Search results

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

The purpose of this paper is to investigate how bank-specific factors affect the riskiness of conventional and Islamic banks in response to shocks in major financial indices as market conditions change.

The authors use a multivariate quantile model using daily equity returns data to analyze financial risk spillovers in the values at risk that may occur between major financial indices and the equity prices of conventional and Islamic banks worldwide. Then, using both quantile and quantile-on-quantile models, the authors examine the effects of bank-specific variables such as leverage ratio, bank size, return on equity and capital adequacy ratio on the initial impact of shocks in major global financial indices on bank equity price returns at different quantiles of shocks and bank-specific variables.

The findings reveal that major financial indices can predict bank stock returns. Moreover, the authors find that the effect of bank-specific factors on the riskiness of banks is heterogeneous in that it depends on the bank type (Islamic vs conventional), the level of banking variable (high vs low) and, more importantly, market conditions.

To the best of the authors’ knowledge, this is the first study that compares the dual banking system with stock market performance while considering bank-specific variables as market conditions change. The results of this study reveal that the effect of bank-specific variables on bank performance varies according to different quantiles of shocks and bank-specific variables. Islamic banks may echo or differ from conventional banks depending on the specific factor under investigation.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.

This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.

This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.

Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.

Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.