Portfolio managers' and novices' forecasts of risk and return: are there predictable forecast errors?

This study aims to investigate the individual behaviour that underlies the overreaction hypothesis by conducting a controlled experiment. Two areas that were not captured by previous research on the validity of the overreaction hypothesis are investigated. First, actual portfolio managers are employed as forecasters. Second a real‐world assessment task is given in the form of predicting the prices of stocks traded on the exchange on a real time basis. The purpose is to explore return expectations and risk perceptions of portfolio managers as well as financially unsophisticated investors by using point and interval forecasts provided for different forecast horizons in bull and bear markets. Contributions stem from three sources. (1) The use of financially sophisticated subjects for the first time in an experimental framework testing the overreaction hypothesis makes possible to control for the effect of expertise. (2) The use of different forecast horizons controls for the effect of forecast period. (3) The use of real‐time forecasts of specific stocks traded at the stock exchange, for the first time in an experimental framework testing the overreaction hypothesis enables to control for ecological validity. Discussions will be given as to the portfolio managers' versus naive investors' interpolating asset prices from past trends and hedging behaviour, due to their caution in projections of ranges for future prices. Copyright © 2002 John Wiley & Sons, Ltd.     

A non-linear combination of experts' forecasts: A Bayesian approach

A general Bayesian approach to combining n expert forecasts is developed. Under some moderate assumptions on the distributions of the expert errors, it leads to a consistent, monotonic, quasi-linear average formula. This generalizes Bordley's results.     

Collinearity and the use of latent root regression for combining GNP forecasts

In combining economic forecasts a problem often faced is that the individual forecasts display some degree of dependence. We discuss latent root regression for combining collinear GNP forecasts. Our results indicate that latent root regression produces more efficient combining weight estimates (regression parameter estimates) than ordinary least squares estimation (OLS), although out-of-sample forecasting performance is comparable to OLS.     

Unbiasedness,efficiency and the combination of economic forecasts

This paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.     

The combining of forecasts using recursive techniques with non-stationary weights

This paper evaluates six optimal and four ad hoc recursive combination methods on five actual data sets. The performance of all methods is compared to the mean and recursive least squares. A modification to one method is proposed and evaluated. The recursive methods were found to be very effective from start-up on two of the data sets. Where the optimal methods worked well so did the ad hoc ones, suggesting that often combination methods allowing ‘local bias’ adjustment may be preferable to the mean forecast and comparable to the optimal methods.     

Optimal forecast intervals under asymmetric loss

An optimality criterion for forecast intervals under asymmetric loss functions is proposed. A loss optimal forecast interval is obtained by requiring that the expected loss, conditional on a future realization within the desired interval, be minimal. The main difficulty in the context of forecasting under asymmetric loss emerges when there is no knowledge about the distribution of the innovations. For solving this problem, an extension of estimation under the relevant loss function is suggested. In many cases, one also needs to account for the additional variability due to estimation of model parameters. Another solution, based on the bootstrap, works for both problems. Copyright © 2007 John Wiley & Sons, Ltd.     

基于PSoC的制冷设备稳压研究

PSoC(System-on-a-Programmable-chip)可编程片上系统,它能把传感器件与电路、信号处理电路、接口电路、功率器件和电路等集成在一个硅芯片上,使其具有按照负载要求精密调节输出和按照过热、过压、过流等情况自我进行保护的智能功能,其优越性不言而喻。     

建立普惠型农村金融 助推农户经济发展

发展农户经济有利于农村就业,促进农民增收和地区经济发展,化解社会矛盾。近年来涉农贷款不断增加,但仍不能满足农户经济和农村经济发展的需要。我们认为,应强化现有正规金融的支农功能,规范推进多元化的新型农村金融,完善农村金融的财税支持和补贴政策以降低农户贷款负担,建立政策性保险和担保机构,以降低涉农信贷风险,推动农村经济的健康发展。     

基于多模式预报优选融合技术的短时定量降水预报

为提升短时定量降水预报准确率,使其更好地支撑城市暴雨洪涝及中小河流洪水预报,基于多源数值天气模式预报值,构建了基于预报成员优选融合的短时(0~12h)逐小时定量降水预报模型,模型由单模式百分位映射订正技术模块、模式订正预报成员实时优选技术模块、预报成员实时融合技术模块和融合预报的百分位映射订正技术模块4个技术模块组成。以河南省为例对模型进行检验,结果表明:模型预报降水分布和强度随着预报时效临近更接近实况降水;模型提前6h预报出郑州“7·20”暴雨最强时段降水的落区和强度;在2021年1—9月降水预报对比检验中,模型预报准确率高于任意单模式订正预报和原始模式预报,且准确率随着预报时效的临近逐步提高。