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This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

The current study immerses itself in the realm of diversification prospects within a select group of preeminent global stock exchanges. Specifically, the study casts its discerning gaze upon the financial hubs of the United States, Hong Kong, Germany, France, Amsterdam and India. In this expansive vista of international financial markets, the present analytical study aims to unravel the multifaceted opportunities that lie therein for astute portfolio management and strategic investment decisions.

The study encompasses daily time series data spanning from 2019 to 2022. To assess the interconnectedness among these stock indices, advanced statistical techniques, including Johansen cointegration methods and vector autoregressive (VAR) models, have been applied.

The research outcomes reveal both unidirectional and bidirectional relationships between the Indian, Hong Kong and US stock exchanges, encompassing both short-term and long-term time frames. Interestingly, the empirical findings indicate the presence of diversification opportunities between the Indian stock exchange and the stock exchanges of Germany, France and Amsterdam.

These insights hold significant value for both Indian and international investors, including foreign institutional investors (FIIs), domestic institutional investors (DIIs) and retail investors, as they can utilize this knowledge to construct more effective and diversified investment portfolios by understanding the intricate interconnections between these prominent global stock exchanges.

This research undertaking aspires to bring coherence to a landscape rife with divergent interpretations and methodological divergences. We are poised to offer a comprehensive analysis, a beacon of clarity amidst the murkiness, to shed light on the intricate web of interconnections that underpin the world's stock exchanges. In so doing, we seek to contribute a seminal piece of scholarship that transcends the existing ambiguities and thus empowers the field with a deeper understanding of the multifaceted dynamics governing international stock markets.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

The purpose of this study is to investigate the presence of psychological barriers both in the main stock market indices of the Baltic states and the most actively traded individual stocks. A psychological barrier refers to a specific price point, often at round numbers (i.e. powers of 10), that investors believe is challenging to breach, influencing their behavior and trading decisions.

We conduct uniformity tests and barrier tests, such as barrier proximity tests and barrier hump tests, to evaluate the presence of psychological barriers. Additionally, we explore variations in means and variances near these potential barriers using regression and GARCH analysis.

The findings reveal that psychological barriers do exist in the Baltic stock markets, particularly within market indices. The Estonian market index stands out with the most pronounced indications of psychological barriers. Individual stocks also display significant changes in means and variances related to potential barriers, albeit with less uniformity.

Collectively, our findings challenge the traditional assumption of random returns within the Baltic stock markets. For practitioners, the finding that psychological barriers exist opens up opportunities for investment strategies that can capitalize on them.

This study is the first to comprehensively investigate psychological barriers in the Baltic stock markets. Our results provide a valuable contribution to understanding the impact of that phenomenon on pricing dynamics, which is particularly pertinent in less-researched frontier markets like the Baltic states.

The objective of the study is to shed light on the notion of quality investing in the Indian stock market. The study also attempts to combine the value and quality metrics to test their ability to generate a higher risk-adjusted return.

The paper employs asset pricing models to examine the excess risk-adjusted returns and panel regression model (random estimates) to determine the price of quality in the cross-section of Bombay Stock Exchange (BSE) listed stocks from 2003 to 2020.

The results indicate that the quality-only strategy failed to produce substantial risk-adjusted returns in the Indian stock market. The returns to long/short hedging strategy quality-minus-junk (QMJ) are significantly positive with the majority of the returns attributable to the short leg of the stock portfolio. The findings further discovered that the explanatory effect of quality on prices is limited. In particular, a strategy that combines value and quality investing generated positive and significant alphas as well as a higher Sharpe ratio.

The study provides investors and portfolio managers with valuable insights for navigating undervalued high-quality equities in the Indian stock market.

This is the first research of its kind to examine the performance of quality (Q score indicator) combined with value investing in the Indian stock market. As majority of research have concentrated on developed economies, this study offers out-of-sample evidence to validate the strategy’s success in an emerging market.

This paper examines the reaction of the Egyptian stock market to two substantial devaluations of the Egyptian pound (EGP) in 2022 and tests the informational efficiency of the Egyptian market.

The paper uses the event study framework to analyze the significance and direction of abnormal returns of the leading index of the Egyptian stock market (EGX30) on and around the devaluation days. It employs both the constant mean model and the market model to estimate the normal returns of the EGX30. Additionally, the paper uses data on two equity indices, one global and one for emerging markets, as benchmarks for normal returns.

The paper finds that the Egyptian stock market experienced significant positive abnormal returns on the devaluation days of the EGP in March and October of 2022, indicating a positive market reaction to the devaluation. Furthermore, evidence suggests that the Egyptian market may not be informationally efficient as significant positive abnormal returns were observed two weeks before and two weeks after the devaluation day, suggesting news leaks and delayed reactions, respectively.

This study is the first to examine the impact of the recent two devaluations of the EGP in 2022 on the Egyptian stock market. It complements existing literature by analyzing the immediate market reaction to two consecutive devaluations in an African country. Furthermore, the paper evaluates the efficiency of the Egyptian market in processing information related to exchange rates.

This paper investigates potential safe haven assets for Middle East and North Africa (MENA) stock markets during the uncertainty period of the COVID-19 pandemic.

This study applies the dynamic conditional correlation–generalized autoregressive conditionally heteroskedastic (DCC-GARCH) model and the Diebold–Yilmaz spillover index for ten MENA stock markets, three precious metals and Bitcoin for the period 2013–2021.

Empirical results show, on the one hand, that the COVID-19 crisis risk has been transmitted to MENA stock markets through volatility spillover across markets. This has increased the conditional volatility for all markets. On the other hand, findings point out that the dynamic correlation between the precious metals/Bitcoin and stock markets is not stable and switches between low positive and negative values during the period under studies. Extending analysis to portfolio management, results reveal that investors should include precious metals/Bitcoin in their portfolio of stocks in order to reduce the risk of the portfolio. Finally, for the period of COVID-19, the analysis concludes that gold preserves its traditional role as a safe haven for MENA stock markets during the pandemic, while Bitcoin fails to provide this property.

These results have several implications for international investors, risk managers and financial analysts in terms of portfolio diversifications and hedging strategies. Indeed, the exploration of the volatility connectedness between financial, commodity and cryptocurrency markets becomes an essential task for all market participants during the COVID-19 outbreak. Such analysis can help investors and portfolio managers to evaluate the risk of investments in the MENA stock markets during the crisis period and to achieve the optimal diversification strategy and hedging instruments.

The paper interests MENA stock markets that experienced the last decade a substantial development in terms of market capitalization and number of listed firms. To the author’s knowledge, this is the first study that investigates the dynamic correlation between MENA stock markets and four potential safe haven assets, including three precious metals and Bitcoin. In addition, the paper employs two types of models, namely the DCC-GARCH model and the Diebold-Yilmaz spillover index.

This research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy.

Using daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis.

The results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability.

The study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies’ implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods.