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。     

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.     

Combining forecasts: some results on exchange and interest rates

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and chaotic dynamics); (b) combining the forecasts provided by the different methods. This work has also the aim of revising a short-term econometric forecast using a longer-term forecast. The revision process usually runs the opposite way (revision is made on a longer-term forecast using a short-term one to reflect the current available information, but it is not excluded that it is possible to proceed as described above. Daily data from the financial market is used. Some empirical applications on exchange and interest rates are given.     

Excel与SPSS在线性回归预测中的比较分析

线性回归分析是经济管理与预测的一个重要的量化分析方法。分别运用Excel与SPSS对同一实际问题进行线性回归分析,得出两种软件在线性回归预测问题上各有其优势,在经济管理的实际应用中无论选择那种软件都要了解其功能和操作的特点,有助于对实际问题恰当而且正确地分析与预测。     

THE IMPACT OF POINT‐OF‐SALE DATA INACCURACY AND INVENTORY RECORD DATA ERRORS

The presence or absence of error in point‐of‐sale (POS) data and inventory system records directly affects retailer performance. This study identifies various error sources in retail supply chains and studies the influence of inventory and POS (demand) errors in a simulated retail outlet according to fill rate and average inventory. Other things being equal, we find that inventory record error reduces fill rate more than demand error. This study adds further evidence to other studies that suggest the costs caused by errors in POS systems may be overstated.     

A review of Delphi forecasting research in tourism

The Delphi technique is the most popular judgemental forecasting method in tourism studies, but theoretical and empirical developments in this area (especially for forecasting purposes) have been slow. This paper analyses published research on Delphi forecasting in tourism and hospitality, explores how the Delphi forecasting method has progressed over the past four decades in terms of topical areas, empirical applications, and issues of reliability and validity, and is thus expected to advance understanding of the Delphi technique, providing topical and methodological recommendations for researchers and industry practitioners for producing accurate forecasts. The literature concerning the qualitative and quantitative applications of Delphi forecasting in tourism is mainly divided into three research themes: event forecasting, forecasting tourism demand, and forecasting future trends/market conditions (the most popular application). Issues of accuracy, reliability, and validity, as well as a group of Delphi-specific characteristics, such as panel size, panel selection, consensus measures, and analysis of results, are summarized and discussed. This study also examines the accuracy of Delphi forecasts as well as exploring the role of the Delphi approach in integrating human judgement into quantitative forecasts.     

Does the financial analysts' usage of non-financial information influence the analysts' forecast accuracy? Some evidence from the Belgian sell-side financial analyst

This paper examines whether the use of non-financial information by sell-side financial analysts influences the accuracy of analysts’ forecasts. The research findings, based on a survey of Belgian financial analysts, suggest that financial analysts who use more forward-looking information and more internal-structure information offer more accurate forecasts. Furthermore, the listed Belgian firms examined in this study have improved their non-financial information reporting over time. However, neither the frequency nor the quantity of non-financial information mentioned by financial analysts in their reports appears to have increased over time.     

Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework

This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.