对住宅院落空间的人文解读

文章通过对院落这一传统居住形式的人文解析,阐明了院落住宅的建筑空间特色,指出以院落为中心的基本平面格局成为当下住宅小区规划的新理念,揭示了“咫尺空间”的内涵,并进而分析了“空中院落”这一高层住宅设计的新思路。     

需求依赖价格和交货期的供应链协调模型

在一个两层供应链系统中,基于对价格和交货期敏感的需求,构建了Stackelberg和收益共享两种决策模型,并给出了最优价格和响应时间的求解过程,通过比较说明收益共享契约有助于提高整个供应链的收益.最后讨论了收益共享契约下渠道收益的分配问题.     

影子价格理论的应用误区分析

资源的影子价格是对资源合理配置的重要依据,是企业调整与优化生产方案的一个重要参考因素,本文运用线性规划有关理论并结合具体实例,探讨了一些关于影子价格理论应用的不正确提法,提出影子价格的新内涵:影子价格是对瓶颈资源的价值估计和对其重要性的认可,并且从中得出一些有益的结论.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

银行卡网络价格结构与刷卡消费组合决策模型

针对银行卡网络价格结构与刷卡消费之间复杂的关联问题,引入双边市场理论和组合决策思想,以实现银行卡网络整体发展为目标,基于Wright等设计的银行卡网络交换费优化模型,建立了银行卡网络价格结构与刷卡消费组合决策模型.通过加入扰动因子、采用双适应值比较和保留边界不可行解改进了粒子群算法,解决了模型的求解难题,为合理制定银行卡网络价格结构提供了一种新方法,同时拓宽了双边市场理论的应用领域,在电子支付管理领域具有广阔的应用前景.     

基于跳跃滤波和时变参数估计的中国股市微观结构研究

为了更为有效地探究微观市场结构对股票价格的影响,本文在状态空间模型框架下,同时将交易方向、带方向的交易量、交易间隔、微观噪声以及跳跃因素引入状态方程和观测方程中,建立全面的市场微观结构模型,以反映各变量对交易价格序列产生的暂时性和永久性影响.然后,在采用基于粒子滤波的非参数离群点检测方法检测跳跃并对跳跃进行滤波的基础上,运用贝叶斯最小二乘方法对微观结构模型系数、波动率以及状态变量进行实时更新和估计,在此基础上分析跳跃日度分布、日内分布、个股跳跃和共同跳跃特征,并同时判断不同交易方向、交易量的订单对价格产生的影响.最后选取2015年股灾期间、沪市股票的逐笔数据作为研究样本进行实证研究.实证结果显示:我国股票高频时间序列跳跃和共同跳跃均存在日内季节性,且跳跃频率和大小与市场信息密切相关;买卖订单对交易价格及股票价值的影响并不是完全对称的,交易价格对于微观结构的敏感度、微观结构噪声也随市场波动而发生变化,熊市中都相对更高;考虑跳跃滤波并对参数进行实时估计可以提高价格对于交易的敏感度,能更好地捕捉市场微观结构在价格发现中的作用.     

技术和价格不确定下企业研发投资决策研究

随着科学技术的迅速发展,研发(RD)活动对企业的生存与发展日益重要.本文将企业研发投资项目所承受的风险分为两种:研发前期不可对冲的技术风险,以及研发产品投入市场时因价格和需求的不确定性所承受的价格风险,而后者是可以部分对冲的.本文考虑一个以创新研发投资为主的企业,如何进行相应的投资消费问题.通过建立效用最大化模型,运用随机最优控制理论与方法得到关于指数效用函数的最优策略,同时阐述了研发投资价值及其投资/中止阈值.数值结果表明:较大的技术风险,会产生更大的信息生成价值,使得研发的总价值不减反增;风险厌恶态度对企业的研发价值以及研发/中止阈值都有较明显的影响;因研发信息生成价值的存在,使得研发/中止阈值不同于传统净现值(NPV)降为零的阈值.     

A Comparison of the Forecasting Ability of Immediate Price Impact Models

As a consequence of recent technological advances and the proliferation of algorithmic and high‐frequency trading, the cost of trading in financial markets has irrevocably changed. One important change, known as price impact, relates to how trading affects prices. Price impact represents the largest cost associated with trading. Forecasting price impact is very important as it can provide estimates of trading profits after costs and also suggest optimal execution strategies. Although several models have recently been developed which may forecast the immediate price impact of individual trades, limited work has been done to compare their relative performance. We provide a comprehensive performance evaluation of these models and test for statistically significant outperformance amongst candidate models using out‐of‐sample forecasts. We find that normalizing price impact by its average value significantly enhances the performance of traditional non‐normalized models as the normalization factor captures some of the dynamics of price impact. Copyright © 2016 John Wiley & Sons, Ltd.     

逃离“北上广深”背景下一线城市房价涟漪效应研究

本文基于逃离"北上广深"背景,研究了一线城市对二三线城市的房价涟漪效应,并从城市规模大小、距一线城市远近和涟漪效应高低三个方面,对比分析各因素对涟漪效应的影响.研究发现:1)逃离"北上广深"背景下,产业相似度、生活舒适性差距、交通便捷性、住房市场信息距离、社会文化距离、就业增长率、开放度和总储蓄均对涟漪效应表现出显著促进作用;2)二线城市各因素的影响程度均大于三线城市;3)随着与一线城市距离的增加,产业相似度、交通便捷性、就业增长率和开放度的影响呈递减趋势,生活舒适性差距呈递增趋势,住房市场信息距离和总储蓄基本与距离无关;4)随分位数提高,多数变量的影响程度增强,表现出"加速效应".