Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

Jensen's alpha and the market‐timing puzzle

Theory predicts that market‐timing activities bias Jensen's alpha (JA). However, empirical studies have failed to find consistent evidence of this bias. We tackle this puzzle in a nested model analysis and show that the bias contains an exogenous market component that is unrelated to market‐timing skill. In a comprehensive empirical analysis of US mutual funds, we find that the timing‐induced bias in JA is mainly driven by this market component, which is uncorrelated with measured timing activities. Measures of total performance that allow for timing activities are virtually identical to JA, even if timing activities are present in the evaluated fund. Hence, we conclude that JA is a sufficient measure of total performance.     

Green human resource management: Development of a valid measurement scale

The objective of the study was to develop a valid measurement scale for green human resource management (HRM). Even though the common practices of green HRM have been presented in much of the literature, the previous studies focused only on a small number of functions in integrating environmental management with HRM. Additionally, the measurement of green HRM practices still calls for empirical validation. The two‐stage methodology of structural equation modeling in AMOS was employed for data analysis. Exploratory factor analysis revealed seven dimensions of the construct measured by 28 items. Confirmatory factor analysis confirmed the factor structure. The measuring instruments revealed convergent and discriminant validity. Several model fit indices indicated the model fitness. The study provided supplementary evidence on the underlying structure of the construct that can be valuable to researchers and practitioners in this area.     

How many factors are important in U.K. stock returns?

I use the sequential approach of Harvey and Liu ([2018]. Lucky factors (Working Paper). Duke University) to build linear factor models in U.K. stock returns among a set of 13 candidate factors using individual stocks and three groups of test portfolios between July 1983 and December 2017. My study finds that the Market factor is the dominant factor in reducing mispricing in individual stocks and test portfolios regardless of the pricing error metric used. The Market factor has a bigger impact when using a value weighting pricing error metric. Whether a second factor is used or not depends upon which metric is used for mispricing and the time period examined. My study finds support for a two-factor model for the whole sample period of the Market factor and the Conservative Minus Aggressive (CMA) factor of Fama and French ([2015]. “A five-factor asset pricing model.” Journal of Financial Economics 116: 1–22) when giving greater weight to the mispricing of larger companies.     

我国中小企业三维差异化特征比较研究

作为推动国民经济增长和促进社会稳定的基础力量,中小企业在市场中扮演着最活跃的经济主体。与大企业相比,中小企业有其鲜明的成长特征,从动态演进视角选取体量规模、生命周期、要素需求三个维度,系统探讨了中小企业的差异化特征。结论显示:体量规模特征反映在个体与群体以及经济板块之间;生命周期特征体现在死亡陷阱和空间格局演变上;要素需求特征会根据阶段创新内容的不同做出适时调整。     

Sparse estimation of dynamic principal components for forecasting high-dimensional time series

We present the sparse estimation of one-sided dynamic principal components (ODPCs) to forecast high-dimensional time series. The forecast can be made directly with the ODPCs or by using them as estimates of the factors in a generalized dynamic factor model. It is shown that a large reduction in the number of parameters estimated for the ODPCs can be achieved without affecting their forecasting performance.     

Measuring industrial upgrading: applying factor analysis in a global value chain framework

ABSTRACT

A key question for promoting international competition is how to improve the position of countries and industries in global value chains (GVCs). The first step is to properly measure industrial upgrading in GVCs. This is not a trivial issue because upgrading has not been defined unambiguously. Several authors have used different (and sometimes related) measures, all of which indicate certain aspects of upgrading. Rather than trying to find the single, ultimate measure of upgrading, we propose a different approach. We examine the multidimensionality of industrial upgrading, using eight indicators in factor analysis. Four of the eight indicators adopt the GVC perspective and include, for example, the growth of the share in value-added exports. We provide three quantitative dimensions of industrial upgrading: process upgrading, product upgrading, and skill upgrading. With these dimensions, we compare and analyze the upgrading of different countries and industries using the World Input–Output Database.     

The impact of foreign direct investment on productivity: New evidence for developing countries

We provide new empirical evidence on the relationship between inward foreign direct investment (FDI) and total factor productivity (TFP) growth using cross-country data for 51 developing countries over the period 1984–2010. Our results suggest a weak direct effect of FDI on TFP growth but, after accounting for the roles of human capital and institutions as contingencies in the FDI-TFP growth relationship, we find a robust FDI-induced productivity growth response dependent on these ‘absorptive capacities’. However, the relevance of the human capital contingency effect diminishes when the effect of institutions is also considered, which suggests that improving institutions is relatively more important than human capital development for developing countries to realise productivity gains from FDI.     

Minorías étnicas foráneas y determinación salarial en China

Si bien algunos estudios observan que las minorías étnicas urbanas en China perciben salarios más bajos que la mayoría han, otros no constatan la existencia de esa brecha. Para resolver esta contradicción, los autores proponen desglosar las experiencias laborales por grupo étnico minoritario. El análisis de las minorías étnicas sobre un amplio conjunto de datos indica que las «minorías foráneas», como los tibetanos y los grupos túrquicos, sufren una penalización salarial significativa cuando se controlan las covariables, pero no ocurre lo mismo con la totalidad de las minorías. Estas conclusiones son robustas en varias especificaciones y tienen importantes implicaciones teóricas y en materia de políticas.     

山西省乡村休闲旅游地空间分布特征及影响因素研究

［目的］通过对山西省乡村休闲旅游地的空间分布特征及影响因素进行研究，旨在为山西省的乡村旅游发展提供合理的参考建议。［方法］选取山西省213处国家级和省级乡村旅游示范点为研究对象，运用最临近点指数对其空间分布类型进行判别，并运用地理集中指数和地理联系率对其分布均衡程度和地理联系程度进行考量，通过核密度图进一步分析其空间分布指向，在此基础上对影响因素进行分析。［结果］山西省乡村休闲旅游地空间分布呈集聚分布的特点，且与经济规模和人口密度在空间上的均衡、配合程度较高。［结论］（1）山西省乡村休闲旅游地包括以太原和晋中为主的中部密集区和以临汾、运城、晋城为主的南部密集区，且都以各地级市为中心向周边扩展，大多数示范点分布于城郊地区； （2）经济发展、人口密度、资源禀赋、区位因素和社会因素是影响山西省乡村休闲旅游地分布的主要因素，其中农业科技类、农事体验类和特色村镇类多分布于经济和人口密度较高的地区。