城市出租车起步价格模型分析

以城市出租车起步里程改革为研究对象,针对出租车2 km起步的租价体系展开讨论.为利用敏感度测量法制定合适的租价,实施了问卷调查,利用回收问卷所得到的数据分析了出租车2 km起步时其起步价格体系中的下限价格、上限价格、无差别价格、最小抵触价格、价格承受带和价格可接受区域.并进一步使用Logit型PSM函数建立了具有竞争力的租价体系下的市场规模模型,最终得到出租车2 km起步时的最优起步价格.     

基准地价系数修正法评估宗地地价的若干问题

基准地价是经各地政府确认并公布的,对应于一定的区域、用途的土地平均价格,是政府宏观调控土地市场的重要杠杆．针对多数城镇公布的基准地价,文中分析了基准地价内涵,讨论基准地价系数修正法的评估思路、宗地地价的影响因素,提出新的修正系数计算方法,以便更准确、客观地评估宗地地价．     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

数据挖掘技术在零售业中的应用

利用数据挖掘(DM)技术,从零售业的各种应用系统中采集数据,经条件分类,放到数据仓库里,进行数据多维度加工,最终透过数据找出人与物间的规律,从而更好地为客户提供服务.最后还谈到了数据挖掘在零售业中应用的广阔前景.     

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.