财务困境预测中BP神经网络模型应用综述

金融危机席卷全球。处于金融市场之中的企业随时面,临着陷入财务困境的可能,财务困境预测模型的建立可以使公司提前预测到困境的发生,从而及早避免投资损失。随着信息技术的发展,人工神经网络预测模型开始兴起,本文重点介绍了BP神经网络模型在财务困境预测中的应用情况,并将BP神经网络模型与传统统计方法进行了比较分析。     

我国上市公司独立审计意见的预测价值分析

选取沪、深两市2000～2002年样本的综合数据和各年度子样本数据,运用超额收益和Logistic回归分析方法,同时考虑公司规模和样本所属板块的影响,对各解释变量与累计超额收益率的价值相关性及审计意见类型对股价超额收益的增量解释能力进行了研究.单变量检验结果表明,净资产收益率与股价超额收益具有显著的正价值相关性,审计意见类型则弱之;联合价值检验显示,审计意见类型和其他相关因素的联动作用与审计意见类型单独作用相比,对样本累计超额收益的影响具有显著差异;Logistic回归分析得出,审计意见类型具有预测价值.     

网上技术交易趋势预测模型及应用研究

网上技术交易平台开展交易趋势预测和交易潜力识别，可提高平台技术信息管理水平和网上技术市场运作效率。从网上技术市场和专利情报分析理论出发，提出网上技术交易趋势预测分析指标，设计构建预测模型和相关流程方法，并以广州知识产权交易中心的网上挂牌专利数据为例，进行定量测度和评价分析，从而提出发展网上交易服务能力的建议。结果表明，提出的技术交易趋势预测模型与流程具有适用性和可操作性，可针对网上平台技术资源确定差异化预测类型及其关键影响指标，建立基于技术领域分类的平台分析体系。研究结论可为网上平台设计基于自身特点的技术信息管理策略，提高信息服务专业化能力提供参考。     

上市公司财务困境预测的实证分析新探

本文以中国内地上市公司中ST公司为研究样本,通过配对样本的比较分析,选出了5个财务指标变量建立ST公司被ST前5年的回归模型,通过LPM回归估计出模型的参数,并用预先选取的样本进行超前预测的准确性检验,得到了具有超前5年预测效果的模型组.     

Properties of optimal forecasts under asymmetric loss and nonlinearity

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of optimal forecasts can be invalid under asymmetric loss and nonlinear data generating processes and thus may be very misleading as a benchmark for an optimal forecast. We establish instead that a suitable transformation of the forecast error—known as the generalized forecast error—possesses an equivalent set of properties. The paper also provides empirical examples to illustrate the significance in practice of asymmetric loss and nonlinearities and discusses the effect of parameter estimation error on optimal forecasts.     

制造业上市公司财务预警研究

本文以2006年深沪两市新增制造业ST上市公司为研究样本,同时选取相应的制造业盈利公司配对,通过定量和定性分析建立预警指标体系,运用fisher二类线性判别法建立财务预警模型,对制造业上市公司财务预警进行了分析,以期为上市公司财务预警分析提供参考。     

WHO DRIVES THE RUSSIAN FINANCIAL MARKETS?

By employing the EGARCH model using monthly data from September 1995 to March 2003, we found that financial indicators from Germany rather than the United States are the main drivers of Russian financial markets. In a one‐step prediction, the fluctuations of asset returns are well predicted that the prediction errors fall within the prescribed range of the confidence bands. However, EGARCH does not necessarily dominate the benchmark prediction of the random walk model, because with Russia's financial markets constantly in transition and adjusting to frequent changes in the financial system, the usefulness of past data is diminished.     

Keeping a weather eye on prediction markets: The influence of environmental conditions on forecasting accuracy

Increasingly, prediction markets are being embraced as a mechanism for eliciting and aggregating dispersed information and providing a means of deriving probabilistic forecasts of future uncertain events. The efficient market hypothesis postulates that prediction market prices should incorporate all information that is relevant to the performances of the contracts traded. This paper shows that such may not be the case in relation to information regarding environmental factors such as the weather and atmospheric conditions. In the context of horserace betting markets, we demonstrate that even after the effects of these factors on the contestants (horses and jockeys) have been discounted, the accuracy of the probabilities derived from market prices is affected systematically by the prevailing weather and atmospheric conditions. We show that significantly better forecasts can be derived from prediction markets if we correct for this phenomenon, and that these improvements have substantial economic value.     

The M4 Competition: 100,000 time series and 61 forecasting methods

The M4 Competition follows on from the three previous M competitions, the purpose of which was to learn from empirical evidence both how to improve the forecasting accuracy and how such learning could be used to advance the theory and practice of forecasting. The aim of M4 was to replicate and extend the three previous competitions by: (a) significantly increasing the number of series, (b) expanding the number of forecasting methods, and (c) including prediction intervals in the evaluation process as well as point forecasts. This paper covers all aspects of M4 in detail, including its organization and running, the presentation of its results, the top-performing methods overall and by categories, its major findings and their implications, and the computational requirements of the various methods. Finally, it summarizes its main conclusions and states the expectation that its series will become a testing ground for the evaluation of new methods and the improvement of the practice of forecasting, while also suggesting some ways forward for the field.