Asia-Pacific Journal of Financial Studies最新文献

In this study, we propose a two-step, less-volatile value-at-risk (LVaR) estimation using a generalized nearly isotonic regression (GNIR) model. In the proposed approach, a VaR sequence is first produced under the generalized autoregressive conditional heteroskedasticity (GARCH) framework. Then, the VaR sequence is adjusted by GNIR, and the generated estimate is denoted as LVaR. The results of an empirical investigation show that LVaR outperformed other VaR estimates under the classic equally weighted and exponentially weighted moving-average frameworks. Furthermore, we show not only that LVaR is less volatile, but also that it performed reasonably well in various backtests.

Using data from Singapore, South Korea, and Taiwan, we estimate the coefficient of relative risk aversion (RRA) in the constant relative risk aversion utility specification of the consumption-based capital asset pricing model. Conventional instrumental variables methods find that the coefficient of RRA is low but the inverse of it—the elasticity of intertemporal substitution in consumption—is also low. Such contradictory findings could be attributed to instruments being weak. Using weak-instrument robust tests, we find from the equity market data that the coefficient of RRA is rather high, which could potentially explain the high equity premiums in these three East Asian economies.

In this study, we empirically analyze Chinese firms listed on US exchanges to examine the standalone effects of geographic and market proximity on equity performance. In this regard, we find a negative association between Chinese institutional investors domiciled in the same country where investee firms are incorporated and improved earnings management. However, we find no significant relationship between US institutional ownership and earnings management. Hence, our findings support the geographic rather than the market proximity advantage. Furthermore, the geographic proximity advantage is more substantial in firms with high information opacity, and the results are not altered by choice of earnings management variables.

This paper aims to evaluate the predictive power of financial variables by using various machine learning methods. An analysis is conducted on data for the Korean stock market, which is representative of emerging markets, over 32 years from 1987 to 2018. The study shows that median regression is  a more efficient tool than mean regression in the presence of potential heterogeneity of stocks, significantly improving performance in terms of average realized monthly return. This suggests that median regression can have better predictive performance in emerging markets where there are likely to be outliers. Additionally, a gradient boosting machine (GBM) is found to be better than a traditional linear model both in prediction accuracy and portfolio performance. The hedged return from GBM is on average 2.89% per month with an annualized Sharpe ratio of 0.93 in the median regression. The neural network (NN) is also tested and shown to perform best when the number of hidden layers is two or three. Finally, we evaluatea list of predictor variables with various measures of variable importance. Variables of risk, price trend and liquidity are found to serve as important predictors.

In this study, we investigate whether extreme liquidity risk is priced in the China A-shares market. We find that the market extreme liquidity risk significantly and negatively predicts market returns up to 9 months. In addition, the extreme liquidity risk beta of individual stocks commands a positive monthly premium of 0.75%. Moreover, our findings show that the extreme liquidity risk beta can subsume the tail risk beta in predicting stock returns. Furthermore, our findings show that both the potential selling pressures caused by insiders and by institutional investors significantly and positively influence an individual stock's extreme liquidity risk beta. We also find that the potential selling pressure component of the extreme liquidity risk beta significantly and positively predicts stock returns. Taken together, our evidence demonstrates that a stock's extreme liquidity risk beta provides a channel through which the stock's potential selling pressure caused by both insiders and institutional investors influences its expected return in the China A-shares market.

This study investigates the pricing of liquidity risk in the Korean corporate bond market. We use three different liquidity factors — namely, aggregate market liquidity, liquidity innovation, and predicted liquidity. The empirical results show that, while a liquidity premium exists in the Korean corporate bond market when measured by the market liquidity factor, a liquidity discount occurs when measured by the predicted liquidity factor. Drawing on prior studies, we further describe that the lower (higher) returns for portfolios with a high sensitivity to unexpected liquidity shocks may be attributable to the infrequent (frequent) trading of AAA(A)-rated bonds in the Korean market. Finally, our findings suggest that while a liquidity premium exists in expectation, investors are penalized for taking predicted liquidity risks in the Korean corporate bond market.

Exploring the turnover of city heads in China, I find that state-owned enterprises (SOEs), which possess competitive advantages (especially resource-based) due to innate government connections, significantly increase innovation compared with non-SOEs when local policy uncertainty heightens. In particular, when the turnover of city heads occurs, SOEs are able to obtain a larger amount of external financing at lower costs than non-SOEs. Additionally, SOEs that file invention patents at the height of local policy uncertainty have better product market performances. My findings indicate that heightened local policy uncertainty enables firms with innate government connections to explore first-mover advantages through innovation activities.

Using 2.09 million pieces of media coverage associated with Chinese firms during 2006–2019, we find a significantly positive relationship between media coverage and labor investment efficiency. Both newspaper and online media coverage can effectively reduce firms' labor over-investment and under-investment. Our results are robust after addressing a range of endogeneity concerns. Further analyses show that the positive relationship between media coverage and labor investment efficiency is more pronounced for firms with higher labor cost stickiness or when human capital is more important to the firm's business model. Mechanism analyses reveal that media coverage improves labor investment efficiency through the compensation incentive mechanism, supervision mechanism, and information disclosure mechanism.

This study investigates the relationship between corporate social responsibility (CSR) and financial distress in China using the O-score and a Chinese sample of 4202 observations between 2011 and 2017. The relationship is accordingly more pronounced in weak corporate governance firms with low levels of institutional shareholders and free cash flow and among non-state-owned enterprises and mandatory CSR disclosure firms. The findings are robust to endogeneity through robustness checks and the 2013 Sichuan Lushan earthquake as an exogenous shock to CSR. Ultimately, the study will help investors, shareholders, and policymakers appreciate the impact of CSR dimensions.