Review of Pacific Basin Financial Markets and Policies

In this paper, we use a unique dataset to examine the cause of return irregularity in financial markets around the end of the year. Year-end return irregularities are often attributed to either window-dressing or preferred habitat. We study year-end return irregularities in the foreign exchange swap market. We find that the return of a contract ending before the end of the year is lower than the return of a contract ending after the beginning of the year. The results support the existence of the preferred habitat and provide some evidence of window dressing in foreign exchange swaps.

We examine whether investor sentiment is associated with loss reserve estimates of property-liability (P/L) insurers. Using the Michigan Consumer Confidence Index as a proxy for sentiment, we find that the level of investor sentiment is negatively associated with discretionary component of loss reserve error. In contrast, our evidence does not suggest a similar relationship hold for investor sentiment and nondiscretionary loss reserve error. Further analysis indicates that stock insurers are more sensitive to investor sentiment than mutual insurers, in terms of discretionary component of loss reserves. The results are consistent with our hypothesis that P/L insurers cater to investors’ optimism (pessimism), driven by investor sentiment, via discretionary loss reserve claims. For robust test, we also measure investor sentiment by using two alternative proxies: the Conference Board Consumer Confidence Index, and the index in the stock market developed by Baker and Wurgler (2006, 2007). The results are consistent. Our study discovers a new rationale for why insurers may use discretion over their loss reserves.

This study examines whether the relationship between managerial ability and firm performance is driven by familiness and foreignness of the CEOs. We divide the sample of CEOs according to their familiness and foreignness and estimate CEOs abilities via DEA and Tobit regression. We then employed dynamic GMM panel estimator to address the endogeneity issues. Based on a sample of 361 firms in Malaysia over 2011–2015, we documented empirical evidence that managerial ability of local CEOs with foreign working or education experience are most positively related to Tobin’s Q, followed by local CEOs without foreign experience. We further found that local CEOs contribute to firm performance only if they are not family connected with the firm. Last, our findings also suggest that the presence of foreign CEOs improve the firm performance when they are nonfamily CEOs originated from high management practice countries.

We present a parsimonious method of improving forecasts and show that fit, the discrepancy between model forecasts and realized values, is persistent for individual stocks. Conditioning on fit profoundly affects the forecast error for future and out-of-sample returns. Forecasts of stock price direction with the best (worst) decile of historical fit are correct 63.6% (49.2%) of the time and are significantly different from the unconditioned model’s 56% accuracy. We find that superior factor forecasts are essential to profit from model conditioning and conclude that analysts who possess superior factor estimates can dramatically improve their forecasts through the technique we present.

This study examines the decision to underwrite dividend reinvestment plans (DRPs) by Australian listed firms over the sample period 1995–2013. We find that the decision to underwrite a DRP for non-financial firms is negatively related to the level of franking credits attached to dividends, but positively related to the leverage of the firm and the discount for new shares issued in lieu of dividends. Non-financial and financial firms that are larger in size are more likely to underwrite their DRP. Lastly, underwriting of DRPs decreased with the onset of and subsequent to the height of the global financial crisis.

This study examines the price effects of investment-grade–corporate bond transactions on the Australian Securities Exchange (ASX). Results indicate that both purchases and sales of exchange traded corporate bonds incur significant transaction costs upon execution. Post execution, purchases of all sizes either experience price continuations, or no significant price reversals, suggesting the presence of information. Sales of all sizes experience complete price reversals, implying that selling corporate bonds conveys no information to the market. These results are consistent with the majority of equity market studies that document a similar asymmetry between purchases and sales. Analysis of the determinants of price effects associated with bond market trades reveals that trade-size, market conditions, underlying stock price volatility, underlying stock turnover, bid–ask spreads (BAS), and market depth are associated with the magnitude of price movements surrounding these trades. Contemporaneous equity returns appear to have no effect on the price impact of corporate bonds.

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the $k$-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.