A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.     

Delegation and the regulation of U.S. financial markets

We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress’ decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.     

The effect of control deprivation on consumers’ adoption of no-pain,no-gain principle

Consumers often base their judgments on a no-pain, no-gain principle—that is, one must pay a cost in order to achieve a beneficial outcome. For example, they infer the quality of a product from its price and judge a bad-tasting medicine to be more effective than a tasty one. Although the use of this principle to infer the value of a product or service has been observed in several domains, the processes that underlie its use have not been fully explored. We find that when people feel out of control, they tend to use the principle because it exemplifies a causal relationship between actions and outcomes and endorsing it reaffirms their belief that they have control over the outcomes of their behavior. Our findings have implications for how marketers might position products and services to attract consumers who perceive themselves as having different levels of control.     

金融周期、行业技术周期与经济结构优化

本文首先采用马尔科夫区制转移模型测算金融周期和12类行业的技术投入周期,进而采用交叉谱分析法和OLS法分析金融周期对行业技术周期的影响,结果发现:不同金融变量的周期具有异步性,各行业的技术投入周期具有明显差别;不同金融变量周期与行业技术投入周期之间存在不同的数量关联性,对不同的行业技术投入具有不同的影响作用;在繁荣阶段,金融市场和银行对各行业的技术投入都具有正向推动作用,但金融市场对高密度创新行业技术投入的推动作用更为显著,银行对低密度稳定行业技术投入的推动作用更为显著;在紧缩阶段,金融市场对高密度创新行业技术投入的紧缩效应更为剧烈,银行却对低密度稳定行业的技术投资下行起到缓解作用。根据这些事实和规律,可以引导利用各金融变量对具有发展前景的行业强化技术投资助推作用,提升经济结构优化效率。     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Institutional Economics as Theory of Policy Change: Impact of Past Policy Failures on Present Policy

Social scientists have developed several theories for understanding or evaluating policy change over time. Since all costs or benefits are not internalized owing to positive transaction costs, policymaking is always implemented under cost underestimation conditions and, therefore, is imperfect. I call this trait policy failure in this article. Furthermore, I show that a new framework combining the social costs approach and the legal/economic approach in institutional economics is suitable and can be applied to evaluating how past policy failures affect present policy, providing as an example the Fukushima nuclear disaster in Japan.     

基于产业背景的金融控股集团协同机制研究

产业金控集团是当前我国产业资本借力资本市场发展实体经济的重要方式，但在 实际运行中存在协同机制不健全、协同效应发挥不足、产融结合的深度和广度不足等问题。本 文对国内外产业金融理论进行了文献综述，深入分析了产业金控集团产生的动因，指出了目前 产业金控集团在发挥协同效应中存在的不足。结合产业金控集团的优势和特点，从构建横向协 同机制和纵向协同机制的角度，探讨了完善产业金控集团协同机制的举措。     

Central bank disclosure as a macroprudential tool for financial stability

The Basel Capital Accord (pillar 3) states that disclosure of information (transparency) is essential to financial stability. This study analyzes, through inflation reports, the disclosure of information from the Central Bank of Brazil concerning the credit market. We consider credit risk and capital buffers as measures of financial stability in this analysis. Furthermore, in order to measure the perception of the monetary authority on the credit market, we built two indices based on the central bank’s communication on credit development. We performed a panel data analysis based on a sample of 125 banks for the period from June 1999 to September 2014 (7000 observations). The findings suggest that central bank communication regarding expectations concerning the credit market contributes to financial stability. Therefore, this kind of communication of central banks (about credit development) may constitute an important macroprudential tool to improve financial stability.     

就业结构调整对水库移民土地流转的影响研究*——以南水北调中线工程移民为例

[目的]为促进水库移民的生计恢复及转型,探寻就业结构和土地流转间的交互机制,分析两者间耦合作用对当前移民安置模式及政策的影响。[方法]文章从移民就业结构变迁出发,通过超边际理论构建了安置区移民土地的流转机制模型,推演了从业决策对土地流转行为的影响路径,并运用南水北调农业安置移民数据进行实证检验。[结果]大面积转出土地是移民搬迁后土地处置的主要行为,移民中土地转入户有所增加,但土地集中度却有所减少。移民非农就业较搬迁前有明显上升,并有向当地兼业转移的趋势,但与土地流转间并不互为因果,农村社保还难以弱化其对土地养老的依赖。劳动力数量、非农就业时间、非农收入占比、土地政策等对移民土地流转行为和规模均有显著影响,而外出就业前景和地租水平在促进土地流转行为的同时,却不一定能增加土地流转面积。[结论]移民外迁中可以采用无土安置代替农业安置,推动移民土地流转的关键在于技能培训、政策保障和地租补贴,简单的就业推荐和土地权属划分的作用有限。而对于移民中的农业大户,则需在满足土地转入面积的基础上,保障其在安置区的各项土地权益。