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.     

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Does decision quality (always) increase with the size of information samples? Some vicissitudes in applying the law of large numbers.

Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic account for such a seemingly paradoxical effect is offered within a satisficing-choice framework. Accordingly, a choice is only made when the sample contingency describing the relative evaluation of the 2 options exceeds a critical threshold. Small samples, because of the high dispersion of their sampling distribution, facilitate above-threshold contingencies. Across a broad range of parameters, the resulting small-sample advantage in terms of hits is stronger than their disadvantage in false alarms. Computer simulations and experiments support the model predictions. The relative advantage of small samples is most apparent when information loss is low, when the threshold is high relative to the ecological contingency, and when the sampling process is self-truncated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Note of clarification on Scholten and Sherman (2006): Erratum.

Reports an error in "Tradeoffs and Theory: The Double-Mediation Model" by Marc Scholten and Steven J. Sherman (Journal of Experimental Psychology: General, 2006 May, Vol 135[2], 237-261). This article was inadvertently printed with the incorrect title. The original title was "Tradeoffs and Conflict: The Double-Mediation Model." This title highlights the relation between tradeoffs and conflict as investigated by the authors and accounted for by their model. However, readers are asked to refer to the article by the title with which it was printed to facilitate its retrieval.. (The following abstract of the original article appeared in record 2006-06642-006.) Most theories of decision making suggest that, when options imply tradeoffs between their attributes, conflict increases as tradeoff size increases, because greater sacrifices are to be incurred in choosing one option instead of another. An alternative view is that conflict decreases as tradeoff size increases, because stronger arguments can be made for any decision. The authors propose a unified model, the double-mediation model, which combines the mediating effects of sacrifice and argumentation. Our model generally predicts an inverse U-shaped relation between tradeoff size and conflict. Results support this prediction. Also, when the decision situation increases the mediating effect of sacrifice relative to that of argumentation, the relation between tradeoff size and conflict changes in an upward direction; conversely, when the decision situation increases the mediating effect of argumentation relative to that of sacrifice, the relation changes in a downward direction. Results support these predictions as well. Commonalities and differences between our model and other formulations are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

灰局势决策在水利工程开发方案优选中的应用

针对水利工程开发方案优选具有多目标、多层次等特点,建立灰局势决策模型并应用变异系数法确定评价指标权重。利用该模型对灌区改建扩建规划方案进行优选,结果表明这是一种有效的方法。     

Unresolved controversies concerning psychopathy: Implications for clinical and forensic decision making.

At some point in their careers, clinicians who work or consult in forensic and correctional settings will almost certainly encounter individuals who exhibit psychopathic personality features. Because of the widespread use of this disorder to inform legal and clinical decision making, psychologists should be exceedingly familiar with the relevant research literature on this topic before venturing into these settings. This article reviews the empirical bases of several clinically relevant claims and assertions regarding psychopathy and concludes that many areas of research are decidedly more equivocal in their findings than is commonly perceived. Although there is much to be gained by assessing psychopathy in various contexts, clinicians need to be cautious about drawing overzealous and empirically questionable conclusions about an important disorder that also has great potential for abuse. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fast handoff with reducing waste rate approach for wireless resources management in UMTS mobile networks

3G Wideband CDMA systems adopt the Orthogonal Variable Spreading Factor code tree as the channelization codes management for achieving high data rate transmission in personal multimedia communications. It assigns a single channelization code for each accepted connection. Nevertheless, it wastes the system capacity when the required rate is not powers of two of the basic rate. One good solution is to assign multiple codes for each accepted connection but it causes two inevitable drawbacks: long handoff delay and new call setup delay due to high complexity of processing with multiple channelization codes, and high cost of using more number of rake combiners. Especially, long handoff delay may result in more call dropping probability and higher Grade of Service, which will degrade significantly the utilization and revenue of the 3G cellular systems. Therefore, we propose herein an adaptive efficient codes determination algorithm based on the Markov Decision Process analysis approach to reduce the waste rate and reassignments significantly while providing fast handoff. Numerical results demonstrate that the proposed approach yields several advantages, including the lowest GOS, the least waste rate, and the least number of reassignments. Meanwhile, the optimal number of rake combiners is also analyzed in this paper. This research was supported in part by the National Science Council of Taiwan, ROC, under contract NSC-93-2213-E-324-018.     

我国水权交易博弈定价决策机理

结合水权交易双方的效用函数，基于博弈原理及方法分别建立了水权交易准市场阶段和水市场阶段的水权交易博弈定价模型，并对水市场定价模型的纳什均衡解进行了探讨；基于定价相关要素间关系的探讨，解析了准市场阶段和水市场阶段水权交易定价决策机理，结果认为水权交易双方存在利益冲突，并且准市场阶段水权交易定价相对较低，水市场阶段水权交易定价相对较高。     

信息安全风险评估的模糊多准则决策方法

基于ISO/IEC17799标准建立了一个综合的信息系统风险分析框架,并运用模糊多准则决策(FMCDM)方法计算信息安全风险,根据风险等级矩阵(RLM)对信息资产风险进行级别划分,最终建立评估信息资产相关风险的完整模型。     

分类挖掘技术在高校人事管理中的应用

探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型，应用数据挖掘中决策树算法，对高校人力资源数据源中的信息进行分析，发现其中有价值的数据模式，寻找其中存在的关系和规则，对高校人才规划提供比较客观的决策支持。