引入非财务变量的企业破产预测方法研究

以往的企业破产预测大都单纯以企业财务比率为解释变量进行模型估计。本文尝试分析近20年来国内外在引进非财务变量进行企业破产预测方面的一些创新及其发展趋势,供我国学者借鉴。     

Finding the future: Crowdsourcing versus the Delphi technique

When managers are unable to use quantifiable time series data to make forecasts or decide on uncertainties, they can either rely on their own intuition and judgment or resort to the insights of others. The Delphi technique is a well-known forecasting technique that relies on the pooled perspectives of experts to predict uncertain quantities or the outcomes of events. This relies on polling the opinions of experts, aggregating these opinions, feeding them back to the responding experts along with their own estimates, and having them repeat their judgment calls until some level of consensus is reached. More recently, however, the opinions of many others who are not experts have been sought on a range of topics in a loose assembly of similar techniques bundled under the title of crowdsourcing. This article compares Delphi and crowdsourcing as prediction and estimation tools for managers. It notes their differences and similarities, and provides a simple tool for executives to use in deciding whether or not to use these tools, and if so, which tool or combination of them will work best in a given situation.     

Research on Measurement and Forecast of Hong Kong＇ s RMB Offshore Market

近年来,香港人民币离岸市场迅速发展。随着市场规模的不断扩大,其波动对内地金融市场的冲击不容小视。本文运用VAR模型和状态空间模型,参照国际经验数据,模拟分析度量了香港人民币离岸市场对内地金融市场的风险,并在此基础上预测了未来风险多发的时间区间。     

基于《企业绩效评价标准值》的医药全行业绩效评价及预测

科学合理的绩效评价是现代企业发展的基础,对行业的发展能起到促进和引导作用。通过计算医药工业全行业、化药制造业、中药材及中成药制造业三个领域全行业综合绩效发现,2008—2014年,中药材及中成药制造业最优,医药工业全行业次之,化药制造业最低。同时通过灰色关联GM（1,1）模型对医药行业绩效进行预测可知,未来五年医药全行业绩效均呈上升趋势。     

基于混沌理论和PSO神经网络的短时交通流预测

交通流预测已成为智能交通的重要组成部分,针对短时交通流的非线性和不确定性,文中根据实际交通流中存在的混沌,利用C-C方法和小数据量法对交通流混沌进行了分析,在交通流混沌时间序列相空间重构的基础上构建了基于粒子群优化神经网络的单点单步预测模型,运用该模型对实际采集的美国加州城市快速路交通流数据进行了仿真研究,结果表明,该预测模型具有较高的预测精度,能够满足智能交通控制和诱导的需求。     

Asymptotic and bootstrap prediction regions for vector autoregression

Small sample properties of asymptotic and bootstrap prediction regions for VAR models are evaluated and compared. Monte Carlo simulations reveal that the bootstrap prediction region based on the percentile-t method outperforms its asymptotic and other bootstrap alternatives in small samples. It provides the most accurate assessment of future uncertainty under both normal and non-normal innovations. The use of an asymptotic prediction region may result in a serious under-estimation of future uncertainty when the sample size is small. When the model is near non-stationary, the use of the bootstrap region based on the percentile-t method is recommended, although extreme care should be taken when it is used for medium to long-term forecasting.     

Performance requirements of future Trajectory Prediction and Conflict Detection and Resolution tools within SESAR and NextGen: Framework for the derivation and discussion

Future Air Traffic Management will increasingly be based on a strategic, collaborative and automated concept of operations. A key prerequisite is the capability to guarantee common situational awareness amongst relevant stakeholders as a function of time, extrapolated into the future in order to strategically optimise safe air traffic flow. This is achieved with Decision Support Tools (DSTs), including Trajectory Prediction (TP) and Conflict Detection and Resolution (CDR) tools. The functions and requirements which these tools must fulfil are dependent upon the application within the concept of operations. In order to optimise the development of the DSTs, it is important to understand the requirements for each of the applications. This paper reviews the key functions of the TP and CDR elements of DSTs in relation to these applications. It discusses the key performance drivers, derives performance metrics and develops a framework for the derivation of TP and CDR performance requirements, to support industry and standardisation bodies in the harmonisation process. A mapping exercise is undertaken to identify which of the functionalities are supported by state-of-the-art TP and CDR tools (in the public domain) and establishes those that require further research and development, highlighting some of the key challenges.     

Inefficiency and predictability in the Brexit Pound market: a natural experiment

Exploiting the near-experimental conditions provided by the GBPUSD exchange rate during the Brexit vote of 2016, we quantify a significant delay of the market price in reflecting the increasing probability of a Brexit outcome over the vote counting period. We claim that the Brexit outcome could realistically have been predicted hours before the market adjusted to the outcome. This inefficiency is identified by comparing the market-implied probability of a Brexit outcome with a separate probability, estimated by a standard Monte-Carlo algorithm based on a simple linear regression model, representative of what should have been easily possible in real time. The core of the method is the real-time re-calibration of ex-ante ‘pollster’ predictions for the voting district outcomes by regressing the observed voting results onto them. For comparative purposes, a study of the MXNUSD exchange rate in the 2016 US Presidential Election was done, finding that the market-implied and model-estimated probabilities moved more consistently toward the Trump outcome. Put together, this identifies a somewhat anomalous breakdown in market efficiency in the case of the Brexit vote, which we attribute to its novelty as well as a kind of political bubble and subsequent crash, generated by confirmation bias and social herding.     

企业执行力问题诸说比较分析及研究趋势预测

近年来,全球学术界开始关注企业执行力问题,有必要对国内外执行力研究的理论内容进行系统梳理,并应以企业外部环境的演变为背景对已有的企业执行力相关理论和案例研究结论进行客观的分析和评价,以逻辑推理的形式对执行力理论未来的基本发展态势进行预测。     

应用回归分析加强预测管理

文章利用历史资料，运用数理统计中回归分析的方法，预测分析了居民非商品支出与居民文化生活服务支出的关系，建立了回归方程和进行相关性检验。