A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

社会主义精神富裕论

马克思主义经典作家的相关阐述 ,社会主义本质的实现和比较优势的获取 ,精神富裕和物质富裕特殊的关系均表明 ,精神富裕是建设社会主义的应有之义。而精神富裕的导向、认识、实践和凝聚作用 ,又反过来在促进社会主义事业快速、健康与持续发展中扮演着重要角色。同时 ,社会主义精神富裕还具有向度的特性 ,是一个动态开放的发展过程     

Assessing accuracy of a continuous screening test in the presence of verification bias

Summary.  In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be biased in a study with such a design. This bias is known as verification bias. Verification bias correction methods that accommodate screening tests with binary or ordinal responses have been developed; however, no verification bias correction methods exist for tests with continuous results. We propose and compare imputation and reweighting bias-corrected estimators of true and false positive rates, receiver operating characteristic curves and area under the receiver operating characteristic curve for continuous tests. Distribution theory and simulation studies are used to compare the proposed estimators with respect to bias, relative efficiency and robustness to model misspecification. The bias correction estimators proposed are applied to data from a study of screening tests for neonatal hearing loss.     

三维Minkowski空间中有相等主曲率函数的一类特殊曲面的位置向量场

讨论三维Minkowski空间R2 .1中一类特殊曲面在主曲率函数相等时的情形 ,给出了这类曲面在这一情形下的位置向量场     

B股与H股及红筹股之间的溢出效应与信息流动

运用向量GARCH-M模型检验了B股与H股及红筹股之间的溢出效应与信息流动.实证结果表明:红筹股对沪深市B股、H股对沪市B股的收益和波动溢出效应均显著存在,而反向的溢出效应均不显著,表明信息是从红筹股向沪深市B股、从H股向沪市B股单向流动的;深市B股与H股之间相互的收益溢出效应均存在,而波动溢出效应均不存在,信息在H股和深市B股之间的流动情况不明显;在信息流动过程中,红筹股始终处于信息领先地位.     

关于参数化测度（Ⅱ）

引进一类拓扑向量空间，用以描述测度的偏导。使用的工具是所谓的参数化鞅。建立了若干参数化鞅的收敛定理。     

亚里士多德的"德性就是中道"思想解读--读《尼各马科伦理学》有感

亚里士多德的《尼各马科伦理学》是其伦理学思想的代表作,书中阐述的重要内容之一:“德性就是中道”,具有丰富的内涵,体现了与德性相关的选择性、实践理性以及与社会性生活密切相关等特点。这些对当代道德教育仍有一定的启迪意义。     

国家财政对农业投入的分析与预测——基于状态空间模型的实证分析

国家财政对农业的投入是影响农业发展的重要因素。收集了建国以来国家财政对农业投入的数据,分析构造出状态空间模型,应用卡尔曼滤波估计出状态向量,挖掘出隐藏在数据内部的变化特征,分析国家财政对农业投入的实际情况,并对未来几年的农业投入进行预测。     

低碳视角下农业集约化、绿色化与资源再生化 的实现机制研究

在实现"碳达峰碳中和"目标的重要历史机遇期,推动实现农业集约化、绿色化和资源再生化,构建资源节约型、绿色低碳型、生态循环型的农业发展模式,对于农业可持续发展具有重要意义。以2000-2018年我国31个省份为研究对象,通过面板向量自回归模型(PVAR),刻画资源消耗、农业产出、农业碳排放之间的动态传导机制。结果表明:(1)东部农业"碳达峰"节点出现较早,东北、西部相对较晚;2018年农业碳排放居前三的省份为湖南、黑龙江、河南,居后三的为北京、天津、上海;(2)资源消耗和农业产出之间呈现"低投入高产出"的农业集约化生产模式,但影响力微弱;农业产出和农业碳排放之间"高产出高排放"的情形长期面临农业绿色化的严峻考验;农业碳排放与资源消耗之间呈现"低排放低投入"的资源再生化现象,影响力短期微弱长期较强;(3)推动实现农业集约化、绿色化、资源再生化的关键在于探索降低农业碳排放的多重路径。因此,应强化农业集约化生产,充分发展资源再生化模式,探索形成农业绿色化格局新路径,以此来加快实现碳减排目标,推动农业可持续发展。     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.