Machine learning for US cross-industry return predictability under information uncertainty

This paper investigates the association between industry information uncertainty and cross-industry return predictability using machine learning in a general predictive regression framework. We show that controlling for post-selection inference and performing multiple tests improves the in-sample predictive performance of cross-industry return predictability in industries characterized by high uncertainty. Ordinary least squares post-least absolute shrinkage and selection operator models incorporating lagged industry information uncertainty for the financial and commodity industries are critical to improving prediction performance. Furthermore, in-sample industry return forecasts establish heterogeneous predictability over US industries, in which excess returns are more predictable in sectors with medium or low uncertainty.     

The disabling effects of enabling social policies on organisations’ human capital development practices for women

Paid parental leave and externally provided childcare are social policies designed to enhance parents' labour force participation. These policies influence not only men's and women's decisions regarding their labour market activity but also organisational decision makers' (ODMs) expectations about their employees' availability to work and thus, their willingness to invest in their employees' human capital. Using a sample of over 13,000 individuals from 19 countries, we investigate the interaction between gender and social policies on human capital development practices. In line with statistical discrimination theory, which suggests that ODMs hold different expectations about female and male productivity, we find that paid parental leave and externally provided childcare are negatively associated with the provision of human capital development for women but not for men.     

复杂信任网络群组综合评价方法及应用研究

考虑评价环境的复杂不确定性、评价专家的信任行为特征以及群组专家的共识等问题，提出了一种区间Fermatean模糊信任网络群组综合评价方法。首先，针对区间Fermatean模糊评价信息的集成问题，提出了区间Fermatean模糊信息的运算规则，并在此基础上提出了一种区间Fermatean模糊Frank集成方法。其次，考虑到评价专家间信任关系对专家权重和共识达成的影响，构建了基于语言术语的信任网络以计算专家权重，同时设计了个性化反馈机制，进而构建了信任共识交互模型以提高群组共识度。此外，针对评价指标权重未知的情形,提出了区间Fermatean模糊熵权法获取指标权重。最后，通过高校商科类本科生统计设计能力的评价问题验证所提出方法的可行性和优越性。     

Time-variation,multiple testing,and the factor zoo

Bayesians circumvent the need for significance threshold correction when multiple testing and we recommend controlling the Type-S (sign), rather than the Type-1, error rate because it yields more reliable frequency properties for inferences. Our unified Bayesian framework, with theory-informed priors, identifies two breaks (2001 and 2008) in our 1980–2018 sample period. After each break the set of characteristics changes, and only market beta is selected in all regimes. In a portfolio application, the method generates significantly larger Sharpe ratios after transaction costs than a range of benchmark methods, including the same model that uses a Type-1 (not Type-S) error framework.     

Improving risk classification and ratemaking using mixture-of-experts models with random effects

In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit-weighted Reduced Mixture-of-Experts, which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness-of-fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history.     

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.     

中国数字经济规模测算研究——基于国际比较的视角

伴随着信息技术的快速发展及其与经济运行方式的不断融合,数字经济已被视为经济增长的“新引擎”,在世界上多数国家的发展战略中占据重要位置,数字经济规模测算研究是当前国内外统计机构与研究学者面临的亟待解决的问题。本文在系统梳理信息经济、互联网经济、数字经济演变历程的基础上,提炼数字经济的内涵与形成要素,构建数字经济规模核算框架,界定数字经济核算范围,确定数字经济产品,筛选数字经济产业,对2007—2017年中国数字经济增加值与总产出等指标进行测算,并将测算结果与美国和澳大利亚进行比较。测算结果表明:2017年,中国数字经济增加值53028.85亿元,占国内生产总值的6.46%;数字经济总产出147574.05亿元,占国内总产出的6.53%。基于国际比较的视角,2017年,中国数字经济增加值约为美国的58.12%;数字经济增加值占GDP比重低于美国0.44个百分点;2016年,中国数字经济增加值约为美国的52.77%,占GDP比重低于美国0.77个百分点,略高于澳大利亚0.03个百分点。近年来,中国数字经济增加值年均实际增长率明显高于美国和澳大利亚。2008—2017年,中国数字经济增加值年均实际增长率达14.43%,明显高于国内生产总值年均实际增长率8.27%,数字经济推动经济增长的作用明显。本文深化了数字经济规模核算框架研究,系统监测了中国数字经济的发展规模与结构,为进一步完善中国数字经济统计核算体系和提出促进数字经济高质量发展的战略措施提供参考依据。