Asia-Pacific Financial Markets最新文献

In this paper, we examine the effects of return and volatility shocks captured from Bitcoin to other seven types of major cryptocurrencies by employing the daily data spanning from June 2011 to June 2020. We examine return and volatility transmission from Bitcoin to other cryptocurrencies using ARMA-GARCH model and extension of the asymmetric model of ARMA-TGARCH and ARMA-EGARCH. Moreover, we apply Dynamic Conditional Correlation and Asymmetric Dynamic Conditional Correlation (DCC and ADCC) models to measure the time-varying nature of conditional correlation. The results of the study show strong evidence of shocks transmission from Bitcoin to other cryptocurrencies in terms of both returns and volatility spillover, except for some less inefficient cryptocurrencies. In addition, the majority of the cryptocurrencies also reflect strong evidence about time-varying dynamic conditional correlation with asymmetric effects that adds ups the significant novelty in the existing literature from the methodological perspective as well.

This study innovates in credit default prediction in Pakistan by developing, calibrating, and recalibrating machine learning-based credit scorecards for non-financial listed firms, leveraging extensive financial ratio analysis. This study innovates in credit default prediction in Pakistan by developing, calibrating, and recalibrating machine learning-based credit scorecards for non-financial listed firms, leveraging extensive financial ratio analysis. Identifies 12 key financial ratios out of 71 remained vital for default prediction, with Random Forest and Artificial Neural Networks leading in scorecard performance. This marks Pakistan’s first detailed scorecard approach as a potential alternative to traditional banking systems. Offers advanced risk assessment tools (credit scorecards) for improved credit risk management, aiding policymakers and finance professionals in decision-making. This research distinguishes itself through a detailed longitudinal study of non-financial Pakistani firms and a comprehensive evaluation of machine learning algorithms for default prediction. By exploiting various financial ratios to develop scorecards (an alternative of Internal Ratings-based – IRB System), it offers new insights into risk evaluation and significantly advances financial risk management. Acknowledging data limitations and variable exclusions, it sets the stage for further exploration of credit risk environment in context of Pakistan.

Identifying investment opportunities with high returns is crucial for individuals seeking to generate wealth and accumulate assets. The cryptocurrency market exhibits high volatility and significant price variations compared to the stock market, suggesting that price movements may be influenced by additional factors. Identifying and categorizing these factors helps in making precise forecasts about the increase in investors’ asset. Thus, this study aims to investigate the primary elements influencing cryptocurrency’s’ market performance. The qualitative approach included a literature review, sentimental analysis and in-depth interviews with investors. Non-probability sampling techniques were adopted in identifying the prospective respondents for in-depth interviews. Additionally, the bibliometric analysis helped in the collection of appropriate literature for a systematic literature review. A taxonomy was built, combining all codes received with the help of experts. Distinct determinants were identified and categorized as internal and external factors, which were then further subdivided. Internal variables include fear of missing out, money accumulation, flexibility, and understanding of patterns. External factors such as technological progress, community involvement, airdrops/roadmap, news/speculations, and government laws also have a role. Understanding the determinants helps investors and traders gain appropriate knowledge on investments and profit-making, thereby yielding wealth that could provide financial freedom and a better lifestyle. This study is novel because exploring, understanding, and predicting the cryptocurrency market is one of the latest and most widely spoken topics among researchers in the finance domain. Earlier studies have not emphasized empirically the concepts, strategies and factors impacting price fluctuations and investment behavior in the crypto market.

This study examines the time-varying nature of investor herd behavior over different market episodes in Sri Lankan stock market, that has been subjected to convulsed periods such as civil war, political instability, terrorist attacks and COVID-19 pandemic. The study employs Cross-Sectional Absolute Deviation methodology, applying quantile regression approach, to detect aggregate level herding using a survivorship-bias-free dataset of daily firm level returns from April 2000 to March 2022. The dataset is subdivided into market episodes corresponding to pre-war, bubble, crash, post-crash, pre-COVID crash, COVID bubble and post-COVID crash periods. Exhibiting an evolutionary herding pattern over market episodes, the results depict that herding appears in pre-war period irrespective of the market directions, persisting in bubble episode in upmarket days, which then, turning into negative herding in down market days in crash episode. Subsequently, herding gradually disappears in post-crash episode, reappears with greater intensity in pre-COVID crash episode and disappears in COVID bubble and post-COVID crash episodes. This study attributes such wax and wane nature of herding in financial markets to a survival action, a rational heuristic, in keeping with Adaptive Market Hypothesis. The study is of peculiar importance to investors, policymakers, regulators and researchers, as presence of herding misprices securities and invalidates the existing asset pricing models constructed on the assumptions of investor rationality.

The paper is an extended contribution to the ongoing debate on cryptocurrency as a hedging instrument while investing in developed and emerging markets. At the edge of the 4th industrial revolution, the paper identifies diversification opportunities with technologically based assets and non-conventional assets like Cryptocurrency (BITW), Fintech (FINX), and Green Equity Index (QGREEN) with the Developed market (MSCI World Index) and Emerging market (MSCI Emerging Markets Index). The study is rigorous in methodology, including Granger Causality, Symmetrical and Asymmetrical Dynamic Conditional Correlation models, Diebold Yilmaz Spillover Index, and Network analysis. The study used robust statistical models like Granger Causality, Symmetrical, and Asymmetrical Dynamic Conditional Correlation models, Diebold Yilmaz Spillover Index, and Network analysis for a more accurate assessment of the investment alternatives. The results of the study aim to assist passive portfolio managers in investing in developed and emerging indices and looking for non-conventional investment options. The study assumes relevance for policymakers, as it deciphers the relevance of the cryptocurrency market vis-a-vis other emerging assets.

This study aims to demystify the financial sustainability social outreach trade-off debate in the case of Microfinance Institutions (MFIs) operating in India. In particular, the authors estimate the bias-adjusted efficiency of MFIs operating from 2010 to 2019 to scrutinize the mutual exclusivity between their twin aspects. Further, the study deploys bootstrap Data Envelopment Analysis (DEA) to estimate the robust efficiency estimate of individual MFIs. Further, the study uses a multi-dimensional approach to examine the trade-off debate between sustainability and outreach. Additionally, the study also ranks the MFIs based on their dual mission. The results suggest that the Indian MFIs are better at handling the financial dimension than the social aspect of MFIs. Moreover, the article claims the absence of a trade-off between the two goals of MFIs in India. Suryoday is the top-performing MFI in terms of financial and social aspects, followed by M-power. Further, the policymakers, top management, and microfinance professionals must redesign the regulatory and operational structure to ensure the maximum social outreach of MFIs without hampering their financial sustainability.

This study applied novel wavelet techniques to daily stock returns and COVID-19 case data from January 22, 2020, to March 31, 2022, for the five most COVID-affected countries (US, India, Brazil, France, and Turkey). We discovered that pandemic cases have a negative effect on stock returns across all nations. All countries except Turkey’s equity market returns and COVID-19 cases exhibit specific short-run and consistent long-run coherence. This study contributes to the existing literature about the financial implications of the pandemic. The current study empirically examine the positive/negative, long/short-run, and leading/lagging dependence of COVID-19 and financial equity markets of the top 5 COVID-19 affected countries. The current findings reveal particularized short-run and consistent long-run coherence among COVID-19 cases and equity market returns of all the sample countries except Turkey, and specified short-run and consistent long-run coherence of USA COVID-19 cases with Brazil, France, India, and Turkey stock markets returns, respectively. Furthermore, this study will augment the knowledge of the policy maker to ward off crises created by any future pandemic by their understanding of the stock market reaction to such unwarranted situations. This study will also guide the investment professional in making the right decision to mitigate risks arising from the pandemic.

This study examines the impact of subjective norm, attitude and perceived control behavior (financial literacy) on investors’ behavioral intention to invest in mutual funds based on the theory of planned behavior. We have applied Structural Equation Modelling - path analysis to examine the influence of financial literacy, subjective norms, and attitude on the behavioral investment intention of mutual fund investors. The study’s findings indicate that Investors’ choice to invest in a mutual fund is positively prejudiced by their subjective norms, attitude, and financial literacy. Subjective norms significantly influence investment decisions more than attitude and financial literacy. Age, gender, and level of education have no moderating effect on attitude, subjective norms, and financial literacy. The study is proved to be unique to the literature on behavioral finance. The study’s findings are eye-opening as the investment intentions in the mutual fund are influenced by subjective norms, indirectly signaling that investors lack awareness of mutual fund investment.

This paper analyzes the relationship between investors’ risk perception, heuristic biases (overconfidence, representativeness, availability bias, and anchoring bias), and the moderating role of sex and age. Since it is evident from the literature that investor risk perceptions affect investors rationally, the study explores the impact of risk perception on mental shortcuts or heuristic decision-making. The authors collected the data from 447 individual investors using a self-administered questionnaire to investigate the proposed phenomenon. After confirming the validity and reliability of the data obtained, we employed structural equation modeling to evaluate the relationship between risk perception and heuristic biases. We used process macro to scrutinize the moderating effect of sex and age in the mentioned constructs. The study demonstrates that risk perception affects three heuristic biases (i.e. anchoring, representativeness, and availability bias). Further, the outcome exhibits that the sex of a person moderates the relationship between risk perception and availability bias. The study could be helpful for individual investors, investment advisors, and policymakers. The investment advisor can gain insights into the different mental shortcuts their customers take to guide them appropriately. Governments and relevant policymakers can gain insights into the roadblocks to rational investment decisions to ensure the correct appraisal of the stock market. The present study fills the necessity to realize the effect of investors’ risk perception on decision-making heuristics and the moderating role of sex and age in the phenomenon.

The increased interconnection among financial markets and their susceptibility to economic and political fluctuations have spurred investors to seek out markets capable of offering hedging mechanisms for their diversified portfolios. This study aims to elucidate the intricate web of interdependence among various financial markets, namely oil Brent, global equity, green investment, Cryptocurrency, and Islamic markets, focusing on the analysis of tail dependence and lead-lag relationships within bullish and bearish contexts. Employing copula and wavelet techniques on data spanning from January 2014 to December 2022, the results indicate distinctive patterns of dependency and interaction among the examined financial markets. Notably, the observed dependency between specific markets does not extend uniformly across all markets, implying a bilateral influence that does not significantly impact the performance of unrelated markets. However, a noteworthy exception arises in the relationship between the Brent and crypto markets, where the influence may propagate to the green market during both bullish and bearish periods. Further analysis reveals that during bullish periods, the strongest dependence between Brent and green markets reaches 38%, contrasting with a 7% dependency during bearish periods. Additionally, a dependency of 25% is observed between global and green markets, consistent across both bullish and bearish conditions. Furthermore, the interaction between Brent and Crypto markets affects the green market by 5% during both bullish and bearish periods. These findings contribute to a deeper understanding of the dynamics within financial markets and offer valuable insights for investors seeking to manage risks and optimize their investment strategies.