浅谈市场预测的技巧及原则

市场预测是根据各种有关信息对未来市场的发展趋势作出科学判断的一种方法,借以编制计划,作出营销策略。在当前市场竞争十分激烈的情况下,市场预测就显得更加重要了。市场预测的十个方面:一、连锁预测。当市场出现某些商品需求时,就会连锁带动其它商品的需求。住房消费,就...     

基于实际算例的需求预测方法模型的选择

文章以一家天然气公司2006~2009年度各季度需求量数据分析了静态预测方法模型和适应性预测方法模型在实际需求预测应用中的具体选择问题。对于适应性预测方法模型的几种具体预测方法模型以算例为基础,做了详细的测算;通过测算结果的比较分析,得出了Winter模型预测精确度更高的结论。在实践中尽可能选择Winter模型进行需求预测对于企业决策是有实际意义的。     

基于粗糙集的组合预测方法在粮食产量预测中的应用

粮食产量是国民经济发展一项重要指标,对粮食产量的预测,目前多采用组合预测的方法.在组合预测中单一预测模型的选择和组合系数的确定是两个至关重要的问题,本文基于粗糙集理论构建了一种新的组合预测模型,这种方法计算简单,无需建立数学模型,完全通过数据驱动.算例显示这种方法不但明显优于传统的最优组合预测方法,还优于先进的神经网络组合模型,在实际的粮食产量预测问题中有着广泛的应用前景.     

变量选择偏离对预测的影响研究

模型选择目的可以分为两类:一类是变量选择,另一类是预测分析.文章系统地研究了两种目的异同和建模时要注意的问题,提出了变量选择偏离的概念及其与预测的关系.     

两种成本计算方法对企业预测与决策的影响

在当今激烈的竞争和瞬息万变的市场状况下,能否进行正确的预测与决策,是关系到企业生存与发展的重要问题。正确的预测与决策取决于准确无误的数据资料。据统计,企业预测与决策的信息资料70％以上来源于会计资料。因此,会计核算资料的准确与否及会计核算方法的选择直接影响企业预测与决策的正确程度。目前在预测与决策的理论方法中,大都以变动成本法为会计基础,但在实际核算中却采用制造成本法,这两种成本计算方法在计算产品成本、存货成本和利润方面都有一定的差距,由此对企业预測与实际执行结果产生     

基于Knockoff-Logistic的多因子量化选股研究

在数据驱动时代，如何挖掘金融资产的信息、挑选恰当的资产，对稳定收益、控制风险意义重大。多因子量化模型是选择股票的常用方法，选取最优解释力的因子集合是其主要目的之一。现有因子选择方法没有考虑到控制错误发现率(FDR),不利于构建稳健的投资策略。为此，在Logistic回归的基础上引入Knockoff方法进行因子选择，通过Lasso实现因子选择，利用Knockoff控制变量选择的FDR从而提高准确率。基于所选因子，在Logistic回归下进行股票预测，并与线性判别分析、支持向量机以及随机森林模型的预测结果进行对比。对沪深300指数和中证500指数成分股2007—2020年的数据进行实证研究，采用滑动回归法进行收益预测，并建立季度换仓的投资策略。研究表明，从变量选择上来看，基于Knockoff方法选出的因子所构造的选股模型具有更好的市场表现；从模型对比上来看，Logistic回归预测的投资组合具备高收益、低风险的优势。综合来看，将Knockoff方法引入到多因子选股模型有利于提高因子选择的准确度，对优化资产配置具有参考意义。     

组合预测及其应用研究

组合预测及其应用研究浙江财经学院徐大江一、简述各预测方法性能的优劣是有条件的，为了有效地使预测具有对未来变化较高的适应性，即提高预测精度，增加预测的稳健性，可将若干种预测方法组合成一个预测模型，这就是组合预测。组合预测模型是两个或两个以上观测方法对同...     

知识服务业背景下客户市场细分的聚类方法研究

知识服务业所面对的客户需求具有知识化、专业化及问题解决为导向等特征.在服务过程中客户具有高度的参与性和交互性.文章指出客户市场细分不再局限于事前细分方法中常见的客户行为特征变量,提出了基于态度变量的客户市场事后细分策略,介绍了传统聚类方法K-means和SOFM方法在市场细分中的应用,以及支持向量机聚类方法(SVC)进行知识服务业的客户市场细分的过程.应用SVC方法于具体行业实施客户市场细分,并对比三种方法的聚类效果,说明了该方法对于提高判别分类效果的能力和优势.     

基于品牌认知的顾客选择决策模型

顾客选择是一个动态的过程,决策的最终核心是品牌价值与品牌认知的问题。从消费者角度看,认知度高的品牌更容易被消费者选中,特别是在同质化的大众消费品市场,顾客会倾向于根据品牌的熟悉程度来决定购买行为。文章从品牌认知背景出发,分析驱动消费者选择的决策因素,构建一种新水平上的顾客选择决策模型并进行了实证分析,为企业品牌建设提供管理借鉴。     

预测方法有效度指标的估计方法探讨

预测方法有效度指标的估计方法探讨王明涛李文华人们在进行预测时,关心的问题之一就是预测的精度。要想使预测达到一定的精度,就必须选择有效的预测方法。本文试图在预测有效度的估计方法方面做一些探讨,以起到抛砖引玉的作用。一、预测方法有效度指标概述设［Ｔ１,Ｔ...     

Forecasting time series of economic processes by model averaging across data frames of various lengths

This paper presents an extension of mean-squared forecast error (MSFE) model averaging for integrating linear regression models computed on data frames of various lengths. Proposed method is considered to be a preferable alternative to best model selection by various efficiency criteria such as Bayesian information criterion (BIC), Akaike information criterion (AIC), F-statistics and mean-squared error (MSE) as well as to Bayesian model averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models having different number of observations and selects forecast weights by minimizing the unbiased estimator of MSFE. Proposed method also yields forecast confidence intervals with a given significance level what is not possible when applying other model averaging methods. In addition, out-of-sample simulation and empirical testing proves efficiency of such kind of averaging when forecasting economic processes.     

Non-nested model selection based on the quantiles and it’s application in time series

We consider the problem of model selection based on quantile analysis and with unknown parameters estimated using quantile leasts squares. We propose a model selection test for the null hypothesis that the competing models are equivalent against the alternative hypothesis that one model is closer to the true model. We follow with two applications of the proposed model selection test. The first application is in model selection for time series with non-normal innovations. The second application is in model selection in the NoVas method, short for normalizing and variance stabilizing transformation, forecast. A set of simulation results also lends strong support to the results presented in the paper.     

Empirical information criteria for time series forecasting model selection

In this article, we propose a new empirical information criterion (EIC) for model selection which penalizes the likelihood of the data by a non-linear function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be used where there is a single time series to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task.

We compare the EIC with other model selection criteria including Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms both the AIC and BIC, particularly for longer forecast horizons. We also compare the criteria on simulated data and find that the EIC does better than existing criteria in that case also.     

Variable selection for semiparametric proportional hazards model under progressive Type-II censoring

Variable selection is an effective methodology for dealing with models with numerous covariates. We consider the methods of variable selection for semiparametric Cox proportional hazards model under the progressive Type-II censoring scheme. The Cox proportional hazards model is used to model the influence coefficients of the environmental covariates. By applying Breslow’s “least information” idea, we obtain a profile likelihood function to estimate the coefficients. Lasso-type penalized profile likelihood estimation as well as stepwise variable selection method are explored as means to find the important covariates. Numerical simulations are conducted and Veteran’s Administration Lung Cancer data are exploited to evaluate the performance of the proposed method.     

Variable selection in classification model via quadratic programming

Variable selection is an important decision process in consumer credit scoring. However, with the rapid growth in credit industry, especially, after the rising of e-commerce, a huge amount of information on customer behavior is available to provide more informative implication of consumer credit scoring. In this study, a hybrid quadratic programming model is proposed for consumer credit scoring problems by variable selection. The proposed model is then solved with a bisection method based on Tabu search algorithm (BMTS), and the solution of this model provides alternative subsets of variables in different sizes. The final subset of variables used in consumer credit scoring model is selected based on both the size (number of variables in a subset) and predictive (classification) accuracy rate. Simulation studies are used to measure the performances of the proposed model, illustrating its effectiveness for simultaneous variable selection as well as classification.     

基于联系数贴近度的区间型组合预测模型及其有效性

利用区间数和二元联系数的相互转化关系,把区间数组合预测问题转换成二元联系数组合预测问题。在联系数贴近度的最优准则下,建立基于联系数贴近度的区间型组合预测模型,分析了该模型的有效性理论,包括:基于联系数贴近度的区间型组合预测模型是非劣性组合预测、优性组合预测的充分条件定理,基于联系数贴近度的区间型组合预测模型的冗余预测方法的存在性和冗余方法的判定定理。对某省社会保障水平适度区间值进行组合预测的实证分析,结果显示所建立的模型能有效提高预测的精度。     

PREDICTION-FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS

In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time‐series model, for which the order of the model is chosen by an information criterion. We propose an extension of the focused information criterion (FIC) for model‐order selection, with emphasis on a high predictive accuracy (i.e. the mean squared forecast error is low). We obtain theoretical results and illustrate by means of a simulation study and some real data examples that the FIC is a valid alternative to the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of a prediction model. We also illustrate the possibility of using the FIC for purposes other than forecasting, and explore its use in an extended model.     

A New Feature Selection Method for Text Categorization of Customer Reviews

With the rapid development of e-commerce, online consumer review plays an increasingly important role in consumers’ purchase decisions. Most research papers use the quantitative measures of consumer reviews for statistical analysis. Here we focus on analyzing the texts of customer reviews with text mining tools. We propose a new feature selection method called maximizing the difference. Various classification methods such as boosting, random forest and SVM are used to test the performance of the new method along with different evaluation criteria. Both simulation and empirical results show that it improves the effectiveness of the classifier over the existing methods.     

变权重组合预测模型的局部加权最小二乘解法

随着科学技术的不断进步，预测方法也得到了很大的发展，常见的预测方法就有数十种之多。而组合预测是将不同的预测方法组合起来，综合利用各个方法所提供的信息，其效果往往优于单一的预测方法，故得到了广泛的应用。而基于变系数模型的思想研究了组合预测模型，将变权重的求取转化为变系数模型中系数函数的估计问题，从而可以基于局部加权最小二乘方法求解，利用交叉证实法选取光滑参数。其结果表明所提方法预测精度很高，效果优于其他方法。     

A Selection Procedure for the Number of Signals in Presence of Colored Noise

Ranking and selection theory is used to estimate the number of signals present in colored noise. The data structure follows the well-known MUSIC (MUltiple SIgnal Classification) model. We deal with the eigenvalues of a covariance matrix, using the MUSIC model and colored noise. The data matrix can be written as the product of two matrices. The first matrix is the sample covariance matrix of the observed vectors. The second matrix is the inverse of the sample covariance matrix of reference vectors. We propose a multi-step selection procedure to construct a confidence interval on the number of signals present in a data set. Properties of this procedure will be stated and proved. Those properties will be used to compute the required parameters (procedure constants). Numerical examples are given to illustrate our theory.