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.     

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing insider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading (using actual insider trading allegation by shareholders as the proxy) from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct.     

Computer crimes: theorizing about the enemy within

A majority of computer crimes occur because a current employee of an organization has subverted existing controls. By considering two case studies, this paper analyzes computer crimes resulting because of violations of safeguards by employees. The paper suggests that various technical, procedural and normative controls should be put in place to prevent illegal and malicious acts from taking place. Ultimately a good balance between various kinds of controls would help in instituting a cost-effective means to make both accidental and intentional misconduct difficult. This would also ensure, wherever possible, individual accountability for all potentially sensitive negative actions.     

数字货币交易匿名性与监管的博弈分析

通过利用区块链等技术,数字货币作为一种新型货币资产推动了数字经济的发展。数字货币交易平台在数字货币用户对私有数字货币使用中占有重要角色。现有的数字货币交易平台可能存在泄露用户个人隐私的风险,为不法分子提供参与“空投”事件、勒索、骗局等各种异常交易行为的条件;数字货币交易平台对用户强匿名性保护也会增加监管部门对于数字货币交易情况的监管难度。因此数字货币交易平台选择对用户的匿名性保护的强弱不仅需要考虑用户的个人隐私安全,而且要考虑如何符合监管部门对其监管工作的要求。基于对数字货币交易平台和监管部门之间关系的分析,建立演化博弈模型,对数字货币交易平台与监管博弈模型进行定义和构建,根据数字货币交易平台与监管部门收益矩阵求解复制动态方程并得出均衡解。通过构建雅可比矩阵对演化系统在不同参数取值下的均衡点进行稳定性分析,并分析在不同情况下,数字货币交易平台与监管部门的演化稳定选择及策略。结合Matlab实验环境,对数字货币交易平台的强弱匿名性保护策略和监管部门的强弱监管策略进行仿真实验,验证博弈模型的准确性。结合实验结果,提出对数字货币交易平台的监管建议,为我国将来开展数字货币交易平台的监管工作提供...     

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).     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Using rough set to support investment strategies of real-time trading in futures market

Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rule-based trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.     

Security Lessons Learned from Société Générale

The huge trading losses in 2007 and 2008 at Societe Generale were caused by insider Jerome Kerviel's unauthorized actions. We can learn many lessons about the technological aspects of security from this insider attack, as well as some that we might hope to learn, but can't.     

Leader’s dilemma game: An experimental design for cyber insider threat research

One of the problems with insider threat research is the lack of a complete 360° view of an insider threat dataset due to inadequate experimental design. This has prevented us from modeling a computational system to protect against insider threat situations. This paper provides a contemporary methodological approach for using online games to simulate insider betrayal for predictive behavioral research. The Leader’s Dilemma Game simulates an insider betrayal scenario for analyzing organizational trust relationships, providing an opportunity to examine the trustworthiness of focal individuals, as measured by humans as sensors engaging in computer-mediated communication. This experimental design provides a window into trustworthiness attribution that can generate a rigorous and relevant behavioral dataset, and contributes to building a cyber laboratory that advances future insider threat study.     

基于交易特征对以太网多类型非法账户的分析与预测

日益频繁的非法交易行为妨害以太坊安全交易,针对电子货币的匿名性使得非法交易行为难于跟踪分析问题。以太坊平台交易数据作为数据源,以被标记得非法账户和未标记的正常账户数据集作为训练集,利用交易数据的特征属性为构造基础,通过CatBoost算法对其中包含多种类型的非法账户进行整体预测。其过程通过T-SNE算法实现交易特征的降维可视化,采用多倍交叉验证,引入SHAP value因子判断特征影响的正负属性,所建立模型的预测效果准确率达到了94.29%,感受者曲线下面积（AUC）数值的评估度量达到了0.984 6。建议的方案能较为准确地预测以太坊交易平台上存在的非法行为,有效改善基于区块链的交易环境。     

证券公司集中监控系统设计

证券市场是完整的市场体系的重要组成部分，对整个经济的运行具有重要影响.证券交易系统的安全稳定运行是证券业界非常关注的课题.本文设计一个可以快速实时响应、灵活监控证券公司各个交易系统的网络连通和业务功能运行情况的监控系统（RSCMS），以提高部门的实时监控效率，并为系统的运行情况提供数据评估的依据.本文研究了监控系统关键技术的解决方法，并提供了在证券公司运行的成功案例     

A multi‐agent system for analyzing the effect of information on prediction markets

Prediction markets have been shown to be a useful tool for forecasting the outcome of future events by aggregating public opinion about the event's outcome. In this paper, we investigate an important aspect of prediction markets—the effect of different information‐related parameters on the behavior of the traders in the market. We have developed a multi‐agent based system that incorporates different information‐related aspects including the arrival rate of information, the reliability of information, the penetration or accessibility of information among the different traders, and the perception or impact of information by the traders. We have performed extensive simulations of our agent‐based prediction market for analyzing the effect of information‐related parameters on the traders' behaviors expressed through their trading prices, and compared our agents' strategies with another agent‐based pricing strategy used in prediction markets called the zero intelligence strategy. Our results show that information‐related parameters have a significant impact on traders' beliefs about event outcomes, and, frequent, reliable information about events improves the utilities that the traders receive. Overall, our work provides a better understanding of the effect of information on the operation of prediction markets and on the strategies used by the traders in the market. © 2011 Wiley Periodicals, Inc.     

一种基于Paxos算法的证券交易系统内存复制方法研究

近年来随着高速网络技术的发展与高频交易需求的增加,提升交易速度成为电子商务交易提供者的重要关 切。当前交易系统通常采用基于共享存储的主备机复制方法来保证高可用性与数据持久性,但因其存在持久化的性 能瓶颈而无法进一步降低延迟。为此,提出一种基于Paxos算法的内存数据复制方法,即通过消息传递完成主备机复 制,以保证结点间数据的一致性,容忍可能发生的良性故障;并以证券交易系统场景为例对其进行分析。实验结果表 明,相比基于共享存储的主备机复制,该方法在万兆以太网环境下可将交易系统订单处理延迟由毫秒级降至百微秒 级,并在主机故障时正确地完成热备切换。     

基于深度神经网络的驾驶员违章行为识别方法

驾驶员的违章行为是造成交通事故的主要原因之一,利用摄像头实时监控行驶过程中的驾驶员违章行为是一个减少交通事故的有效方法.本文提出一种通过深度神经网络的驾驶员违章行为识别方法.首先,利用Deep-Pose检测驾驶员身体关键点,接着,基于这些关键点提取动作特征,然后使用最近邻分类器识别典型违章行为.实验证明,本文的方法对于典型的违章行为是有效的.     

面向比特币交易网络的拓扑结构可视探索方法

分析比特币交易网络有助于人们理解交易者在比特币交易中的交易模式.比特币交易网络的匿名性和其巨大的规模使得用户很难在分析前对整个交易网络产生大致的认知.提出了一种基于拓扑结构推荐的比特币交易网络可视分析方法.核心思想是为每个节点生成一个向量化表达,在用户交互的基础上,所提算法即可检测一系列相似的结构.案例分析证明了系统能够支持用户对比特币交易中的交易模式进行探索和分析.     

The adoption of consortium B2B e-marketplaces: An exploratory study

Despite the considerable number of electronic B2B marketplaces formed and the benefits cited as arising from their use, many have gone out of business. This exploratory study seeks to provide a qualitative exposition of the specific factors influencing the adoption of consortium-owned B2B e-marketplaces. The study is based upon case studies of twelve companies trading through three different consortium B2B e-marketplaces. Twenty-six specific factors are identified and their impact on adoption is discussed. The identification of a significant number of factors specific to this domain provides real meaning and depth to those interested in the future of e-marketplaces. In particular, the factors identified provide those that operate such e-marketplaces with a detailed and actionable understanding of the issues they should address in order to survive, and provide users or potential users of consortium marketplaces with a practical framework with which to assess individual marketplaces. The factors can also form the basis of future studies of other types of marketplaces and of quantitative studies of adoption.     

Do high-frequency fleeting orders exacerbate market illiquidity?

This paper investigates whether fleeting orders account for market illiquidity. By discussing relevant trading strategies, our study suggests that fleeting orders serve for market making and contribute to market liquidity. Moreover, fleeting orders do not distort price accuracy and are not the outcome of illegal manipulation. We then empirically examine fleeting orders using a NASDAQ ITCH dataset. Our results indicate that fleeting orders have very small effects on market illiquidity and account for neither the amplification of price impact nor the decrease of revenues to liquidity providers. In summary, fleeting orders are not the trigger of market illiquidity and thus should not be considered as “spoofing” defined by the Dodd–Frank Act.     

Assessing insider threats to information security using technical,behavioural and organisational measures

The UK government took a bruising in the headlines (Sep 2008) after a Home Office contractor lost a USB stick containing unencrypted data on all 84,000 prisoners in England and Wales. As a result, the Home Office terminated the £1.5 million contract with the management consultancy firm.The world woke up to the largest attempted bank fraud ever when the UK’s National Hi-Tech Crime Unit foiled the world’s largest potential bank robbery in March 2005. With the help of the security supervisor, thieves masquerading as cleaning staff installed hardware keystroke loggers on computers within the London branch of a Japanese bank, to steal £220m.It is indeed sobering to imagine that any organisation could fall victim to such events and the damage an insider can do. The consulting firm lost the contract worth £1.5 million due to a small mistake by an employee. The London branch of the Japanese Bank would have lost £220 million had not the crime been foiled.Insider threat is a reality. Insiders commit fraud or steal sensitive information when motivated by money or revenge. Well-meaning employees can compromise the security of an organisation with their overzealousness in getting their job done. Every organisation has a varied mix of employees, consultants, management, partners and complex infrastructure and that makes handling insider threats a daunting challenge. With insider attacks, organisations face potential damage through loss of revenue, loss of reputation, loss of intellectual property or even loss of human life.The insider threat problem is more elusive and perplexing than any other threat. Assessing the insider threat is the first step to determine the likelihood of any insider attack. Technical solutions do not suffice since insider threats are fundamentally a people issue. Therefore, a three-pronged approach - technological, behavioural and organisational assessment is essential in facilitating the prediction of insider threats and pre-empt any insider attack thus improving the organization’s security, survivability, and resiliency in light of insider threats.     

An Empirical Method for Selecting Software Reliability Growth Models

Estimating remaining defects (or failures) in software can help test managers make release decisions during testing. Several methods exist to estimate defect content, among them a variety of software reliability growth models (SRGMs). SRGMs have underlying assumptions that are often violated in practice, but empirical evidence has shown that many are quite robust despite these assumption violations. The problem is that, because of assumption violations, it is often difficult to know which models to apply in practice. We present an empirical method for selecting SRGMs to make release decisions. The method provides guidelines on how to select among the SRGMs to decide on the best model to use as failures are reported during the test phase. The method applies various SRGMs iteratively during system test. They are fitted to weekly cumulative failure data and used to estimate the expected remaining number of failures in software after release. If the SRGMs pass proposed criteria, they may then be used to make release decisions. The method is applied in a case study using defect reports from system testing of three releases of a large medical record system to determine how well it predicts the expected total number of failures.