Asia-Pacific Financial Markets最新文献

The green bond (GB) is a new financial product in the green finance field that has recently become a corporate social responsibility (CSR) tool for organizations. Previous studies show that high-CSR firms receive more trust from shareholders during a financial crisis. This paper aims to assess the stock performance of publicly listed Chinese companies that issued GBs during the COVID-19 pandemic. The bond sample covers 2016–2019 and consists of 67 listed issuers. The paper uses the event study method based on the market and Fama-French (1993) three-factor models. Our results show that GB issuers exhibited significantly positive cumulative abnormal stock returns on the official announcement dates of the COVID-19 outbreak. The positive cumulative abnormal returns are mainly driven by non-financial GB issuers rather than financial GB issuers. The results reflect the attitudes of investors toward GB-issuing companies primarily in the context of the crisis and contribute to the development of green finance policies.

This paper focuses on managing exchange rate risk associated with a secondary, non-USD exchange rate of Japanese yen (JPY). Employing Korean firm data, our preliminary analysis reveals that Korean firms are exposed differently to changes in the KRW/JPY rate than to changes in the KRW/USD rate. Our results show that firms exhibiting significant shifts in exposure from pre- to post-global financial crisis have distinctively different firm attributes including more currency derivatives use and lower firm values, compared to firms exhibiting little such shifts. A further analysis reveals that the lower values of high exposure firms are attributable mainly to the financial risk from foreign currency borrowing, but not to the operating risk resulting from exporting activities. Hence, the currency derivative use by Korean firms hardly helps them mitigate the value loss from heightened capital costs of foreign borrowing following the crisis.

How does the market react when more or fewer investors are allowed to trade certain stocks? Stock Connect, a cross-border investment channel between mainland China and Hong Kong, provides a natural testing ground. Investors are allowed to trade a list of qualified stocks from the stock market on the other side, and when a stock is removed from the list, investors can only sell but cannot buy that stock. We find that the inclusion of stocks is correlated with abnormal returns, implying downward-sloping demand curves for stocks. The effect weakens over time and disappears in about 40 trading days. There are no abnormal returns when stocks are removed from the list. On the other hand, when investors can only sell some stocks, they have a significantly higher propensity to sell. Their trading style becomes more contrarian for such stocks, and they tend to trade in small amounts. After 6 months, their investment behavior returns to that before the removal.

The main focus of this investigation is potential Granger causal relationships between the insurance market, Information and Communications Technology (ICT) infrastructure, and economic growth in a sample of high- and middle-income countries (H&MICs) from 1980 to 2019. We deployed a panel vector autoregressive model, and found that in the long run, the insurance market and ICT infrastructure Granger-cause economic growth. In the short run, we found robust causal links, but they vary in nature. The findings suggest that H&MICs should base ICT infrastructure planning on strategies that endorse economic growth and policies that may also promote insurance market development.

Graphical Abstract

The purpose of this study is to explore the factors affecting the selection of multibagger stocks in the securities market. Further, the study aims to develop a model using interpretive structural modeling (ISM). Thereafter, the driving and dependence power of factors was found using matriced impact croises multiplication appliquee a un classement (MICMAC). A group of financial analysts and academic experts having experience in dealing in the Indian securities market were consulted and interpretive structural modelling (ISM) is adopted to develop the contextual relationship among various factors for each dimension of multibagger stocks selection. Further, to identify the driving and the dependence power of factors, the results of the ISM are used as an input to MICMAC analysis. The results of the study indicate the large potential market (C11), visionary leader (C13), unique business model (C6), understanding of the sector (C1), and promoter and management capability (C2) are the dominant factors. MICMAC analysis indicates that driving, dependent and linkage factors are 4, 5, and 4 respectively. The factors obtained for ISM model development and MICMAC analysis are based on the experts’ opinions. As it is a subjective judgment, there are chances of biasness on basis of personal opinions. A questionnaire survey can be conducted to gather viewpoints on these factors from more financial experts and portfolio consultants. The study has been executed in discussion with financial analysts and academic experts having experience in dealing in the securities market. Hence, derived results have practical validity. The securities market is quite volatile in nature and the right choice of multibaggers may prove to be wealth creators for the general public. Investors may look for the derived factors for investing their savings into profitable channels by picking up those stocks which may prove to be multibaggers in near future. The development of a model for the identification of factors affecting the choice of multibaggers in the securities market is the original contribution of the authors.

This paper examines the spillover effect from Chinese stock market to select emerging economies to check the diversification opportunities. The study analysed the data in three different periods including full period from January 3, 2000 to February 7, 2020; first sub period from January 3, 2000 to October 18, 2009 and second sub period from October 19 to February 7, 2020. We applied Granger Causality and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) to investigate the spillover between Chinese and emerging economies. Referring to the Granger causality, it reveals that there is bi-directional causality between China and Indonesia only in full period. Further, DCC-GARCH indicates that there is spillover effect from the Chinese market to the Indonesian stock market in full period of observations both in the short run and long run. There is no spillover effect from China to emerging economies in first and second sub periods. We recommend that portfolio managers investing in Chinese economy may explore emerging economies as possible destinations to diversify their risk.

This study investigates the dynamic volatility connectivity of important environmental, social, and governance (ESG) stock indexes from May 2010 to March 2021. The empirical research is focused on five major S&P ESG stock indexes from the US, Latin America, Europe, the Middle East and Africa, and Asia Pacific regions. The study reveals that ESG stock indexes in the Middle East Africa, and Latin America are net shock transmitters, whereas the United States and Asia Pacific are net volatility receivers. Furthermore, the study finds that bilateral intercorrelations are higher among US, Latin America, and Europe region group pairs and weaker in relation to Middle East Africa and Asia Pacific region group pairs, indicating the presence of contagion within developed and/or emerging regions, which has relevance for portfolio and risk management.

In this paper, we attempt to understand and identify the cyclical fluctuations in cryptocurrency markets. To this end, we apply the Markov-Switching approach on daily prices of 17 selected digital currencies. This model allows us to capture the nonlinear structure in cryptocurrencies’ prices. The empirical results clearly show potential difference(s) among digital currencies when they react to the varying levels of the pandemic's severity. The existence of two distinguishable states and each state seems to be characterized by different features of market cycle’s phase for each cryptocurrency. So, the Covid19 pandemic affects asymmetrically the different market phases of digital currencies. Such findings can have insightful portfolios implications.

The presented study investigated the effect of index concentration on component security and index variances to explore the possibility of concentration risk and its impact on index performance in different markets. The study also investigated the 1/n index with the market cap index to find possible concentration costs for the investors. We analyzed BRICSU (BRICS plus USA) by applying various tools for concentration measures and determining index volatility and returns with the help of the mean–variance model. We did a simple simulation to understand the sensitivity of relationships. The study found the impact of index concentration on index variance, component security covariance, and index performance varies with the market. It may be due to different levels of investor biases and the inclusion of multinational companies in the index. We show how excessive growth of a few companies does not increase risk in the index, even delivering information benefits to investors. The lower Sharpe ratio of the Equal weighted index confirms the nonexistence of any index concentration cost for investors. We concluded index concentration is a generic process in the competitive market condition.

Demonetization is an act of divesting a currency unit from its legal tender. In a developing country, the act of demonetization will have a direct influence on various sectors. An event study is an empirical analysis to investigate the effect of such unforeseen events. In this study, we investigate the impact of demonetization on the Indian stock market. For the analysis, daily data from the NIFTY 50 Index during demonetization have been analyzed within the observation of different event windows. These event windows are framed as 0–7 days, 0–14 days, and 0–30 days to understand the impact of demonetization during the analysis period 8th November 2016–21st December 2016. The study concludes that the impact of events on the Indian stock market lasted for a short time-period and the market recovered within 1 month. More precisely, in the case of demonetization, though the stock market initially viewed the event as disruptive, Cumulative Abnormal Returns bounce back to indicate that the negative financial impact was not as severe as the industry perceived.