基于ARIMA模型的沪深300股指期货价格预测研究

基于ARIMA模型建立了股指期货价格的预测模型,对20100416～20110113间共180个交易13的沪深300股指期货合约收盘价数据进行了实证分析,结果表明：ARIMA模型对于股指期货的价格走势短期预测效果良好,模型能有效反应期货价格的波动性走势．     

Short-run price forecast performance of individual and composite models for 496 corn cash markets

Using daily prices from 496 corn cash markets for July 2006–February 2011, this study investigates short-run forecast performance of 31 individual and 10 composite models for each market at horizons of 5, 10, and 30 days. Over the performance evaluation period September 2010–February 2011, two composite models are optimal across horizons for different markets based on the mean-squared error. For around half of the markets at the horizon of 5 days and most of them at 10 and 30 days, the mean-squared error of a market's optimal model is significantly different from those of at least other 23 models evaluated for it. Root-mean-squared error reductions through switching from non-optimal models to the optimal are generally around 0.40%, 0.55%, and 0.87% at horizons of 5, 10, and 30 days.     

Predicting Turning Points Through the Integration of Multiple Models

A new method for forming composite turning-point (or other qualitative) forecasts is proposed. Rather than forming composite forecasts by the standard Bayesian approach with weights proportional to each model's posterior odds, weights are assigned to the individual models in proportion to the probability of each model's having the correct turning-point prediction. These probabilities are generated by logit models estimated with data on the models' past turning-point forecasts. An empirical application to gross national product/gross domestic product forecasting of 18 Organization for Economic Cooperation and Development countries demonstrates the potential benefits of the procedure     

When and how much do average forecasts improve predictive accuracy

A composite forecast combines two or more individual forecasts into a single estimate by way of a number of different averaging schemes. The easiest way to combine forecasts is through using a simple average. In this paper, the authors show that in many instances the simple average of individual forecasts approximates the optimal combining scheme. Results are expressed in terms of the probability that a composite forecast will improve upon an individual forecast.     

国际煤炭市场价格的预测模型

根据国际煤炭市场价格的特性,建立了国际煤炭市场价格预测的随机序列线性模型,应用实际数据回归出预测方程并检验了模型。     

嵌入知识的综合集成预测方法及其在企业收益额预测中的应用

针对企业收益额预测，提出了一种嵌入知识的综合集成预测方法。首先，基于系统思想，提出了嵌入知识的人-机交互式综合集成预测方法框架；进而重点介绍了集成预测方法中的信息预处理、预测模型选择、效果检验等关键子模块；最后，以某公司的收益额预测为例，验证了其有效性。     

A Reality Check for Data Snooping

Data snooping occurs when a given set of data is used more than once for purposes of inference or model selection. When such data reuse occurs, there is always the possibility that any satisfactory results obtained may simply be due to chance rather than to any merit inherent in the method yielding the results. This problem is practically unavoidable in the analysis of time‐series data, as typically only a single history measuring a given phenomenon of interest is available for analysis. It is widely acknowledged by empirical researchers that data snooping is a dangerous practice to be avoided, but in fact it is endemic. The main problem has been a lack of sufficiently simple practical methods capable of assessing the potential dangers of data snooping in a given situation. Our purpose here is to provide such methods by specifying a straightforward procedure for testing the null hypothesis that the best model encountered in a specification search has no predictive superiority over a given benchmark model. This permits data snooping to be undertaken with some degree of confidence that one will not mistake results that could have been generated by chance for genuinely good results.