Feedforward neural networks in the classification of financial information

Financial research has given rise to numerous studies in which, on the basis of the information provided by financial statements, companies are classified into different groups. An example is that of the classification of companies into those that are solvent and those that are insolvent. Linear discriminant analysis (LDA) and logistic regression have been the most commonly used statistical models in this type of work. One feedforward neural network, known as the multilayer perceptron (MLP), performs the same task as LDA and logistic regression which, a priori, makes it appropriate for the treatment of financial information. In this paper, a practical case based on data from Spanish companies, shows, in an empirical form, the strengths and weaknesses of feedforward neural networks. The desirability of carrying out an exploratory data analysis of the financial ratios in order to study their statistical properties, with the aim of achieving an appropriate model selection, is made clear.     

2012年-2013年新疆经济增长分析与预测

2012年前三季度,在全球经济低迷、我国经济增速下行的严峻形势下,新疆经济保持了平稳较快增长。初步预计,前三季度全疆经济增长11.5%,同比加快0.4个百分点,快于全国3.8个百分点。新疆经济增长纵向比成绩斐然,横向比不够快,在全国和西部地区的位次不断后移。其主要原因是产业结构不合理,即经济持续快速增长缺少产业支撑。本文通过对新疆2012年前三季度经济运行情况的分析,预计2012年经济增长可达12%,2013年增长13%左右。2013年必须把保增长放在更加重要的位置,加强对经济运行的分析与把握,在抓好投资、工业快速增长的同时,加快结构调整和转型,全力保持经济平稳较快增长。     

Can Losses Caused by Wind Storms be Predicted from Meteorological Observations?

This paper contains a study of the extent to which aggregate losses due to severe wind storms can be explained by wind measurements. The analysis is based on 12 years of data for a region, Ska § ne, in southern Sweden. A previous investigation indicated that wind measurements from six recording stations in Ska § ne was insufficient to obtain accurate prediction. The present study instead uses geostrophic winds calculated from pressure readings, at a regular grid of size 50 kilometres over Ska § ne. However, also this meteorological data set is seen to be insufficient for accurate prediction of insurance risk. The results indicate that currently popular methods of evaluating wind storm risks from meteorological data should not be used uncritically by insurers or reinsurers. Nevertheless, wind data does contain some information on insurance. risks. There is a need for further research on how to use this information to improve risk assessment.     

On the distribution of discounted loss reserves using generalized linear models

Renshaw and Verrall [] specified the generalized linear model (GLM) underlying the chain-ladder technique and suggested some other GLMs which might be useful in claims reserving. The purpose of this paper is to construct bounds for the discounted loss reserve within the framework of GLMs. Exact calculation of the distribution of the total reserve is not feasible, and hence the determination of lower and upper bounds with a simpler structure is a possible way out. The paper ends with numerical examples illustrating the usefulness of the presented approximations.     

How to motivate people to put their money where their mouth is: What makes employees participate in electronic prediction markets?

Although prediction markets are widely acknowledged to be as effective as other institutions predicting future events, little is known regarding their application in organizational settings. For organizations, prediction markets offer a new technical possibility to make use of the beliefs, information and knowledge of their employees for organizational decisions and forecasts. Based on survey data of 147 users in German-speaking Europe, we show that expected rewards increase user satisfaction. While contribution effort and general reciprocity in isolation have no impact on satisfaction, perceived general reciprocity in conjunction with high rewards is likely to increase user satisfaction. Our findings extend our knowledge on user behavior and emphasize the importance of incentives in prediction markets.     

Group-based forecasting?: A social psychological analysis

The potential of group (vs. individual) forecasting is analyzed from the perspective of the social psychology of groups. The social decision scheme theory (SDST) is summarized, and several simulations are presented to demonstrate the dependence of group aggregation accuracy upon factors such as group size, the accuracy and distribution of individual forecasts, and shared representations of the forecasting problem. Many advantages and disadvantages of group aggregation are identified and related to four generic methods of group aggregation (statistical aggregation, prediction markets, the Delphi method, and face-to-face discussion). A number of aspects of forecasting problems are identified which should govern whether or not group forecasting can be relied upon, and if so, what aggregation method should be used.     

广佛同城对佛山房地产业的影响研究

应用房地产增加值、房地产投资额、自然空置率、房价收入比等指标对佛山房地产业进行研究,并运用GM(1,1)模型对房价进行预测。根据房地产业与区域经济的关系,探索广佛同城对佛山房地产的影响,发现佛山房地产业的发展空间大、投资合理、需求稍大于供求、房价稍高的状况。结果表明佛山房地产业在受广佛同城化的影响下发展是比较健康的,同时对研究发现的问题提出建议。     

基于AIW-PSO小波神经网络的上证指数预测

针对小波神经网络(Wavelet Neural Network,WNN)的学习算法的不足,采用一种自适应惯性权重粒子群优化算法(Adaptive Inertia Weight Particle Swarm Optimization,AIW-PSO)作为小波神经网络的学习算法,建立AIW-PSO小波神经网络模型对上证指数进行预测,并将预测结果传统小波神经网络模型比较。结果表明,AIW-PSO小波神经网络模型对上证指数具有更好的预测效果。     

Evaluating alternative models of trend inflation

With the concept of trend inflation now being widely understood to be important to the accuracy of longer-term inflation forecasts, this paper assesses alternative models of trend inflation. Reflecting the models which are common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation that incorporate different trend specifications. We compare the models on the basis of their accuracies in out-of-sample forecasting, both point and density. Our results show that it is difficult to say that any one model of trend inflation is the best. Several different trend specifications seem to be about equally accurate, and the relative accuracy is somewhat prone to instabilities over time.