备件消耗预测方法研究

准确的备件消耗预测是合理有效地进行备件保障各项工作的基础。文章研究了现有备件消耗预测方法,并将其分为三类:基于解析的方法、基于历史数据的方法和基于仿真的方法。对各类预测方法分别进行了详细的介绍,阐述了其适用条件和使用局限性,并对比分析了各方法的优缺点。最后指出了下一步的研究方向。     

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.     

盈余构成、持续性差异与财务分析师盈余预测——基于我国A股上市公司的经验分析

遵循Sloan的经典研究思路,从分析师对盈余构成持续性差异区分的角度,对我国分析师盈余预测的有效性进行了实证研究。研究发现,我国的财务分析师高估了所有盈余构成的持续性,其发布的盈余预测虽然能够反映出应计利润的低持续性和现金流量的高持续性,但不能恰当地反映出操控性应计利润与非操控性应计利润之间的持续性差异。进一步的研究表明,财务分析师对盈余构成持续性的高估会显著影响其整体盈余预测准确度,这意味着我国财务分析师发布的盈余预测是有一定信息含量的,但在应对复杂会计信息时仍显不足。     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.     

证券投资风险预测的E-Bayes估计

本文给出了E-Bayes方法,以上海证券个股五粮液52个连续交易日的收盘价格为例,建立数学模型进行分析和预测,预测结果与市场实际值相当吻合。与灰色系统理论中的GM（1,1）预测模型相比,本文提出的方法预测的精度更高,计算量小。不仅适用于经济系统的分析与预测,也适用于其它系统的分析与预测。     

基于"标杆管理"的海南远景经济指标预测

在海南远期经济指标的预测过程中,时间跨度较大,经济社会发展的不确定因素很多,利用传统的数学模型不易把握其发展态势,难以预测。借鉴管理学上“标杆管理”的模式成功地对海南远景经济指标进行了分析预测,该思路同样适用于其他区域经济远景指标的预测。     

秦皇岛汤河流域产汇流参数特性

基于同一流域的产汇流参数,随着降雨强度及时空分布呈一定规律变化的特点,现结合汤河秦皇岛水文站实测资料,阐述了浅山区流域产汇流参数在不同条件下的计算方法,分析计算了流域前期影响雨量Pa、最大损失量Im、稳定下渗率fc、汇流参数m值,并利用计算的参数对历史洪水进行了检验,计算精度较高,对本流域及类似流域产汇流规律研究、洪水预报、工程设计均有一定参考价值。     

基于总体最小二乘法的系统响应修正方法

为了提高实时洪水预报的预报精度,提出了基于总体最小二乘平差理论的系统响应方法。传统的系统响应方法基于最小二乘法,只能考虑观测值的误差,因而传统的系统响应方法不能考虑动态系统响应矩阵存在的误差。对传统方法进行了分析并引入了总体最小二乘平差理论,改进方法同时考虑了动态系统响应矩阵和观测值的误差。针对动态系统响应矩阵的病态情形,通过总体最小二乘的岭估计解法给出稳定解。通过改进方法对新安江模型中的土壤含水量进行修正,应用于七里街流域,并与传统的系统响应方法进行比较。结果表明,两种方法都能提高模拟精度,改进方法相比于传统方法精度明显提升;改进方法的修正效果要优于传统方法,更加稳定。