Active investment manager portfolios and preferences for stock characteristics

The present study investigates the stock characteristic preferences of institutional Australian equity managers. In aggregate we find that active managers exhibit preferences for stocks exhibiting high‐price variance, large market capitalization, low transaction costs, value‐oriented characteristics, greater levels of analyst coverage and lower variability in analyst earnings forecasts. We observe stronger preferences for higher volatility, value stocks and wider analyst coverage among smaller stocks. We also find that smaller investment managers prefer securities with higher market capitalization and analyst coverage (including low variation in the forecasts of these analysts). We also document that industry effects play an important role in portfolio construction.     

新校区建设全过程跟踪审计思考

新校区建设成为令人瞩目的亮点,但对于动辄数千万上亿、甚至十亿的巨额建设资金,以及如何适应紧迫的发展需要,如何建设好新校区,成为高校管理层重点关注的焦点。基于以上的原因,本文主要从新校区开展建设全过程跟踪审计的必要性、基本保障措施、审计的工作重点等方面阐述了对新校区建设全过程跟踪审计的思考。     

Evaluating the performance of global emerging markets equity exchange-traded funds

We examine the performance of passively managed exchange-traded funds (ETFs) that provide exposure to global emerging markets equities. We find that the tracking errors of these funds are substantially higher than previously reported levels for developed markets ETFs. ETFs that use statistical index replication techniques turn out to be especially prone to high tracking errors, and particularly so during periods of high cross-sectional dispersion in stock returns. At the same time, we find no convincing evidence that these funds earn higher returns than ETFs that rely on full-replication techniques.     

A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification

We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on simulated datasets and two real-world datasets.