分析师的盈利预测偏差与本地优势

分析师在金融市场中起着重要的信息中介作用,其信息来源及获取方式会直接影响信息处理的质量。本文从分析师对企业年度盈利预测的偏差出发,基于对分析师与公司地域关系的实证研究,发现分析师在预测那些所在地与分析师隶属的机构同属一个省份的公司时,预测结果更加准确,这种本地优势与公司高成长性及公司国有性质显著相关。研究还发现,我国分析师的本地优势与个人本身的乐观性并无关系,可能更多与信息优势相关。     

铁路专用线噪声调查与预测特点分析

铁路专用线与国铁相比具有线路长度较短、技术标准较低、运输密度较小等特点。在分析铁路专用线噪声特点的基础上,提出铁路专用线应选取等效声级为噪声评价量,在采用模式预测法进行铁路专用线噪声预测时,应关注各主要修正参数,为科学、客观评价专用线噪声提供依据,同时以某铁路专用线为例进行噪声分析。     

中国银行业市场化改革的五个阶段及特点

改革开放以来,中国经济社会转型不仅是中国银行业改革的大背景,也是银行制度市场化演进的推动力。与此相适应,中国银行制度受强制性制度变迁的影响不断缩小,而受市场主导下的诱致性制度变迁的影响不断扩大。银行体制、运行机制、市场结构的市场化变迁过程呈现五个不同的阶段性特点,经历了一个从计划经济体制下的银行制度到基本符合市场经济要求的现代银行制度转变的过程。     

股票价格波动的信息渠道影响研究——基于媒体报道和分析师预测的视角

文章手工收集、整理了2006-2015年中国A股上市公司的媒体报道倾向数据,实证研究了媒体报道正向倾向与分析师乐观预测偏差对上市公司负收益偏态系数的影响。研究表明,虽然媒体报道正向倾向、分析师乐观预测均分别对上市公司股价负收益偏态系数产生显著的负向影响,但是两者的交互项对股价负收益偏态系数的影响则是显著为正,即当媒体报道正面倾向与分析师乐观预测两者共同作用时,将会触发"信息过度关注偏差"机制,从而降低了上市公司股票收益。文章研究还发现,媒体报道、分析师预测和"信息过度关注偏差"机制的作用强度,在市场化程度高地区要明显大于市场化程度低地区,可能的原因是市场化程度高地区的企业更加容易被媒体报道和分析师关注,进而产生更强的关注效应。     

失业风险预警系统研究

基于我国29年的历史数据,采用BP神经网络模型对我国的失业风险预警问题进行研究。结果表明,神经网络方法在失业风险系统中具有优良的预警效果,其对失业风险综合警情值的预测误差小于3％。相对于景气分析预测法、时间序列分析、灰色预测模型以及回归预测模型等技术,神经网络方法不仅具有良好的预测精度,同时还具备较强的容错能力和泛化能力。因此,在构建我国的失业风险预警系统中,神经网络模型应该是一种被优先考虑的方法。     

通货膨胀实时预测及菲利普斯曲线的适用性

本文从实时分析的视角,基于多种退势方法的产出缺口最终估计、准最终估计和实时估计序列,分别构建了四类预测模型对我国通货膨胀率进行预测,分析了产出缺口修正效应和滞后阶数变化效应对通胀预测的影响,并进一步考察了产出缺口在通胀预测中的作用及菲利普斯曲线在通胀预测中的适用性。研究结论表明,通胀率的实时预测效果要明显比基于最终数据的差,其中滞后阶数变化效应对实时预测精度的影响大于产出缺口修正效应;尤为重要的是,尽管在最终数据的预测分析中,产出缺口的引入能够提高通胀率的预测精度,但是在实时预测中,产出缺口没有提供有价值的信息,因此"产出—通胀"型菲利普斯曲线在我国通胀实时预测中并不适用。     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

投资额与GDP和财政收入的回归分析及预测

文章首先运用MATLAB工具对1991-2008年我国GDP、财政收入与投资额等数据进行回归分析,得出固定资产投资与GDP及财政收入的回归模型。其次,分别建立年份对GDP与财政收入的拟合曲线,预测出2009——2010年的GDP与财政收入值,进而推出未来两年的投资额预测值。     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.