Asia-Pacific Financial Markets最新文献

This paper aims to examine the liquidity connectedness between major asset classes, including cryptocurrencies, oil, gold, stocks, and bonds, over the period from September 2014 to November 2022. Results from the time-varying parameter vector autoregression (TVP-VAR) show that the liquidity connectedness between the examined asset classes is generally low, with Bitcoin being the main transmitter of liquidity shocks while oil and bonds act as net receivers. Next, we employ the biwavelet analysis to investigate the co-movement between the liquidity connectedness index (TCI) and various uncertainty factors. Our findings suggest a weak correlation between the TCI and uncertainty factors, and especially no significant correlation between the TCI and geopolitical risk. However, some notable correlation still appears during the 2014–2015 and 2018–2021 periods. During the former period, the TCI plays the leading role, whereas during the latter period it is affected by various risk factors.

Accurately predicting market direction is crucial for informed trading decisions to buy or sell stocks. This study proposes a deep learning based hybrid approach combining convolutional neural network (CNN), attention mechanism (AM), and gated recurrent unit (GRU) to predict short-term market trends (1 day, 3 days, 7 days, 10 days) across different stock indices (BSE, HSI, IXIC, NIFTY, N225, SSE). The architecture dynamically weights the input sequence with the AM model, captures local patterns through CNN and effectively models long-term dependencies with GRU thus aiming to accurately classify either "buy" or "sell" positions of stocks. The model is assessed using classification and financial evaluation metrics involving accuracy, precision, recall, f1-score, annualized returns, maximum drawdown, and return on investment. It outperforms benchmark models, and different technical indicators including average directional index, rate of change, moving average convergence divergence, and the buy-and-hold strategy, demonstrating its effectiveness in various market conditions. The proposed model achieves an average accuracy of 98% in predicting the 1 day-ahead direction, and an average accuracy of 88.53% across all prediction intervals. The model was also validated using the wilcoxon signed rank test that further supported its significance over the benchmark models. The CAG model presents a comprehensive and intuitive approach to stock market trend prediction, with potential applications in real-world asset decision-making.

This study aims to evaluate the influence of digital inclusive finance on the income levels of farmers and elucidate its operational mechanism. Using statistical data spanning from 2011 to 2020 across 281 prefecture-level cities in mainland China, our findings establish a significant positive correlation between the developmental stage of digital inclusive finance and farmers’ income levels. Additionally, digital inclusive finance plays a constructive role in reducing income disparity between urban and rural areas, primarily through advancing agricultural technological innovations. Heterogeneity analysis reveals that the beneficial impact of digital inclusive finance is more pronounced in regions with higher human capital and lower levels of digital development. Furthermore, we observe a mutually reinforcing relationship between traditional financial development and digital financial inclusion. While affirming the fostering of inclusive and balanced development to some extent, this paper underscores the imperative of addressing the digital divide. The favorable effects of digital inclusive finance are diminished for individuals with low human capital and limited financial literacy, highlighting the necessity of enhancing financial literacy and education among rural residents as pivotal factors for digital inclusive finance to effectively fulfill its inclusive role.

Context/Motivation

Non-identification of Early Warning Signals (EWS) or Red Flag Indicators (RFI) on time is an important reason behind the rising trend in credit frauds in the Indian banking industry. Literature suggests that for effective identification of EWS, it is not enough to have a set of EWS but it is essential to rank them and highlight the most important ones to look out for. In the Indian context, there is no ranking of EWS for credit frauds, which is a serious challenge to practicing bankers.

Design/Methodology

This paper therefore ranks the EWS for credit frauds using a novel Natural Language processing (NLP) approach and further analyses the most important EWS impacting frauds.

Findings

This paper finds that the presence of early warning signals from Diversion of Funds, Inter-Group/Concentration of Transactions, Issues in Primary/Collateral Security (COLSEC), makes it very likely that frauds would be in the high-value category.

Originality

First, this is the first Indian study which develops a ranking or scoring of either EWS/RFI on the basis of NLP tools. Secondly, we use a unique methodology for identification of EWS based on NLP techniques, which makes possible the harnessing of a rich source of data, not so far attempted.

This article studies the profitability and arbitrage efficiency of the Chicago Mercantile Exchange (CME) Nikkei 225 futures. As one of the most typical quanto derivatives in the world, the CME Nikkei futures is traded in dollars while the underlying Nikkei index is traded in yen. The special characteristic involves more complicated uncertainties, which necessitate an investigation into its profitability and efficiency. To this end, we construct an arbitrage-free quanto pricing model to examine the mispricing of the CME Nikkei futures and the underlying spot prices for potential arbitrage opportunities. Distinguishing an ex-post trading rule from an ex-ante trading rule, we conduct non-parametric moving block bootstrap simulations to test the significance of profitability in the CME. The results show insignificant ex-post profitability but significant ex-ante profitability before and after the 2008 global financial crisis. Moreover, delayed execution significantly impacts the futures profitability. Profitable arbitrage opportunities are confirmed by implied transaction costs and explained by lagged absolute mispricing, lagged error, futures time to maturity, stock volatility, and trading volume in the CME. These findings have important implications for practitioners in their cross-border arbitrage trades, and for policy makers in their regulation of quantos in futures globalization.

This study delves into the ramifications of financial derivatives usage on the firm value among Indian non-financial firms, covering both the COVID-19 crisis period (2020–2021) and the preceding stable phase (2015–2019). This comparative analysis aims to discern how the strategic use of derivatives influences firm valuation across varying economic conditions. Analyzing 712 firm-year observations during the pandemic and extending to 1735 observations in the pre-COVID era, our findings reveal that while foreign exchange and interest rate derivatives consistently enhance firm value, the use of commodity derivatives exhibits a complex relationship with firm value, becoming notably negative during the pandemic. This suggests that derivatives’ effectiveness in risk management and value preservation is contingent upon both the type of derivative and the economic context. Our research underscores the critical role of derivatives in navigating financial uncertainties, offering nuanced insights that enrich our understanding of firm-level risk management strategies in both stable and turbulent times.

The inclusion of digital finance provides vital financial support for rural revitalization and farmer income, and it has emerged as a significant impetus for social and economic growth. This study uses a spatial measurement model to assess the impact of DIF on FI, based on panel data sets from 2011 to 2020 in 31 provinces of China. The following conclusions were drawn: First, DIF expands financial services, lowers the threshold of financial services, alleviates financial exclusion through advanced digital technologies, promotes financial institutions to serve rural areas, and continuously releases “digital dividends” to rural areas. Second, the development of DIF mainly promotes FI through digital payment, credit, and insurance; this, in turn, indirectly promotes farmers’ income through regional economic growth agglomeration characteristics of “low–low.” Third, the development of DIF plays a vital role in the income structure, promoting wage, property, and transfer income. However, it is not apparent in household operating income due to DIF being in rural areas’ early stages of development. Finally, the income-increasing effect of DIF is regionally heterogeneous, manifesting better in the eastern region than in the western and central regions.

This study aimed at differentiating the qualitative characteristics (Basic and Technical) of the Indian Socially Responsible (SR) funds. The study also compared the performance of SR funds with their benchmark indexes. The novelty of the current study is analyzing the impact of the market return and the Covid-19 outbreak on the returns of SR funds. The study used content analysis, independent t-test, and multiple linear regression analysis. The content analysis results highlighted that the majority of the SR funds adopt the Environmental, Social and Governance (ESG) integration approach, invest in large-cap, high-growth companies with good ESG score, and have an investment committee. The comparative analysis indicated that out of 14 SR funds, only four funds outperformed their benchmark index. The regression analysis showed that the selected four funds had a significant relationship with their respective benchmarks and a non-significant relationship with the Covid-19 outbreak. The current study contributes to SRI literature by identifying the differentiating characteristics of the Indian SR funds. It also contributes to the extant literature a comparative analysis, assessing the performance of the SR funds with their benchmark index. Further, determining the impact of the market return and the Covid-19 outbreak on the returns of SR fund is also a contributing factor of the present study. Findings are useful for individual investors, institutional investors and fund managers, as they can launch more SR funds on similar terms. Findings are useful for regulators and policymakers for framing new rules and regulations for boosting ESG adoption by the companies.

This study empirically examines price discovery and volatility spillover between the spot and futures markets for India using both daily and intraday data of Nifty50 and its associated futures index. Within the Johansen cointegration framework, the study for the first time used the recursive cointegration method for examining the dynamics of the long-run relationship between the equity spot and futures markets. To analyze the volatility spillovers between the two markets the study employs BEKK–GARCH model. This model ensures the positive definiteness of the conditional covariance matrix and estimates the same with less number of parameters as compared to the traditional multivariate GARCH models including the VECH model. The empirical results show that there is a stable long-run relationship between the two markets. The Granger causality findings support the notion that the futures market plays a dominant role in causal relationships. There is also a two-way volatility spillover between the two markets. However, it is relatively seen that the futures market has strong transmission effects which are carried over to the spot market. This is intuitive because the futures market is more sensitive to new information than its counterpart due to differences in cost and liquidity. The results based on the latest data, offer a new perspective on the lead–lag relationship between India’s stock market futures prices and spot prices. These findings can benefit stock market stakeholders by protecting themselves from uncertainty and developing futures contracts that will increase the efficiency of the Indian equity market.

This study provides empirical evidence of the multifaceted impact of corporate governance on stock price crash risk within the distinctive corporate governance landscape of the Pakistan Stock Exchange. Utilizing data from all non-financial firms listed on the Pakistan Stock Exchange, the study employs a robust methodology to control for potential endogeneity and reverse causality issues commonly encountered in corporate governance research. The findings reveal a dominance of family firms in the Pakistani market, holding majority control over boards and decision-making processes. Contrary to expectations, these corporate boards and family ownership structures do not contribute to the reduction of crash risk. However, the presence of block ownership indicates that institutional investors are predominantly short-term participants. In alignment with previous research, institutional investors in this context tend to play a passive and negative role in monitoring firms. Rather than applying monitoring pressure, they exhibit a tendency to exit, further contributing to price reductions and subsequent crashes. These insights underscore the necessity for market regulators to develop a corporate governance framework that not only ensures investor protection but also encourages firms to diminish information asymmetry through improved disclosure and transparency practices.