Stock market forecasting is a very difficult problem for study and the forecasting support system is an effective method.
股票市场的预测是一个非常难的研究课题，建立股票预测支持系统是进行股票预测的一种有效手段。
Neural networks have been used as a mechanism of knowledge acquisition for expert system in stock market forecasting with astonishingly accurate results.
神经网络已被用为股票市场预测专家系统的知识获取的一种机制，并取得了惊人的准确结果。
This paper presents a method based on BP neural network for a stock market modeling, forecasting and deciding.
给出一种基于BP神经网络的股票市场建模、预测及决策方法。
As a result, it is necessary to replace the traditional statistic model with nonlinear model that can deal with imperfective information in order to improve the quality of forecasting stock market.
股市的运行是一个非常复杂的不完备的非线性过程，因此，需要用对不完备信息进行处理的非线性模型代替传统的统计模型，以便进一步提高股市预测的质量。
However there are lots of problems in the supply chain management, demand forecasting deviated real market demand, severe bullwhip effects, the high stock costs and low level of service, etc.
但是供应链管理中存在着需求预测偏离市场、牛鞭效应严重、库存成本高、服务水平低等问题。
And after an impressive 2010, stock-market strategists are forecasting good gains again for 2011.
在收益颇丰的2010年之后，股票策略师预测，2011年的股市仍然会有不错的表现。
Using the A-stock data in the Chinese stock market from 2004 to 2010, we examine whether firms' historic forecasting reputation affects investor response to their subsequent forecasts.
本篇论文采用2004年至2010年A股市场上发布管理层预测的公司作为研究对象，研究公司的历史预测声誉是否会影响投资者对公司的盈利预测的反应。
Using the A-stock data in the Chinese stock market from 2004 to 2010, we examine whether firms' historic forecasting reputation affects investor response to their subsequent forecasts.
本篇论文采用2004年至2010年A股市场上发布管理层预测的公司作为研究对象，研究公司的历史预测声誉是否会影响投资者对公司的盈利预测的反应。
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