知情交易者和不知情交易者的策略互动

本文利用EEOW模型结合高频交易数据,对中国股票市场上知情交易者和不知情交易者的交易策略互动进行了实证研究.研究发现:在中国股票市场上,不知情交易者的交易行为对知情交易者没有影响,但是知情交易者的交易行为却对不知情交易者有显著的影响.当预期到市场上有众多知情交易者时,不知情交易者就减少交易以降低与知情交易者发生交易的概率.这一点与传统模型中的"噪声"交易者假设是不一致的.本文的结论对于今后构建更加贴近实际情况的交易策略模型有很好的指导意义.     

具有内部交易的多头交易博弈

以Kyle的单期模型为基础,针对交易市场的参入者掌握的信息集,将交易者分为内部交易者、若干类型的外部交易者、噪声交易者和做市商.从而建立具有内部交易的多头交易博弈随机模型,并求得该模型的线性纳什均衡解.由此发现公共信息存在,有利于外部交易者和噪声交易者,却不利于内部交易者操纵交易市场,因此,公共信息的存在对市场稳定和发展具有重要意义.     

信息不对称下带有“随机压力”定价规则的连续时间模型

本文是对Back(1992)和Cho(2003)关于内部交易模型的拓展.在金融市场中一共有3类人:内部交易者,不知情交易者和做市商.考虑一类比Cho研究的模型更广的定价规则.主要用动态规划的方法,证明了当内部交易者是风险中性时,定价规则中"随机压力"消失,均衡价格还是仅依赖市场上累计交易量.相应地,本文的结论推广了Back和Cho在经典模型中的结论.     

P2P电子商务中信任模型研究

为了提高P2P电子交易的安全性,提出了基于模糊理论的信任管理模型.通过类群来初始化信任向量及信任评估,提高了评估效率.通过模糊综合评判方法,较为全面地考察了影响信任的多种因素,使得评估结果更加客观、真实.基于模糊贴近度的概念来实现信任更新,并在推荐因子的更新中融入交易金额和交易时间,客观地反映了信任的动态性和交易的上下文特性.实验仿真及分析表明,与传统方法相比,本方法可以有效地提高评估的可靠性及可用性.     

完全信息下有限个风险喜好型内部交易者的交易行为研究

张首元和巩馥洲等,针对一类特定的混合交易策略研究了具有完全信息的两个内部交易者的行为特征.依据张首元所建立的具有完全信息的有限个内部交易者的类似模型,我们研究了风险喜好型内部交易者的行为特征.我们首次发现:这些内部交易者可能的均衡混合交易策略中分量的参数是相等的,因而其分量具有同分布性;当内部交易者的个数小于等于6时,混合均衡策略的参数具有存在唯一性;而当内部交易者的人数大于等于7时,由于市场的激烈竞争,导致了混合均衡策略不存在.进一步,利用数值模拟方法我们分析了该模型中经济金融变量的变化特征及其经济金融含义.     

具有不完全信息的内部交易动态博弈

在Kyle模型中的线性均衡假设进行了修正的基础上,针对内部交易者只具有资产价值不完全信息情况,建立两期风险厌恶型内部交易均衡模型,并求得该模型的子博弈纳什均衡解.由此发现资产价值不完信息中噪音对市场干扰程度愈小(波动程度愈小),就愈有利于内部交易者的收益;内部交易者的交易就愈活跃;交易均衡价格包含资产价值信息就愈多.     

内部交易行为对股票价格影响机制研究

上市公司的内部人或者机构投资者通常拥有比其他投资者更多的信息,诸多实证研究已经证明内部交易者能够利用私有信息在市场中获利.我国自2014年起开始在新三板市场中实施做市商制度,在这一大背景下,主要对内部交易者的交易行为进行了理论预测性研究.在经典的信息模型框架下,通过讨论股票价格的鞅性,建立了一种带有预防对冲策略控制系数的风险厌恶模型.模型表明内部交易者可以充分利用私有信息对做市商的定价进行预测,并且有针对性的选择交易策略以获利.     

中国证券市场知情交易概率的动态马尔科夫状态转移模型研究

对指令驱动市场知情交易的研究是近年来的热点问题。常用的EKOP模型存在一些缺陷,本文放宽了EKOP模型关于日内信息均匀释放以及交易者行为独立性的假设,用动态的马尔科夫状态转移模型对该模型进行了改进,并检验了改进后的知情交易概率模型在中国证券市场的适用性。通过模拟数据以及对中国证券市场交易数据的实证研究发现动态的马尔科夫状态转移模型克服了EKOP模型受买卖方数据影响而产生的系统偏误,估计的知情交易概率更符合事后检验。     

代表性启发式交易者市场微观结构

本文研究了一个市场,其中所有的交易者都是代表性启发式交易者,他们过于重视新信息和当前价格.代表性启发法增加了市场深度,增加了价格的信息量,降低了知情交易者的期望效用.启发式交易导致价格对新信息反应过度,知情交易者作为动量交易者的期望效用低于不知情交易者作为反转交易者的期望效用.在有内生噪声交易者的市场中,除了相似的结论外,代表性启发法增加了噪声交易并增加了价格波动性.     

证券市场正反馈交易与收益自相关

正反馈交易是非理性投资者的一种交易策略,正反馈交易者通常依据证券前一期收益的高低决定其当期买卖行为。在正反馈交易存在的情况下,证券市场收益会表现出不同于有效市场假设所假定的特征,使证券市场表现出超常的波动性。本文建立了一个正反馈交易者和理性交易者参与的市场模型,分析了该市场中证券收益时间序列呈现出的正自相关性。     

Robust dynamic pairs trading with cointegration

This paper investigates the robust optimal pairs trading using the concept of equivalent probability measures and a penalty function associated with the confidence in parameter estimates when the parameters in the drift term of the continuous-time cointegration model are estimated with errors. A closed-form solution is derived for the robust pairs trading rule. We compare the robust pairs trading rule against its non-robust counterpart using simulations and real data. The robust strategy is empirically more stable and less volatile.     

协整模型的配对交易策略优化

对基于协整理论的配对交易策略进行了改进.改进后的模型利用计算机能够快速循环运算的特点,循环查找最优配对组合与建仓阈值,使模型能够快速运用到各类资产及多种数据频率的配对交易中,具有根据数据变化进行自我动态修正的功能.     

卖空限制下知情交易的测度及识别研究

经典的测量知情交易概率的模型默认交易者可以无限制的按照私有信息进行卖空交易,而目前我国股票市场存在卖空限制,直接将经典模型应用到我国股票市场时会使测量结果出现偏差。考虑到我国股票市场现状,本文在经典的知情交易概率模型中引入两个卖空限制参数,构建了本文的SC-TPIN模型。通过对融券标的中发生利空消息的股票样本进行实证分析,证实了本文构建的SC-TPIN模型估计出的结果与实际情况相符合。本文还以SC-TPIN模型估计出的SCTPIN值为参照,基于样本股票的低频数据构建了知情交易识别指标组,并使用数据挖掘中的支持向量机算法、KNN算法及Logit模型对黑白样本的知情交易高低情况进行识别比较,构建知情交易识别体系,发现使用支持向量机算法识别全样本的正确率达到了89%,识别效果较理想。     

金融系统的非线性分析:交易量对股价波动的非线性影响

如何研究股价波动和成交量之间的关系一直是金融系统研究中感兴趣的话题.Lamoureux 和 Lastrapes 认为选择日交易量度量每天流入市场的信息量是合理的,但他们假定交易量对波动率的影响是线性的.提出部分非线性GARCH模型分析交易量对股票市场波动率的影响,基于GARCH模型局部线性化非参数似然估计方法,对中国证券市场股票价格和交易量数据进行实证研究.结果表明,交易量对股价波动的影响具有显著的非线性性.     

Risk-Sensitive Dynamic Asset Management

This paper develops a continuous time portfolio optimization model where the mean returns of individual securities or asset categories are explicitly affected by underlying economic factors such as dividend yields, a firm's return on equity, interest rates, and unemployment rates. In particular, the factors are Gaussian processes, and the drift coefficients for the securities are affine functions of these factors. We employ methods of risk-sensitive control theory, thereby using an infinite horizon objective that is natural and features the long run expected growth rate, the asymptotic variance, and a single risk-aversion parameter. Even with constraints on the admissible trading strategies, it is shown that the optimal trading strategy has a simple characterization in terms of the factor levels. For particular factor levels, the optimal trading positions can be obtained as the solution of a quadratic program. The optimal objective value, as a function of the risk-aversion parameter, is shown to be the solution of a partial differential equation. A simple asset allocation example, featuring a Vasicek-type interest rate which affects a stock index and also serves as a second investment opportunity, provides some additional insight about the risk-sensitive criterion in the context of dynamic asset management. Accepted 10 December 1997     

大额资产清仓的自动化交易策略

市场的机构投资者经常需要清仓手中持有的大额资产, 因此清仓的交易策略成为了关心的问题. 以工商银行的股票为例,给出适用于计算机执行的自动化清仓策略. 首先将高频的工商银行股票历史数据在每个交易日分别划分出48个交易期, 将问题简化为处理每个交易日交易期的数据. 在此基础上, 综合考虑用神经网络模拟预测清仓时股票价格随时间下降的风险和用信息流理论模型衡量的价格冲击和交易时刻, 并通过优化模型得到清仓持续的交易日天数. 此后, 再制定出每个交易日的具体自动化交易策略.在制定日内交易策略 时, 首先用神经网络对交易时刻做出预测, 然后综合考虑使用 VWAP 预测出的交易量和通过 Kalman 滤波方法修正过的期权定价公式预测出的各时刻股票的初始价格, 最终给出详细的交易策略及交易的成本.     

Constrained optimization involving expensive function evaluations: A sequential approach

This paper presents a new sequential method for constrained nonlinear optimization problems. The principal characteristics of these problems are very time consuming function evaluations and the absence of derivative information. Such problems are common in design optimization, where time consuming function evaluations are carried out by simulation tools (e.g., FEM, CFD). Classical optimization methods, based on derivatives, are not applicable because often derivative information is not available and is too expensive to approximate through finite differencing.The algorithm first creates an experimental design. In the design points the underlying functions are evaluated. Local linear approximations of the real model are obtained with help of weighted regression techniques. The approximating model is then optimized within a trust region to find the best feasible objective improving point. This trust region moves along the most promising direction, which is determined on the basis of the evaluated objective values and constraint violations combined in a filter criterion. If the geometry of the points that determine the local approximations becomes bad, i.e. the points are located in such a way that they result in a bad approximation of the actual model, then we evaluate a geometry improving instead of an objective improving point. In each iteration a new local linear approximation is built, and either a new point is evaluated (objective or geometry improving) or the trust region is decreased. Convergence of the algorithm is guided by the size of this trust region. The focus of the approach is on getting good solutions with a limited number of function evaluations.     

凸约束优化问题的带记忆模型信赖域算法

本文我们考虑求解凸约束优化问题的信赖域方法 .与传统的方法不同 ,我们信赖域子问题的逼近模型中包括过去迭代点的信息 ,该模型使我们可以从更全局的角度来求得信赖域试探步 ,从而避免了传统信赖域方法中试探步的求取完全依赖于当前点的信息而过于局部化的困难 .全局收敛性的获得是依靠非单调技术来保证的     

Global Convergence of a Nonmonotone Trust Region Algorithm with Memory for Unconstrained Optimization

In this paper, we consider a trust region algorithm for unconstrained optimization problems. Unlike the traditional memoryless trust region methods, our trust region model includes memory of the past iteration, which makes the algorithm less myopic in the sense that its behavior is not completely dominated by the local nature of the objective function, but rather by a more global view. The global convergence is established by using a nonmonotone technique. The numerical tests are also given to show the efficiency of our proposed method.     

The informational content of insider trading disclosures: empirical results for the Polish stock market

In this paper, we try to answer the question as to whether insider trading disclosures convey valuable information to market participants, valuable in the sense of the profitability of an investment strategy that faithfully mirrors insider behaviour. Our interest in this subject is limited to the case of announcements concerning insider transactions issued over a 6 year-period on the Warsaw Stock Exchange (WSE). Initially, we use event study methodology to check whether insider trading disclosures are accompanied by a performance of stock returns as well as trading volume. Two different models generating expected returns (expected volume) are employed to verify the robustness of our results. The first of these is the regime switching model, with the results then being recalculated by using a GARCH-type model which seem to be most useful for dealing with some of the inconvenient statistical properties of stock return and trading volume data. Afterwards, a technique based on the reference return strategies is used to examine whether or not outsiders who imitate insider behaviour are able to profit from it. The major findings are as follows: firstly, announcements about the sale of stocks by insiders convey no information to market participants. Secondly, a statistically significant market response to insider disclosures of purchases of stocks in their own company can be observed in the three days prior to the announcement release for both return as well as trading volume series, and finally, outsiders who purchased stocks previously bought by insiders experience negative returns whereas outsiders disposing of stocks previously sold by insiders earned a return of 8.57% over the 6 month-period.