Predicting recovery rates using logistic quantile regression with bounded outcomes

Logistic quantile regression (LQR) is used for studying recovery rates. It is developed using monotone transformations. Using Moody’s Ultimate Recovery Database, we show that the recovery rates in different partitions of the estimation sample have different distributions, and thus for predicting recovery rates, an error-minimizing quantile point over each of those partitions is determined for LQR. Using an expanding rolling window approach, the empirical results confirm that LQR with the error-minimizing quantile point has better and more robust out-of-sample performance than its competing alternatives, in the sense of yielding more accurate predicted recovery rates. Thus, LQR is a useful alternative for studying recovery rates.     

物流配送路径优化问题的模型及改进混合算法

在建立带有时间窗的物流配送路径优化问题数学模型的基础上．构造了求解该问题的遗传模拟退火混合算法。该混合算法利用了遗传算法较强的全局搜索能力和模拟退欠算法较好的局部搜索能力,克服了两种算法各自在寻优方面的不足,使其在全局最优搜索和计算速度方面都有了很大的提高。最后经仿真试验证实了混合算法解决物流配送路径优化问题的优越性。     

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate.     

The dynamic financial distress prediction method of EBW-VSTW-SVM

Financial distress prediction (FDP) takes important role in corporate financial risk management. Most of former researches in this field tried to construct effective static FDP (SFDP) models that are difficult to be embedded into enterprise information systems, because they are based on horizontal data-sets collected outside the modelling enterprise by defining the financial distress as the absolute conditions such as bankruptcy or insolvency. This paper attempts to propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW). The dynamic FDP (DFDP) method is named EBW-VSTW-SVM, which keeps updating the FDP model dynamically with time goes on and only needs the historic financial data of the modelling enterprise itself and thus is easier to be embedded into enterprise information systems. The DFDP method of EBW-VSTW-SVM consists of four steps, namely evaluation of vertical relative financial distress (VRFD) based on EBW, construction of training data-set for DFDP modelling according to VSTW, training of DFDP model based on SVM and DFDP for the future time point. We carry out case studies for two listed pharmaceutical companies and experimental analysis for some other companies to simulate the sliding of enterprise vertical time window. The results indicated that the proposed approach was feasible and efficient to help managers improve corporate financial management.     

关于《建筑边坡工程技术规范》的讨论——圆弧滑动破坏的土质边坡锚杆加固技术力学分析

从Bishop法的基本理论及假设出发,推导边坡规范附录A.0.1圆弧滑动破坏边坡稳定性计算公式,并进一步对圆弧滑动破坏土质边坡安全系数的概念进行延伸,提出了边坡广义安全系数为总抗滑力与总下滑力之比,在边坡支护工程设计时,提出了支护工程抗力的求解方法,通过工程实例计算出边坡支护工程抗力,最后按照规范要求进行锚杆设计计算。     

溪洛渡水电站出线竖井滑模施工

基于溪洛渡水电站出线竖井滑模施工要求和生产现状,探讨滑模装置组成设计,明确滑模施工顺序和方法步骤,旨在规范滑模施工流程,确保工程质量达标。     

Uncovering the interrelationship between the U.S. stock and housing markets: a bootstrap rolling window Granger causality approach

The purpose of this study is to investigate the time-varying interrelationship between the housing market and the stock market in the U.S. during the period of 1890–2013, by employing a rolling window subsample with a bootstrap Granger causality test. The rolling window allows for structural changes in the economy over time. Whereas previous studies have not identified a causal relationship between the U.S. housing price index and the SP500 stock price index, this new analysis is the first to identify certain periods wherein either the wealth effect or the investment effect can be observed.     

浅谈夏热冬冷地区建筑外窗及外窗玻璃对建筑能耗的影响

本文从国外及我国建筑外窗现状、外窗开启扇对建筑物使用的影响、外窗玻璃的性能分析、培养被动节能的习惯,这四个方面阐述建筑外窗及外窗玻璃对建筑能耗的影响。     

Short-term decision support system for maintenance task prioritization

Maintenance operations have a direct influence on production performance in manufacturing systems. Short-term production analysis is imperative to enable manufacturing operations to optimally respond to dynamic changes in the system behavior. However, most of the conventional decision support systems for production and maintenance focus on long-term statistic analysis, which is usually not applicable to a short-term period. Maintenance task prioritization is crucial and important for short-term analysis to reduce unnecessary or improper maintenance activities, especially when availability of maintenance resources is limited. The existing methods for maintenance priority assignment are often through heuristic methods or experience, which could cause unscheduled downtime and production losses. In this paper, a short-term decision support system for maintenance task prioritization based on the system operating conditions is introduced. The impact factor for priority assignment is obtained theoretically. A case study based on the simulation of an automotive assembly line illustrates that the proposed short-term system improves the system performance with a lower cost than the long-term method.     

一种低复杂度的降低MIMO—OFDM峰平比的CARI方法

天线旋转取反算法(CARI)是降低MIMO-OFDM系统峰平功率比(PAPR)的一种性能较优的算法,不足之处是其迭代次数很大。CARI的次最优算法逐次次最优天线旋转取反(SS-CARI)算法虽然可以减小迭代次数,但性能损失又比较大。针对这一问题,提出了一种用滑动窗搜索旋转取反组合的次最优CARI方法,利用滑动窗搜索局部最优的旋转取反组合,既可以避免整体的遍历搜索大大减小CARI算法的计算复杂度,又可以改善SS-CARI的PAPR减小性能。仿真结果表明,该方法能够在系统复杂度与PAPR降低性能上取得很好的折衷。