Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

Is there a diversification “cost” of Shari’ah compliance? Empirical evidence from Malaysian equities

Islamic equity portfolios work with a smaller investment universe given the filtering of non-Shari’ah compliant stocks. It has been theoretically argued that this culminates in suboptimal portfolio diversification, which in turn adversely affects risk-adjusted returns. We offer empirical evidence that such a conceived portfolio diversification “penalty” is far from a foregone conclusion, at least empirically. Our results tend to indicate that Islamic portfolios are not invariably handicapped in terms of portfolio diversification. We also explored dimensions that may account for differences in the relative investment performance between Islamic and conventional portfolios, such as portfolio constraints, short selling and market conditions. We believe this paper is among the first to apply substantial empirical analysis specifically with respect to the portfolio diversification perspective on Islamic equity investments.     

A supply-demand model of public sector size

We develop a supply-demand model for the public sector with a political equilibrium. The model considers the inefficiencies caused by taxes and includes costs associated with the provision of public goods to consumers. We show that the size of the public sector may depend on the median voter's income, population size, costs associated with paying tax, and quality of institutions, all of which reflect the costs of provisioning public goods. The estimates for the Organisation for Economic Co-operation and Development member countries are compatible with theoretical predictions; however, they do not confirm Wagner's law, which holds that the public sector share does not grow with an increase in income. A greater dependency ratio and the Gini coefficient increase demand for redistribution policies. Greater government effectiveness is a supply-side factor that increases the public sector's share in an economy.     

Assessment and selection of management consultants: A comparative cognitive study between small- and large-scale companies

This paper explores alternative ways of evaluating and ultimately hiring a generalist consultancy for short- and medium-term projects, and how this is shaped by the size of the clients’ company. What buying factors do managers of small and large firms have in mind when hiring consultants for their projects?Instead of employing surveys incorporating predefined buying factors based on what the researchers think would be important, a cognitive mapping methodology - the repertory grid technique - is applied to investigate the underlying phenomena. Using the repertory grid technique to draw mental models is a novel research technique within the supply management field. Recent behavioral supply chain management research found the behavior of managers to be critically influenced by mental models: the lenses through which managers perceive, simplify and interpret the world.Results show differences between the buying factors used in small- and large-scale companies: managers of large companies are more ‘reputation and outcome-oriented’ and managers of small businesses are more concerned with ‘how the services are rendered’. However, both constituents stressed the importance of expertise, quality and the relationship between the client and the management consultancy, although the character of such a relationship differed between small- and large-scale firms. Results also point out criteria that managers of small and large companies employ to discriminate between good and poor performing consultancies and to judge service delivery. The practical implications for both companies hiring consultancies and the consultancy companies are discussed. For consultancies, our findings can help tailoring their efforts to a differing clientele when marketing and selling their services. For companies hiring consultancies, fixating too much on particular buying factors may lead to biased decisions.     

论企业核心人才的界定与管理

现代企业间的竞争实质上是人才的竞争,特别是核心人才的竞争。企业发展中核心人才是促进企业发展和创新的动力,核心人才的挖掘和管理直接影响企业的命脉,因此,企业必须要加强对核心人才的界定和管理,明确哪些人才为核心人才,并采取针对性的管理方式,才能促进企业的健康发展。论文主要对企业核心人才的界定进行分析,并探究核心人才的管理策略。     

基于云计算与物联网技术的数据挖掘

在科学技术快速发展的今天,信息通信行业已经与我们的日常生活完全融为一体,并且推动了整个社会的发展,所以在科学技术快速进步的过程中,信息通信相关技术也在不断发展,其中,基于云计算的物联网发展越来越受到人们的重视。论文对基于云计算的物联网数据挖掘模式进行分析及探讨,从而为互联网数据挖掘技术发展提供相应的参考。     

试论大数据技术在生态环境保护领域的应用架构及相关技术

促进大数据在生态环境保护领域的深化应用,可以提高生态环境监管能力,并实现环境管理工作精细化。实践中需要把握生态环境信息化的大数据需求,对其应用架构及相关技术进行深入探究,以从多维度出发挖掘有价值的信息,更好地服务生态环境保护工作。     

关于铁路单线隧道接触网弓形腕臂预配平台的相关研究

为降低铁路单线隧道接触网弓形腕臂预配施工难度,论文研究了一种接触网弓形腕臂预配平台,结合单线隧道内弓形腕臂数据精测技术及计算软件,精确测量预配,保证弓形腕臂的安装精度和质量,使弓形腕臂在现场施工中一次成优。论文结合实际,就某铁路单线隧道接触网弓形腕臂预配平台作具体分析。     

执法监测中正确使用环境标准初探

经过近四十年的发展和完善,我国的环境标准已形成了“两级五类”的完整体系。基层监测人员在执法监测中对环境标准的选择,要结合环境影响批复和污染工艺、排放去向进行综合考虑。在排污许可证上载明的污染物排放浓度与排放标准中规定的浓度限值不一致的情况下,应慎重选取排放标准规定的浓度限值。使用环境标准的基本原则是地方标准优先于国家标准,行业标准优先于综合标准,新标准优先于旧标准,地方政府文件不具有标准效力。