Fund managers’ ability to evaluate risk has important implications for their portfolio management and performance. We use a state-of-the-art deep learning model to measure fund managers’ forward-looking risk assessments from their narrative discussions. We validate that managers’ negative (positive) risk assessments lead to subsequent decreases (increases) in their portfolio risk-taking. However, only managers who identify negative risk generate superior risk-adjusted returns and higher Sharpe ratios, and have better intraquarter trading skills, suggesting that cautious, skilled managers are less subject to overconfidence biases. interestingly, only sophisticated investors respond to the narrative-based risk assessment measure, consistent with limited attention by retail investors.

Using unique data on Alipay users' investment accounts, we find that, in addition to generating better performance than investors’ self-directed portfolios, robo-advising has a positive spillover effect on its adopters in terms that it improves their investment behaviors. Investors have more diversified portfolios and exhibit fewer behavioral biases in portfolio management and fund choices in their self-directed accounts after adopting robo-advising. The spillover effect is more prominent for adopters who interact with the service more actively and who were less sophisticated before adopting the app. We also find that adopters learn from the robo-advisor by simply imitating its portfolios or strategies. Collectively, this study provides large-sample, non-laboratory evidence that robo-advising effectively plays a role in educating investors through repeated interactions with its adopters and setting investment models that are easy to follow.

The adequate evaluation of mutual fund performance and of the fund managers’ ability to add value is an issue to which it has been given special attention in the recent financial literature. One of the traditional evaluation measures most commonly used is Carhart's alpha. However, one of the main problems of the evaluation methods that use the beta of the portfolios as a measure of risk and, therefore, Carhart's alpha is its sensitivity to the definition of the market portfolio. In this work we study the importance of defining the market portfolio using Carhart's alpha for a sample of UK mutual funds, and the influence of this market portfolio in the funds´ excess returns and in the performance ranking classification of the fund sample.

This paper assesses the impact of the severe acute respiratory syndrome (SARS) on the stock market of China. Our results indicate that the Chinese stock market reacts rapidly to the SARS epidemic. We provide strong empirical evidence that the epidemic has an immediate impact on the pharmaceutical and tourism industries. In particular, pharmaceutical companies are benefited from the outbreak of SARS, while the tourism sector is adversely affected. Our results imply the existence of a profitable trading rule during an epidemic.

Most existing empirical studies on affine term structure models have primarily focused on in-sample Þt of historical bond yields and ignored out-of-sample forecast of future bond yields. Using an omnibus nonparametric procedure for density forecast evaluation developed in this paper, we provide probably the first comprehensive empirical analysis of the out-of-sample performance of affine term structure models in forecasting the joint conditional probability density of bond yields. We show that although it is difficult to forecast the conditional mean of bond yields, some affine models have good forecasts of the joint conditional density of bond yields and they significantly outperform simple random walk models in density forecast. Our analysis demonstrates the great potential of affine models for financial risk management in fixed-income markets.