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.     

我国省域农业隐含碳排放及其驱动因素时空动态分析

[目的]我国区域农业发展模式由于气候、环境的不同存在较大的差异,而目前针对农业隐含碳的时空动态研究较为缺乏,导致对于区域减排目标的设定缺乏全面的考虑,无法实现农业碳减排的效率性和公平性。因此,有必要分析农业隐含碳排放在不同省域的特征并分析其驱动因素,为制定体现地区间公平性且有效率的碳减排政策提供依据。[方法]文章利用2002年、2007年和2012年3年的投入产出表估算我国各省(市、区)农业隐含碳排放量,根据Kaya恒等关系将其分解为经济规模、经济结构、一般农业技术进步和低碳农业技术进步4类影响因素,并利用LMDI分解分析法对这4类影响因素的驱动力进行了分析。[结果]2002—2012年我国大部分地区农业隐含碳排放量呈上升趋势,空间上呈现从西到东、从南到北逐渐增加的分布规律,经济规模效应在各省份均呈正向驱动,且在经济发展较为迅速、经济增长后劲较强的地区驱动效应逐渐增强;经济结构效应在大部分省份呈负向驱动,且在重型工业的聚集区域负向效应逐渐增强;一般农业技术因素正向驱动区域逐渐扩散,且在农业大省正向驱动效应逐渐增强;低碳农业技术进步因素在东部发达地区负向的驱动效应较强,在西部驱动效应较弱。[结论]在未来的农业减排政策制定过程中,需要充分考虑不同地区的经济发展、产业结构、农业生产等特点。     

后危机视野下福建省经济格局演变及其影响机制

［目的］后危机时代是目前我国各地区经济发展面对的一个巨大变量，也是近年来区域经济发展关注的焦点话题。［方法］选取福建省县域经济的5项指标作为解释变量，利用探索性空间分析方法和地理加权回归模型，对2010—2015年福建省县域经济格局及其驱动机制进行探讨。［结果］（1）总体上看，福建省经济空间格局变化较小，呈现东南高西北低的态势，重心向福州、莆田地区转移，省内发展均质化与极化现象并存；（2）产业结构调整与城镇化水平提升主导省内经济重心在东西方向上的移动，两者的空间格局具有相似性；（3）创新投入与金融零售业的变化主导了经济重心南北向的移动，其与各市县的经济基础和产业调整政策密切相关。［结论］在后危机时代福建省需根据市场特征推进区域协同发展、实现产业结构调整、促进区域共同富裕； 地方经济政策应与自身经济发展特征相适应，实现区域经济可持续发展。     

山东省生态用地变化及驱动因素影响评价

［目的］研究第二次土地利用现状调查以来山东省生态用地变化特点及驱动因素，评价各类驱动因素对生态用地的影响程度，为科学编制区域空间规划，保护和合理利用生态用地提供参考。［方法］文章构建了全省生态用地转移矩阵，归纳了影响生态因素变化的主要驱动因素，提出了驱动力指数计算方法，定量分析和比对了各驱动因素对生态用地的影响。［结果］2009—2017年生态用地共计转出2048万hm2，占生态用地总面积的472%； 转入316万hm2，占生态用地总面积的072%。8年来全省生态用地净减少1732万hm2，占2009年生态用地总面积的400%。生态用地的流向以耕地最多，向城镇村及工矿用地流出次之，向设施农用地的流出也有较大面积。［结论］（1）在影响生态用地变化的六大驱动因素中，耕地开发和城镇村工矿占用的驱动力指数最高，非耕农业占用对生态用地的变化有一定影响。（2）六大因素中城镇村工矿占用对当地生态环境的破坏力最强。     

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.