Sentiment-scaled CAPM and market mispricing

This study explores the conditional version of the capital asset pricing model on sentiment to provide a behavioural intuition behind the value premium and market mispricing. We find betas (β) and the market risk premium to vary over time across different sentiment indices and portfolios. More importantly, the state β derived from this sentiment-scaled model provides a behavioural explanation of the value premium and a set of anomalies driven by mispricing. Different from the static β–return relation that gives a flat security market line, we document upward security market lines when plotting portfolio returns against their state βs and portfolios with higher state βs earn higher returns.     

长江经济带休闲渔业产业化发展潜力评价与分析

在界定休闲渔业产业化概念的基础上,从经济、社会、产业和支撑四个方面,创新性地选取26项指标构建休闲渔业产业化发展潜力评价指标体系,采用因子分析法和层次分析法对长江经济带11个省份休闲渔业产业化发展潜力进行科学评价。结果表明,长江经济带休闲渔业产业化发展潜力呈现东部沿海地区和中部的湖北相对领先,中部其余省份和西部地区相对落后的分布格局。进一步采用聚类分析法将长江经济带休闲渔业产业化发展潜力划分为四个等级,并分析不同等级地区存在的优势和不足,据此提出促进各地休闲渔业产业化发展的建议。     

组织场域视角下水利水电工程BIM技术扩散的影响因素 ——基于TOE框架的分析

在梳理国内外BIM采纳与扩散相关研究的基础上，从新制度主义理论出发，提出了水利水电工程组织场域并分析了其特征；进而运用技术-组织-环境(TOE)框架和匹配理论，分别讨论了水利水电工程组织场域BIM扩散的主要影响因素，形成一个整体的理论分析框架。分析表明，水利水电工程组织场域BIM扩散的影响因素主要包括:相对优势、兼容性和复杂性等技术因素，组织沟通、资源就绪度和高层领导支持等组织因素，以及制度环境、市场环境等环境因素。     

Analysis of Bitcoin prices using market and sentiment variables

This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.     

Factors influencing consumers' purchase decision-making in O2O business model: Evidence from consumers' overall evaluation

Online-to-offline (O2O) has become a rapidly growing e-commerce model worldwide, but the factors influencing consumers' purchase decision-making have not been examined well. After exploring the working mechanism of the O2O model, we extract eleven indexes of consumers' overall evaluation in the O2O model with Crawlzilla and R techniques. Social network analysis (SNA) is adopted to build social networks reflecting consumers' overall evaluation in the O2O model. Based on the modified social network and the extracted subgraph, this study (N = 768) reveals consumers' overall evaluation behavior patterns in the O2O model by calculating network density, central potential, edge betweenness. The results show that shoppers overall evaluate the eleven indexes, especially perceived product quality, online product price, promotion intensity, business reputation, and product brand, which reflects the significant associations between the indexes. Among all the eleven evaluation indexes, perceived product quality, online product price, and business reputation dominate consumers’ decision-making behavior. When making purchase decisions, consumers not only overall consider online product price, perceived product quality, and business reputation but also balance perceived product quality, business reputation, and promotion intensity. Finally, we make some suggestions on marketing strategy for e-commerce companies.     

Migration,population aging,and income inequality in China

In this study, we examine the effects of four demographic factors, namely, birth, death, natural aging, and net migration, on population aging and income inequality in China. We use the official Chinese data and the China Household Income Project Survey data for the 2007–2013 period and apply a decomposition model based on the Shapley method. Unlike previous studies, we include migration in our decomposition model and find that natural aging is the primary factor contributing to population aging in both urban and rural areas. Further, migration may accelerate population aging in rural areas. Moreover, migration contributes to reducing income inequality in urban areas, while widening income inequality in rural areas. The effect of migration is larger than those of birth, death, and natural aging on income inequality. The robustness checks confirm these conclusions.     

Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.     

A note on the long rate in factor models of the term structure

In this paper, we consider factor models of the term structure based on a Brownian filtration. We show that the existence of a nondeterministic long rate in a factor model of the term structure implies, as a consequence of the Dybvig–Ingersoll–Ross theorem, that the model has an equivalent representation in which one of the state variables is nondecreasing. For two‐dimensional factor models, we prove moreover that if the long rate is nondeterministic, the yield curve flattens out, and the factor process is asymptotically nondeterministic, then the term structure is unbounded. Finally, we provide an explicit example of a three‐dimensional affine factor model with a nondeterministic yet finite long rate in which the volatility of the factor process does not vanish over time.     

Understanding precedents for frontline employee turnover in luxury hotels: Emotional intelligence as a unifying factor

Current turnover research fails to serve the needs of an industry that is long plagued by employee turnover. Existing literature focuses more on evaluating bundles of human resource practices and fail to provide precise and clear guidance for practitioners. This study proposes that emotional intelligence (EI) unifies sufficient individual factors and organizational factors that affect employee turnover and serves as a single significant precedent for turnover. Data were collected from frontline employees at eight luxury hotels. The direct, indirect, and total impacts of employee EI on employee turnover were tested by structural equation modeling and bootstrap tests. The results suggest that EI has significant indirect impacts through the mediation of perceived organizational support, pay satisfaction and job burnout, and significant total impacts on turnover. Implication suggestions include integrating EI into the recruiting process for new employees and providing training opportunities for current employees to improve their EI.     

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.