.

Evidence to Support Multifactor Asset Pricing Models: Case of Indian Stock Market

Emerging stock market returns have been extensively studied by academic community over the past two decades. However, there is still no consensus among the researchers and practitioners as to which asset pricing models should be used to explain returns in these markets. The basic objective of the study is to evaluate the power and performance of multi-factor asset pricing models (three and four factor model) over the traditional one factor CAPM, using the data from one of the fastest growing emerging market: India. The study using a large sample data of 470 listed stocks over a period of 16 years stretching from January 1997 to March 2013, evaluate the relevance of Fama and French three factor model as well as liquidity augmented four factor model in explaining the stock return variations in the Indian stock market. The study employs time series regression approach to examine the impact of market risk, size risk, value risk and liquidity risk on stock returns. The overall results of the study provide support to the multi-dimensional nature of risk and suggest the use of multi-factor asset pricing models for consideration in investment decisions. Both Fama and French three factor model and liquidity augmented four factor model were found to be superior than traditional one factor CAPM. Though, liquidity augmented four factor model was found to be slightly better in explaining Indian stock returns as compared to Fama and French three factor model.

On time-varying predictability of emerging stock market returns

The two recent studies of Cajueiro and Tabak (2004b) and Hull and McGroarty (2014) investigate the predictability of emerging stock market returns based on the Hurst coefficient—a simple but powerful measure of long-range dependence. Unfortunately, the insights gained in these studies are limited because they (i) present conflicting evidence on the time-varying nature of the estimated Hurst coefficients and (ii) incorrectly equate random walk behaviour with market efficiency. In this note, we revisit the issue of time-varying predictability for a rich sample of 21 emerging markets in the 27-year period from 1988 to 2015. Extending the two aforementioned studies by various alternative fractal estimators of the Hurst coefficient, trend regressions and several robustness checks, our analysis reveals significant downward trends in the local Hurst coefficients of almost all markets. Specifically, we document vanishing predictability over time, which indicates that profitable emerging market investment strategies based on past returns may not continue their good performance in the future. Furthermore, we explicitly point out why a random walk is neither a necessary nor a sufficient condition for rationally determined security prices, and thus signs of predictability (randomness) should not be interpreted as evidence for market inefficiency (efficiency).

Evidence to Support Multifactor Asset Pricing Models: Case of Indian Stock Market

Emerging stock market returns have been extensively studied by the academic community over the past two decades. However, there is still no consensus among the researchers and practitioners as to which asset pricing models should be used to explain returns in these markets. The fundamental objective of the study is to evaluate the power and performance of multifactor asset pricing models (three-and four-factor models) over the traditional one-factor capital asset pricing model (CAPM), using the data from one of the fastest growing emerging markets: India. The study, using a large sample data of 470 listed stocks over a period of 16 years stretching from January 1997 to March 2013, evaluates the relevance of Fama and French three-factor model as well as liquidity augmented four-factor model in explaining the stock return variations in the Indian stock market. The study employs time series regression approach to examine the impact of market risk, size risk, value risk and liquidity risk on stock returns. The overall results of the study provide support to the multidimensional nature of risk and suggest the use of multifactor asset pricing models for consideration in investment decisions. Both Fama and French three-factor model and liquidity augmented four-factor model were found to be superior to traditional one-factor CAPM, though liquidity increased four-factor model was found to be slightly better in explaining Indian stock returns as compared with Fama and French three-factor model.

Size, Value, and Momentum in Emerging Market Stock Returns: Integrated or Segmented Pricing?

AbstractIn this paper, we examine size, value, and momentum patterns in the stock returns of four emerging market regions—Latin America, EMEA, Asia, and BRIC. We document a strong and highly significant value effect, and a strong but less significant momentum effect. Substantial value and momentum premiums are also present for big stocks and the overall premiums are not mainly driven by small stocks. Furthermore, the value patterns in emerging markets are more pronounced than in developed markets. In order to examine integrated global pricing across these regions, we test whether empirical asset pricing models with global factors explain the variation in average stock returns and, in particular, we assess their ability to capture the value and momentum patterns. Since the global models perform poorly for emerging markets, we examine the performance of local factor models, and find evidence in favor of the local four‐factor model with local market, size, value, and momentum factors. On the basis of our results, pricing in emerging markets does not seem to be globally integrated.

Size, Value, and Momentum in Emerging Market Stock Returns: Integrated or Segmented Pricing?

In this paper, we examine size, value, and momentum patterns in the stock returns of four emerging market regions - Latin America, EMEA, Asia, and BRIC. We document a strong and highly significant value effect, and a strong but less significant momentum effect. Substantial value and momentum premiums are also present for big stocks and the overall premiums are not mainly driven by small stocks. Furthermore, the value patterns in emerging markets are more pronounced than in developed markets. In order to examine integrated global pricing across these regions, we test whether empirical asset pricing models with global factors explain the variation in average stock returns and, in particular, we assess their ability to capture the value and momentum patterns. Since the global models perform poorly for emerging markets, we examine the performance of local factor models, and find evidence in favor of the local four-factor model with local market, size, value, and momentum factors. On the basis of our results, pricing in emerging markets does not seem to be globally integrated.