A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

A formal description of open distributed processing (ODP) trading based on guidelines for the definition of managed objects (GDMO)

New requirements of growing computer networks and information systems have an influence on extended client/server models with increased functionality. This forms the basis for service management in distributed systems which is realized by a service trading concept. This paper studies the requirements derived from the Open Distributed Processing (ODP) Reference Model in order to consider an open service market. Furthermore, it examines management possibilities for describing the service trading scenario. Because of similar architectures and properties ODP services, service offers, types, exporters and traders are mapped onto management components and modeled as managed objects. Therefore, the Guidelines for the Definition of Managed Objects (GDMO) are used. The final concept allows a precise and unambiguous study of the service trading scenario and provides means for exporting and importing of service offers in a distributed environment.     

与客户建立互惠互利长期稳定的营销关系

在我国加入WTO之后，国内钢铁企业与经销商之间如何实现互惠互利，并建立起长期稳定的营销关系。     

An evaluation of possible EU air transport emissions trading scheme allocation methods

The European Commission has been requested by member states to study the incorporation of air transport into their existing emissions trading scheme (ETS). Only CO₂ is to be included, at least initially.     

细节保持的曲面浅浮雕算法

为保持曲面浮雕的细节特征,改善浮雕与背景曲面的过渡效果,提出一种细节保持的曲面浮雕算法.首先采用Canny算子在梯度域定位内外轮廓,获得连续的梯度域;其次提出非线性函数,并用其压缩梯度幅值实现形状压缩;再通过双边滤波算子来保持和增强浮雕细节;最终通过求解积分方程重建曲面浮雕.该算法将背景曲面作为积分方程的优化条件,使浮雕在轮廓处向曲面光滑过渡;在重建过程中利用Fourier变换的微分性质在频域实现方程的精确求解,无需设置迭代收敛条件;通过调整参数可方便地控制浮雕整体变化范围、细节锐化程度以及浮雕与背景曲面的过渡效果.实验结果表明,文中算法参数的几何意义直观,所得曲面浮雕细节清晰,边界过渡自然.     

基于配额制的自备电厂参与新能源消纳日前交易模式

国内风电和光伏等新能源发电迅速发展,而部分地区本地消纳和电网外送能力均有限,当地自备电厂未充分发挥调峰作用,出现日趋严重的弃风弃光现象,因此难以完成配额制目标。为利用市场手段充分调动当地自备电厂参与新能源消纳,促进新能源配额制目标的完成,同时避免因强行完成指标导致的社会整体消纳成本升高的问题,提出了一种基于竞价摘牌的新能源日前交易模式。通过具有引导效应的市场化手段,调动具备自备电厂用户的主动消纳意愿,进而提升配额制目标完成度,并降低整体消纳成本。最后,以新疆地区算例为例,分析验证了所提交易模式在提升配额制目标完成度及在尽量降低社会整体消纳成本方面的价值,为新疆等类似地区电力市场建设提供了一种新的思路。     

基于区块链的分布式光伏就地消纳交易模式研究

目前配电网中分布式光伏发电渗透率越来越高,利用区块链技术的去中心化、难篡改等特点,有助于分布式发电就地或就近消纳,提高配电网运行的经济性。提出了一种基于区块链的光伏就地消纳交易模式,建立了光伏发电用户和分布式光伏聚合商的效益函数,运用Stackelberg博弈模型确定内部电价,通过边缘计算制定最优用电计划,设计了基于信誉值的就地消纳交易机制,对就地消纳程度低的用户进行惩罚,鼓励用户通过可时移负荷消纳光伏出力。配电网仿真结果表明,在采用区块链的交易模式下,配电网的就地消纳情况得到改善,用户的综合效益得到提升。     

计及综合能源系统全寿命周期碳排放和碳交易的电转气设备和光伏联合优化配置

综合能源系统(IES)能够提高能源利用效率,合理配置低碳设备可有效降低碳排放,促进碳中和.提出了一种计及IES全寿命周期碳排放和碳交易机制的电转气(P2G)设备和光伏(PV)容量联合配置方法.对基于某分布式能源站的IES进行设备建模,构建了IES全寿命周期碳排放模型,给出了P2G设备和PV的全寿命周期碳排放计算方法;以基于阶梯罚金机制的碳交易成本、含用水成本和考虑分时电价的购能成本以及设备成本之和最小为优化目标,建立了P2G设备和PV的联合优化配置模型.基于某分布式能源站的实际参数进行算例分析以验证所提方法的正确性和有效性,并探讨了碳交易机制对优化配置结果的影响.     

基于异构能源区块链的综合能源系统交易模型

综合能源系统是对电、气、热、冷能进行统一规划调度的综合系统,顺应了供给侧改革的趋势.目前综合能源系统的用户呈现个体化、分散化的趋势,传统的单向集中式输配电服务模式逐渐不适应这种趋势.基于区块链技术,设计了一种综合能源系统交易模型.采用异构能源区块链的结构模式,利用智能合约建立了基于价格约束的匹配拍卖机制,保障了清洁能源用户的供能优先级,并采用双重认证机制,保障了交易的可靠性.在实验环境下对所设计模型进行分析,仿真结果验证了所提综合能源系统交易模型的有效性和可行性.     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).