Asia-Pacific Financial Markets最新文献

This study investigates the relationship of fund managers’ performance persistence with (a) personal characteristics of managers and (b) fund characteristics. The study uses a sample of fund managers from India to create a comprehensive dataset of manager returns from December 2006 to March 2022. Using the four factor performance model of Carhart (1997), we investigate the persistence in manager performance across (a) managerial characteristics and (b) fund characteristics based on one month holding period returns over previous 24-months estimation period. The study indicates considerable persistence among the top decile fund managers who are male, MBA-postgraduate, undergraduate with technical qualifications, and also from top institutions. It is also evident among managers who are old, and possess long experience. We also find evidence of persistence in the performance of managers from foreign funds, Indian funds, and also for the joint venture predominantly Indian funds. This study allows investors in mutual funds to make more informed decisions. It is also useful for recruiters and policymakers who are responsible for appointing mutual fund managers and making policy recommendations in light of continuing regulatory changes. This can be considered one of the earliest studies to analyse the relationship of fund managers performance persistence with (a) personal characteristics of managers and (b) fund characteristics from the perspective of an emerging Indian economy.

Recently, interest in socially responsible investing has grown, including new investment vehicles such as environmental, social, and governance exchange-traded funds (ESG ETFs). Despite their rising popularity, few studies have attempted to examine the performance characteristics of these stylized funds. This study aimed to fill this knowledge gap by elaborating on the performance attributes of ESG ETFs and examining fund managers’ security selection and market timing skills. Our results suggest that these funds generally underperform relative to conventional ETFs in many aspects. Additionally, the market timing skills of fund managers require improvement but are comparable to those of conventional ETFs. These results are robust to selecting the individual funds and alternative indices used in the sample. Furthermore, both the security selection and market timing skills of ESG ETF managers deteriorated significantly during the COVID-19 pandemic. Finally, the results indicate a slightly weaker cointegrated relationship between ESG ETFs and their benchmark indices when compared to conventional ETFs, suggesting that potential investors in ESG ETFs should carefully inspect the funds to make informed decisions.

The influence of Covid-19 pandemic crisis on rates of return is analyzed in this paper in the light of possible data manipulation related to reporting systems provided by the administration in the USA, Turkey and Poland. The study used various methods of analyzing the relationship of a discrete, non-discrete and dichotomous data nature between the studied variables. As a result, the strongest reaction of the market was observed in Turkey followed by the USA and Poland. It can be concluded that the reaction of the surveyed markets was influenced by the data manipulations. The added value of the article is related to the use of various methods to study phenomena and detect the impact of data manipulation on the markets.

In this study, we decompose momentum indicators for the Japanese stock market into two components, high-to-price and price-to-high. High-to-price has a lower downside risk and higher Sharpe ratio than price-to-high. We find that a conventional momentum strategy combines the characteristics of high-to-price in a bull market and those of price-to-high in a bear market. In particular, the large drawdowns of momentum strategies reported in previous studies seem to be largely owed to those of price-to-high in bear markets. It is possible that the mechanism generating factor returns differs among the three strategies.

Predicting the stock market remains a difficult field because of its inherent volatility. With the development of artificial intelligence, research using deep learning for stock price prediction is increasing, but the importance of applying a prediction system consisting of preparing verified data and selecting an optimal feature set is lacking. Accordingly, this study proposes a GA optimization-based deep learning technique (CNN-LSTM) that predicts the next day's closing price based on an artificial intelligence model to more accurately predict future stock values. In this study, CNN extracts features related to stock price prediction, and LSTM reflects the long-term history process of input time series data. Basic stock price data and technical indicator data for the last 20 days prepare a data set to predict the next day's closing price, and then a CNN-LSTM hybrid model is set. In order to apply the optimal parameters of this model, GA was used in combination. The Korea Stock Index (KOSPI) data was selected for model evaluation. Experimental results showed that GA-based CNN-LSTM has higher prediction accuracy than single CNN, LSTM models, and CNN-LSTM model. This study helps investors and policy makers who want to use stock price fluctuations as more accurate predictive data using deep learning models.

This paper investigated exchange rate and stock price volatility connectedness and spillover in Brazil, Russia, India, China, and South Africa (BRICS) during pandemic-induced crises. We first extracted volatility using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then volatility connectedness and spillover were investigated by using (Diebold and Yilmaz, International Journal of Forecasting, 28(1), 57–66, 2012) method. We find that exchange rate volatility and stock return volatilities are connected during pandemic-induced crises. The study also finds volatilities spillover among countries in the sample. Russia has strong volatility connectedness with India in these financial markets. The direction of volatility spillover is from Russia to India. Similarly, Brazil has strong volatility connectedness with South Africa and the direction volatility spillover is from Brazil to South Africa. Finally, China has a weak volatility connection with the remaining BRICS countries. Thus, the volatility transfer in these financial markets and across BRICS countries has economic implications.

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

This study aims to examine antecedents of the pension schemes in Indian context. The relationship between the factors underlying the perception of subscribers towards the pension plan: financial knowledge, investment strategy, and satisfaction of investors (employees) is examined. Further, the effect of financial security, future financial goals, risk appetite, and secured returns on the investment strategy and satisfaction are explored. After checking the measurement properties of the structured survey instrument using the structural equation modeling with Lisrel package, data collected from 480 employees working in various administrative units of a State in the northern part of India, were analyzed. The Hayes’s PROCESS was used in analyzing the moderated moderated-mediation complex model and the results reveal that (i) financial knowledge is positively related to (a) investment strategy, and (b) investor satisfaction. The investment strategy mediated the relationship between financial knowledge and employee satisfaction. Further, the results indicate that future financial goals (first moderator) and financial security (second moderator) moderated the relationship between financial knowledge and investor satisfaction mediated through investment strategy. The results also documented that risk appetite moderated the relationship between investment strategy and investor satisfaction; and secured returns moderated the relationship between financial knowledge and employee satisfaction. The novelty of this study stems from the three-way interaction between the financial knowledge, future financial goals, and financial security in influencing the financial strategy. The implications for research and practice are discussed.

Tobin’s Q is an established measure of firm performance, based on investor confidence. However, the association between Tobin’s Q and credit ratings is not well-established in the literature. Using a sample of Korean listed firms over the 2001–2016 sample period, Probit regression analysis shows that overall, Tobin’s Q is positively associated with credit ratings. However, for firms with a > 1 (1 <) Tobin’s Q ratio, a negative (positive) relationship exists. Moreover, in independent regressions, a threshold level if found where the effect of Tobin’s Q on credit ratings changes from being positive (0.2), to negative (0.3). To the best of our knowledge, we are the first to demonstrate that credit rating agencies are nuanced when making default risk assessments. Specifically, that in South Korea, a threshold level exists, at which increasing Tobin’s Q values reduce credit ratings. Empirical evidence of the different association between Tobin’s Q (market confidence) and credit ratings can extend the literature and offer insights to market participants. Furthermore, because Tobin’s Q is a commonly used proxy for financial performance in accounting lectures, the study has practical implications for academics in classrooms.

This study explores the effectiveness of entropy as a proxy of aggregate market risk, in explaining the cross-section of excess returns in asset pricing model, after controlling for established factors like market excess returns, size, book to market and momentum. The analysis considers Indian firms, given that Indian capital markets are characterized by relatively thin trading and higher volatility compared to developed markets. Entropy is estimated using Shannon Entropy. Factor mimicking portfolio is constructed based on Shannon Entropy, whose returns are used as additional risk factor in Fama–French–Carhart four factor asset pricing model. Gibbons Ross Shanken-F statistic and Adjusted R² are used to judge the efficacy of this factor in capital asset pricing model. All analysis is done using built in functions of python. Market beta, size and Book-to-Market are found to impact equity returns significantly. Entropy factor also impacts equity returns, but to a lesser extent. Explanatory power of asset pricing model is found to improve after inclusion of entropy factor, as indicated by GRS-F Statistic and Adjusted R². Entropy augmented Capital Asset Pricing Models can be used by firms to decide hurdle rate for project evaluation and by asset managers for identifying over-valued/under-valued securities. This is the first study that investigates the role of entropy in explaining asset returns, in addition to other established priced factors. This study is limited to Shannon Entropy only. Other forms of entropy may improve results further, and should be explored in future research.