Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

基于云模型的动态物流过程知识表示

以提高物流过程管理实时优化调度的智能性和科学性为突破口,引入云模型和知识工程的知识表示理论,提出一种基于云模型的物流过程的知识表示法。通过云模型的期望值、熵和超熵三个数字特征有效地表达定性概念,并实现定性和定量的不确定转换,从而描述具有动态特点和一定非确定性的物流过程知识。     

倾斜摄影测量中影响模型精度的因素分析

目前,倾斜摄影测量作为一种新技术,经过不断的探索、应用和发展,已开始在测绘行业中大显身手,并取得了不小的成绩。通过三维模型进行的矢量数据采集,其矢量精度完全依赖于三维模型精度。论文以实践经验为基础,定性地分析了在航空摄影、像控测量、模型生产三阶段中,影响模型精度的因素,希望对三维模型的生产起到一定的指导作用。     

功能点分析方法在建筑工程监督巡查项目开发中的应用

软件项目的度量是软件项目管理的一个重要问题。为在度量中取得合理、有效的结果,研究了功能点分析法在软件项目管理中的应用。首先介绍了功能点分析法的原理,然后结合一个具体的项目——建筑工程监督巡查系统,详细阐述了功能点分析法的运用过程。实践表明,取得了很好的效果。     

Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task

We conducted laboratory experiments for analyzing the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) relative to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) who were required to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While we did not find statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets did not outperform FTF for any of the questions and were inferior for three questions. The relative performances of nominal groups and FTF were mixed and the differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value relative to a simple average of the forecasts. In addition, we examined participants’ perceptions of the group and the group process. The participants rated personal communications more favorably than computer-mediated interactions. The group interactions in FTF and nominal groups were perceived as being highly cooperative and effective. Prediction markets were rated least favourably: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.     

A large factor model for forecasting macroeconomic variables in South Africa

This paper uses large Factor Models (FMs), which accommodate a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contain 267 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quarter-ahead out-of-sample forecasts from 2001Q1 to 2006Q4, indicate that the FMs tend to outperform alternative models such as an unrestricted VAR, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the three variables under consideration, hence indicating the blessings of dimensionality.     

The Delphi method, impeachment and terrorism: Accuracies of short-range forecasts for volatile world events

Two experiments examined the effects of iterated polling and feedback on prediction accuracy for volatile world events using the Delphi paradigm. In both experiments, participants predicted rapidly changing geopolitical events. The first experiment occurred during the litigation surrounding the Clinton/Lewinsky affair in 1998. Results indicated that feedback improved individual and group accuracy for predictions of whether an event would occur. These types of predictions derived from group consensus were approximately 90% accurate, which was significantly higher than the average individual participant's accuracy. Neither polling nor feedback had much effect on time course predictions. The second experiment occurred during the American military action against the Taliban regime in Afghanistan in 2001. In Experiment 2, participants were polled three times to determine if increasing the number of iterated polls would improve Delphi accuracy. The results showed that accuracy improved from the first to the second poll but not from the second to the third. The groups predicted whether these scenarios would occur with approximately 75% accuracy, which was significantly higher than the accuracy of any individual participant. Once again, the Delphi procedure did not enhance time course predictions in Experiment 2.     

云数据中心网络虚拟化技术的实现路径

当今社会,随着科学技术的迅猛发展,信息技术实现了持续快速发展。在大环境的影响下,云计算与网络虚拟化技术应运而生。该技术的产生和发展促使云数据中心网络的变革与发展成为新一轮技术革命的核心内容之一。当前,IT行业对数据中心网络的需求越来越大。基于此,论文论述了云数据中心网络虚拟化技术的实现路径。     

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。     

The coverage assignments of financial analysts

Previous studies document that forecast accuracy impacts analyst career outcomes. This paper investigates the influence of forecast accuracy on coverage assignments. I show that brokerage houses reward accurate analysts by assigning them to high-profile firms and penalise analysts exhibiting poor accuracy by assigning them to smaller firms. The coverage of high-profile firms increases the potential for future compensation linked to investment banking and trading commissions. In addition, covering such firms increases analysts' recognition from buy-side investors, which, in turn, increases the likelihood of obtaining broker votes and votes for the Institutional Investor star ranking. Overall, my results indicate that high forecast accuracy leads to increased future compensation.