Scientific methods in finance

Computer software can easily produce many financial models with technical skill, but investigators need to interpret and adapt computer output. Such analysis requires a deep knowledge of scientific fundamentals. We argue that an evidential perspective is more appropriate than a decision framework for model selection. We discuss four necessary conditions for any scientific investigation: observables, interpretables, replicables, and robustness in the context of examples familiar to finance professionals. Carefully planning the design of an experiment is the best way to address many econometric maladies. Furthermore, we emphasize the need for more sensitivity testing and for independent replication of empirical results within the finance profession.     

峰谷分时电价的现状分析与发展趋势探讨

分时电价以经济学均衡价格为理论依据。它在引导客户合理用电、削峰填谷、优化电力资源配置等方面起了积极的作用。但它也存在分时价差幅度小、时段划分长、缺乏灵活性及实施范围窄等问题。完善分时电价运行机制,由分时电价向实时电价转变。     

运用经济杠杆促进错峰调荷——安徽省峰谷电价分析

阐述了安徽省峰谷电价的实施背景、方案的主要内容和创新特点,2004年上半年的实际执行情况及取得的效果。结合安徽省实际情况,对下一阶段如何优化峰谷电价方案、进一步发挥价格杠杆作用提出了建议。     

Inference with Dependent Data in Accounting and Finance Applications

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.     

Why are there time-varying comovements in the European stock market?

This paper analyzes the dynamics of pair comovements between different domestic European stock market returns (Spain, France, Germany, Switzerland and the United Kingdom) seeking to check whether there is a unique source of risk driving those dynamics. Once it is shown that the comovements are time-varying, the question is to find whether a global index such as the Euro Stoxx can be considered the main source of risk. To that end we estimate and test for time-varying global pair covariances and for time-varying remaining pair covariances once the effect of the Euro Stoxx is removed. The empirical results are obtained considering locally stationary variables, a family that includes variables with first and second time-varying moments. Under that framework time-varying means and covariances can be estimated using a spline-based procedure and Wald-type statistics can be computed to test for time-variations. A simulation study shows that the role of the mean estimation part is crucial to the good performance of the tests for second moments. The empirical results evidence that all global pair covariances for the European countries analyzed are time-varying, but also that the Euro Stoxx can be considered as the driving source of risk for these time-varying dynamics. This conclusion is very useful for modeling purpose and financial strategies. Finally, we repeat the analysis considering the Nasdaq as an alternative global index and find that it explains only a small part of the dynamics in the European pair comovements.     

Ratio-based design hour determination for airport passenger terminal facilities

To avoid both over-design and under-sizing of airport passenger terminal facilities such as security checkpoints, the infrastructure is designed for a specifically determined design load. As such, the design load is considered for a short period of time, usually an hour of operation, during which peak, though not necessarily maximum, demand occurs. For strategic planning applications, future design loads can be determined by either fictitious flight schedules or ratio-based models which forecast the relationship between design load and annual demand. This study presents two ratio-based methods which allow the direct determination of design hour loads (DHL) for passenger terminal facilities. The unsaturated DHL model considers the relationship between observed passenger flows in the terminal and aggregated annual demand data. The saturated DHL model includes several operational constraints which limit the actual DHL, such as limitations in the runway system or the fleet mix operating at an airport. Both models are applied to two real-world airports, for which the DHL of the security checkpoint facilities is estimated from large datasets covering multiple years. Results are significant at the 5 % level and suggest that the proposed ratio-based methods are appropriate for airport strategic planning applications.     

材料试验检测技术在公路工程中的应用分析

试验检测技术作为当前对公路路面施工中工程质量保证当中的重要环节,对路面工程施工的质量有着重要的作用。本文以公路路面养护中的主要材料试验检测方法和指标作为基础,从材料试验检测的作用和实际应用对其进行阐述,并指出在养护中运用这些指标和方法的重要性。     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。     

连续梁墩梁固结研究——以二七长江大桥上跨铁路工程为例

结合武汉市二七长江大桥上跨铁路工程施工过程,采用模拟计算墩梁固结临时墩的受力,结合监控资料,得出其荷载的合理取值,并总结归纳墩梁固结的多种形式,分析优缺点,提出各种形式的适用范围。     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.