When do sell-side analysts switch industries they follow?

Between 1984 and 2014 over 3400 sell-side analysts changed the primary industry they followed. This article documents that analysts are more likely to change their industries when their absolute and relative forecasting accuracy in that industry is low and when the accuracy in the new industry is high. Analysts are more likely to switch industries at the beginning of their careers, after a recent change of an employing brokerage house, and if they have a history of switching industries before. Analysts are less likely to make a switch when their forecasting activity in the industry is high, when the industry is followed by many analysts and when they are employed by a top brokerage house.     

The problem of employees' network centrality and supervisors' error in performance appraisal: A multilevel theory

This article reveals an unexplored paradox for HR managers: the centrality of an employee in the social network benefits performance but hampers performance appraisal because it affects supervisors' rating errors. Central employees can be erroneously rated high on performance even when they are not high performers because supervisors tend to overappraise their performance. A distinction is made between rating precision, which depends on supervisors' uncertainty regarding employees' performance, and rating accuracy, which depends on supervisors' bias in favor of employees. Employee centrality is posited to be beneficial to precision but deleterious to accuracy because it regulates the diffusion of positive information, status, and power, all of which distort supervisors' capacity and motivation to accurately appraise performance. It is then argued that rating errors caused by network centrality affect aggregate perceptions of justice in organizations. When employees are highly connected to each other in a dense network, organizations have a strong and positive justice climate. Yet when some employees are more central than others in a centralized network, organizations have a negative and weak justice climate. The article contributes to the literature because it identifies an unexplored dark side of network centrality and offers recommendations for HR managers to cope with its deleterious consequences and for scholars to study them.     

好的表演需要好的观众——顾客察觉准确性在员工情绪劳动影响中的作用

基于385对顾客和员工的问卷调查的配对数据，考察了员工的情绪劳动如何影响顾客的信任和忠诚，以及顾客察觉准确性在其中的调节作用。研究结果表明员工的深层表演会促进顾客的信任，进而促进顾客忠诚；顾客对深层扮演的高察觉准确性会加强员工的深层扮演与顾客信任之间的正向关系，而当顾客对表面扮演察觉准确性低时，员工的表面扮演会对顾客信任有正向的影响；顾客的深层/表面扮演察觉准确性加强了深层/表面扮演、顾客信任和忠诚之间的中介效应。研究帮助从情绪的视角更好地理解服务情境中信任的前因变量，发现顾客的察觉准确性在情绪劳动影响过程中的重要作用，有助于服务企业从情绪劳动的角度来建立顾客信任和忠诚。     

信息分享对原始设备制造商与其合同制造商竞争的影响

研究了原始设备制造商的预测信息分享对一个原始设备制造商和一个与其同时有合作和竞争的合同制造商组成的供应链系统的影响,建立制造商间信息分享的模型,该模型包括一个原始设备制造商和一个合同制造商。研究发现,原始设备制造商关于市场潜在需求预测信息的分享对其预期利润是不利的,同时需求信息预测的精度对原始设备制造商信息分享的决策也有影响,原始设备制造商没有动机与其供应链成员进行信息分享,但信息分享使得供应链整体利润增加。最后,建立一个信息分享补偿机制分享供应链利润的增加量,以期通过信息分享补偿机制促使原始设备制造商有动机进行信息分享,从而实现其与合同制造商的“双赢”。     

工作用玻璃液体温度计检定注意事项

在温度计的检定过程中,温度计插入恒温槽中不垂直、读数不准确等因素都将给检定结果带来较大的误差,从6个方面讲述了检定过程中需注意的事项,并提出了相应的解决方法,从而减小了温度计的测量误差,提高了测量准确度。     

提高关口电能计量装置准确度技术分析

随着电力体制的改革和电力工业的发展,发电企业用于结算电量的电能表由发电机前移至主变出口最终前移至线路出口关口电能表,就要求关口电能计量装置的准确度愈来愈高。本文针对大唐珲春发电厂220千伏系统二期关口电能计量装置误差产生的原因进行分析,发现影响关口电能表准确度的各项因素,制定解决方案逐步进行实施,消除这些不利因素对关口电能表的影响,提高关口电能计量装置准确度,希望能起到借鉴作用。     

Are analysts' cash flow forecasts useful?

This study provides evidence that the cost of equity capital decreases with the number of analysts who issue both cash flow and earnings forecasts (cash analysts). The evidence also shows that cash analysts reduce information asymmetry and predict long‐term earnings more accurately than analysts who issue only earnings forecasts. Taken together, these findings suggest that cash analysts provide market participants with high‐quality information and, as a result, firms benefit from cash analyst coverage in the form of a reduced cost of equity capital.     

行业因素对综合预警模型的判别效果的影响研究--以基于主成分分析的Logit模型为例

以2010～2012年期间我国沪深A股因财务困境陷入ST的公司和按照1∶2比例配比的正常公司作为研究对象,并根据行业特点对样本进行划分。同时,选取能够反映企业盈利能力、股东获利能力、现金流量能力、营运能力、发展能力、偿债能力的30个财务指标,在主成分分析的基础上构建各年度的Logit模型,对各行业的违约概率和判别准确度分别进行分析。结果表明,不同行业的违约概率和判别准确度均存在显著差异且存在共性特征。     

变电设备红外线热成像诊断技术的准确性分析

红外诊断技术是一种新型诊断技术,为变电设备的热诊断提供了很好的诊断方式,该种诊断方式有着不解体、直观性、快速性、准确性的特征,也适宜用在大面积检测中,同时,该种诊断技术也能够与其他检测方式联合使用,定位故障的具体位置,这就可以有效提升检修的便利性。但是,影响红外线热成像诊断技术准确率的因素是多种多样的,在进行诊断时,必须要合理地控制其测量环境与测量方式。本文主要分析影响红外线热成像诊断技术准确性的因素,并提出相关的解决措施。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.