New evidence on the robust identification of news shocks: Role of revisions in utilization‐adjusted TFP series and term structure data

Data revisions and selections of appropriate forwarding‐looking variables have a major impact on true identification of news shocks and quality of research findings derived from structural vector autoregression (SVAR) estimation. This paper revisits news shocks to identify the role of different vintages of total factor productivity (TFP) series and term structure of interest rates as major prognosticators of future economic growth. There is a growing strand of literature regarding the use of utilization‐adjusted TFP series, provided by Fernald (Federal Reserve Bank of San Francisco, Working Paper Series, 2014) for identification of news shocks. We reestimate Barsky and Sims' (Journal of Monetary Economics, 2011, 58, 273–289) empirical analysis by employing 2007 and 2015 vintages of TFP data. We find substantial quantitative as well as qualitative differences among impulse response functions when using 2007 and 2015 vintages of TFP data. Output and hours initially decline, followed by quick reversal of both variables. In sharp contrast to results achieved by the 2007 vintage of TFP data, results achieved by the 2015 vintage of TFP data depict that output and hours will increase in response to positive TFP shock. By including term structure data in our VAR specification, total surprise technology shock and news shock account for 97% and 92% of the forecast error variance in total TFP and total output respectively. We find that revisions in TFP series over time ultimately impact the conclusion regarding news shocks on business cycles. Our results support the notion that term structure data help in better identification of news shock as compared to other forward‐looking variables.     

Measuring the market risk of freight rates: A forecast combination approach

This paper addresses the issue of freight rate risk measurement via value at risk (VaR) and forecast combination methodologies while focusing on detailed performance evaluation. We contribute to the literature in three ways: First, we reevaluate the performance of popular VaR estimation methods on freight rates amid the adverse economic consequences of the recent financial and sovereign debt crisis. Second, we provide a detailed and extensive backtesting and evaluation methodology. Last, we propose a forecast combination approach for estimating VaR. Our findings suggest that our combination methods produce more accurate estimates for all the sectors under scrutiny, while in some cases they may be viewed as conservative since they tend to overestimate nominal VaR.     

Forecasting the Outcome of Closed‐Door Decisions: Evidence from 500 Years of Betting on Papal Conclaves

Closed‐door decisions may be defined as decisions in which the outcome is determined by a limited number of decision‐makers and where the process is shrouded in at least some secrecy. In this paper, we examine the use of betting markets to forecast one particular closed‐door decision: the election of the pope. Within the context of 500 years of papal election betting, we employ a unique dataset of betting on the 2013 papal election to investigate how new public information is incorporated into the betting odds. Our results suggest that the market was generally unable to incorporate effectively such information. We venture some possible explanations for our findings and offer suggestions for further research into the prediction and predictability of other ‘closed‐door’ decisions. Copyright © 2015 John Wiley & Sons, Ltd.     

基于改进的MMR算法的新闻文本抽取式摘要方法

提出了基于最大边缘相关（maximal marginal relevance，MMR）的新闻摘要方法以及基于支持向量机（support vector machine，SVM）和MMR相结合的新闻摘要方法。其中，第1种方法是对传统MMR模型进行了改进，第2种方法使用了改进MMR模型对SVM分类结果进行了二次选择。实验表明：相比于传统MMR模型，该文提出的基于改进MMR的摘要方法和基于SVM-MMR的摘要方法的平均准确率分别提升了0.148、0.204，且基于MMR的新闻摘要方法的摘要效率约为基于SVM-MMR的摘要方法的3倍。改进的MMR算法更加适用于对摘要效率要求高的应用场景，特别是对长文本进行摘要。基于SVM-MMR的摘要方法则更适用于生成对文本内容覆盖相对全面的摘要。     

基于ReliefF-PCA和SVM的发动机故障诊断方法研究

针对柴油发动机机组振动信号非线性和非平稳性以及机组实际故障案例样本数据少的特点，提出了一种基于ReliefF、主成分分析（PCA）以及支持向量机（SVM）的柴油发动机故障诊断方法。首先提取发动机冲击信号的特征参数，运用ReliefF选择出其中的敏感特征以降低处理过程的计算难度；然后采用PCA进一步提取敏感特征，消除各特征之间的相关性，避免冗余；最后利用SVM实现机组的故障分类，诊断不同类型的故障。将本文方法应用于柴油机实际典型故障案例中，结果表明该方法能有效提取柴油机缸盖振动信号中的故障敏感特征，并实现多种典型故障的诊断。     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.     

一种改进的基因表达数据分类方法

从分类算法和特征基因选择两个方面研究基因表达数据的分类,将传统的Support Vector Machines(SVM)算法和K-nearest neighbor(KNN)算法两者结合成为一种应用于基因表达数据分类的算法,并针对基因表达数据分类数据集“样本少,维数高”的特点,提出了一种改进的基于相关性的递归特征消除算法(简称为C-RFE),消除了数据冗余．实验结果表明,新方法可有效提高分类准确率和特征选取的效率．     

Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

This paper examines the forecasting ability of the nonlinear specifications of the market model. We propose a conditional two‐moment market model with a time‐varying systematic covariance (beta) risk in the form of a mean reverting process of the state‐space model via the Kalman filter algorithm. In addition, we account for the systematic component of co‐skewness and co‐kurtosis by considering higher moments. The analysis is implemented using data from the stock indices of several developed and emerging stock markets. The empirical findings favour the time‐varying market model approaches, which outperform linear model specifications both in terms of model fit and predictability. Precisely, higher moments are necessary for datasets that involve structural changes and/or market inefficiencies which are common in most of the emerging stock markets. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Inflation Across Euro Area Countries and Sectors: A Panel VAR Approach

In this paper, we adopt a panel vector autoregressive (PVAR) approach to estimating and forecasting inflation dynamics in four different sectors—industry, services, construction and agriculture—across the euro area and its four largest member states: France, Germany, Italy and Spain. By modelling inflation together with real activity, employment and wages at the sectoral level, we are able to disentangle the role of unit labour costs and profit margins as the fundamental determinants of price dynamics on the supply side. In out‐of‐sample forecast comparisons, the PVAR approach performs well against popular alternatives, especially at a short forecast horizon and relative to standard VAR forecasts based on aggregate economy‐wide data. Over longer forecast horizons, the accuracy of the PVAR model tends to decline relative to that of the univariate alternatives, while it remains high relative to the aggregate VAR forecasts. We show that these findings are driven by the event of the Great Recession. Our qualitative results carry over to a multi‐country extension of the PVAR approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns

This paper evaluates the accuracy of 1‐month‐ahead systematic (beta) risk forecasts in three return measurement settings; monthly, daily and 30 minutes. It was found that the popular Fama–MacBeth beta from 5 years of monthly returns generates the most accurate beta forecast among estimators based on monthly returns. A realized beta estimator from daily returns over the prior year generates the most accurate beta forecast among estimators based on daily returns. A realized beta estimator from 30‐minute returns over the prior 2 months generates the most accurate beta forecast among estimators based on 30‐minute returns. In environments where low‐, medium‐ and high‐frequency returns are accurately available, beta forecasting with low‐frequency returns are the least accurate and beta forecasting with high‐frequency returns are the most accurate. The improvements in precision of the beta forecasts are demonstrated in portfolio optimization for a targeted beta exposure. Copyright © 2016 John Wiley & Sons, Ltd.