基于多Agent仿真技术的虚拟股票市场分析

准确地对股票市场进行预测,是股票投资者和政府宏观调控的必然需求.多Agent仿真技术利用复杂系统理论,能够对复杂的社会、经济以及生态等系统进行仿真模拟.本文利用多Agent技术对股市进行建模,并利用MASON工具对股票市场进行仿真.实验结果表明,多Agent仿真技术能够较好地反映股票市场的基本特性,从而为股票投资和宏观调控提供良好的决策支持.     

基于Agent的空中交通系统建模与仿真研究

从系统特性、内在运行机制和外在行为表现等方面对空中交通系统进行了深入分析,提出基于离散事件和连续时间相结合的混合空中交通仿真模型。该模型采用Agent技术表现实现个体微观行为的仿真,集成个体微观行为构成系统宏观性能表现。构建了典型的空中交通系统的Agent模型:飞机Agent和管制员Agent。最后,通过一个起飞和落地的应用验证了基于Agent的空中交通混合仿真在模拟个体微观行为和系统宏观表现方面均具有较高的逼真度。     

基于Agent的交通流微观仿真系统框架研究

目前Agent技术已经成为人工智能技术和计算机软件学的热点研究问题。该文将Agent概念和技术运用到交通流微观仿真系统中，建立了交通流微观仿真系统框架，比较详细地介绍了交通个体Agent的分类和各自的属性，对机动车、非机动车和行人的Agent集合进行了定义，讨论了他们的属性，同时依据交通控制的分层结构，对交通控制Agent进行了分类，对各层的属性进行了研究，最后对影响交通个体Agent行为的外界环境进行了描述，从理论上对Agent在交通仿真中的应用进行了较为完整的研究。     

基于Agent计算金融的计算机仿真研究综述

传统的金融学研究方法为"由上及下",此类方法倚重于总体把握,而忽视个体行为,特别是缺乏个体以及个体与环境之间的互动.基于Agent的计算金融是一种新的研究金融市场行为的工具.首先阐述了金融市场本身的复杂性和传统金融学存在的一些困惑.在这基础上提出了基于Agent的计算金融方法.其次,概要介绍了目前基于Agent的人工金融市场的主要设计方法,并且提出了在设计过程中要注意的一些问题.最后,分析了这种方法相对于传统金融学研究方法的优点和存在的问题,并进一步提出了该领域的未来研究方向.     

基于多Agent的天战系统建模与仿真方法研究

简单介绍了复杂系统理论及其建模方法，分析了天战系统的总体行为特征，指出对天战系统的仿真研究需采用复杂适应系统（CAS）理论的研究方法，即采用基于Agent的建模仿真方法学来开展研究，概述了Agent和多Agent系统的概念及基于多Agent的仿真，并给出了一个基于高层体系结构（HLA）的天战系统的仿真框架，最后展望了将来的研究工作。     

基于自主行为的RoboCup决策算法设计与实现

为了提高决策的速度和智能性，提出一种基于行为的Multi—Agent决策模型，该模型分为个体决策模型和团队决策模型，个体决策模型是为了提高智能体决策的速度，团队决策模型为了提高决策的智能性。这种基于行为的Multi-Agent决策模型已成功地应用到RoboCup仿真机器人系统中，仿真结果说明了该结构和算法的有效性。     

开放Agent社会的框架模型研究综述

开放Agent社会是一种多Agent系统，其中的Agent是由不同所有者开发的，具有不同的目的和利益。Agent的异构性、冲突的个体目标以及可能与系统规范的不一致性是这种系统的主要特征。因此，这种系统的活动需要一个框架模型进行管理，以进行协调控制和引导全局行为的发生，同时保证个体成员的自主性。本文对开放Agent社会的框架模型的研究和应用现状进行了归纳总结，分析了几种有代表性的理论框架和软件实现。最后，提出了现有的问题和未来的研究方向。     

一种基于组织学习的自适应Agent系统体系结构

在复杂适应系统和组织学习理论的基础上，探讨了一种基于组织学习的自适应Agent系统体系结构。在这种体系结构中，通过Agent交互中的适应性行为获取对系统复杂性的认知，而Agent的适应能力则依靠增强学习和动态自组织与重构来实现。文中给出了一个基于组织增长模型的分类器系统算法以及相应的软件实现技术途径。     

基于Agent的城市废物计量收费方案仿真研究

提出一种基于Agent的城市废物计量收费仿真模型.由于城市废物计量收费仿真模型属于复杂适应性系统(CAS),传统的建模仿真方法无法完好地刻画城市废物计量收费仿真模型,而基于多Agent的建模仿真方法(ABS)是当前研究复杂适应性系统的最有利工具之一.有必要进行深入研究.在简单介绍复杂适应性系统、基于多Agent的建模方法的研究概况,以及多Agent建模工具--Repast仿真甲台以及它的结构、主要类库与建模步骤后,以城市废物计茸收费仿真模型为研究对象,进行基于多Agent的建模仿真实验,结果表明基于多Agent的建模仿真方法非常合适复杂适应性系统的研究,具有很大的发展空间.     

基于Agent的分布式系统信任模型仿真

针对当前信任模型仿真缺乏理论支撑,仿真过程描述及建模步骤不规范的问题,将多Agent建模仿真方法应用于信任模型的仿真,建立信任模型Agent仿真过程框架.由信任模型微观机制入手,对个体Agent进行设计,建立Agent实体模型;针对信任模型中Agent之间的交互问题设计基于推荐网的Agent协作算法;在充分考虑系统宏观约束的情况下,建立微观Agent到宏观系统之间的联系.通过实例验证了该方法的有效性.     

基于多agent的虚拟股市仿真研究

复杂适应系统的基本思想认为系统的复杂性起源于其中个体的自适应性。该文借助Multi-agent的建模技术,对真实股市和投资者的行为进行合理的约简,设计出具有自适应能力的股民(agent),通过自适应股民的相互行为,从而构建一个虚拟的股票市场。最后对仿真的结果进行分析并展望了未来的研究方向。     

通过Web统计信息挖掘研究股市反应

通过Web统计信息挖掘研究股市反应是网络金融课题,属于典型的计算机和金融的交叉学科。文中通过挖掘Web股市信息强度,发现当Web股市信息强度变化较小时,股价变动也常常较小,股市相对平静;当Web股市信息强度变化较大时,股价变动常常也较大,股市相对波动。文中提出了基于自适应标准差的Web股市信息强度变化挖掘方法,并使用股市数据进行了验证。该挖掘方法简单有效,有助于了解股市的微观结构。     

基于行为金融学视角的股票市场价格仿真研究

对股票价格走势及其主要影响因素(投资者心理行为)的研究,不仅有助于投资者理解股票市场的运行特点而且还可以给监管当局提供相关的政策建议.文中利用Multi-agents建模技术,对投资者心理行为进行了合理简化,综合考虑了交易制度、宏观经济因素、历史交易信息等因素的影响,构造出了具有自适应能力的投资者(Agent),动态模拟了真实股票市场的运行情况.文章的主要结论为:相对较多的资金投入致使股票价格在较高水平频繁波动;相对过多的投资者也导致股票价格的频繁波动;消极的投资态度引致较低的股票价格水平.文中的仿真方法可以应用到复杂金融衍生品价格形成机制的研究中.     

Decentralized markets and the emergence of housing wealth inequality

Recent studies suggest that the traditional determinants of housing wealth are insufficient to explain its current inequality levels. Thus, they argue that efforts should focus on understanding institutional factors. From the perspective of complex adaptive systems, institutions are more than the ‘the rules of the game’, they also consider the interaction protocols or the ‘algorithm’ through which agents engage in socioeconomic activities. By viewing markets as complex adaptive systems, I develop a model that allows estimating how much housing wealth inequality is attributable to the market institution. It combines virtues from two different modeling traditions: (1) the microeconomic foundations from overlapping-generation models and (2) the explicit interaction protocols of agent-based models. Overall, the model generates prices and housing inequality endogenously and from bottom-up; without needing to impose assumptions about the aggregate behavior of the market (such as market equilibrium). It accounts for economic and institutional factors that are important to housing consumption decisions (e.g., wages, consumption of goods, non-labor income, government transfers, taxes, etc.). I calibrate the model with the British Wealth and Assets Survey at the level of each individual household (i.e., ~25 million agents). By performing counter-factual simulations that control for data heterogeneity, I estimate that, in the United Kingdom, the decentralized protocol interaction of the housing market contributes with one to two thirds of the Gini coefficient. I perform policy experiments and compare the outcomes between an expansion in the housing stock, a sales tax, and an inheritance tax. The results raise concerns about the limitations of traditional policies and call for a careful re-examination of housing wealth inequality.     

The Interplay Between Two Stock Markets and a Related Foreign Exchange Market: A Simulation Approach

There is still much that is unknown about the interactions among financial markets, and about the relationships between stock prices and exchange rates. This topic gains attention during financial crises, and many papers try to find empirical regularities emerging from financial data, or to study contagion processes. In this paper we present a study on the interplay between two stock markets and one foreign exchange market extending the framework provided by the Genoa Artificial Stock Market. There are four different trading strategies, and the agents are divided into two groups: those who trade in the stock markets and those who trade in the FOREX. We studied three market conditions: the FOREX dynamics, the behavior of the two stock markets together with the FOREX, and finally we conducted a what-if analysis for testing the effects of a inflationary monetary shock of one currency affecting all of the three markets.     

Do shocks to G7 stock prices have a permanent effect?: Evidence from panel unit root tests with structural change

There is a plethora of studies that investigate evidence for the behaviour of stock prices using univariate techniques for unit roots. Whether or not stock prices are characterised by a unit root have implications for the efficient market hypothesis, which asserts that returns of a stock market are unpredictable from previous price changes. The extant literature has found mixed evidence on the integrational properties of stock prices. In this paper, for the first time, we provide evidence on the unit root hypothesis for G7 stock price indices using the Lagrangian multiplier panel unit root test that allows for structural breaks. Our main finding is that stock prices are stationary processes, inconsistent with the efficient market hypothesis.     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

遗传程序设计分析股价移动平均及中长期走势*

将遗传程序设计应用到股票价格分析,在股票市场各种因素相互作用与影响很难厘清的情况下,只从个别因素（价格）入手,测试对单一因素预测所能达到的效果;提出了两种预测方法：对不同尺度的股票移动平均线进行预测和对股票价格数据进行平滑预处理之后所进行的中长期预测。通过遗传程序设计算法,寻找前几个时间单位的股票价格对本期股票价格影响的经验公式,以期反映价格变动的规律。计算机实验模拟表明,该方法对于平均线的预测和中长期预测有较好的效果。     

基于金融文本情感的股票波动预测

股票市场的情绪可以在一定程度上反映投资者的行为并影响其投资决策。市场新闻作为一种非结构性数据,能够体现并引导市场的大环境情绪,与股票价格一同成为至关重要的市场参考数据,能够为投资者的投资决策提供有效帮助。文中提出了一种可以准确、快速地建立针对海量新闻数据的多维情绪特征向量化方法,利用支持向量机(Support Victor Machine,SVM)模型来预测金融新闻对股票市场的影响,并通过bootstrap来减轻过拟合问题。在沪深股指上进行实验的结果表明,相比于传统模型,所提方法能够将预测准确度提高约8%,并在3个月的回测实验中获得了6.52%的超额收益,证明了其有效性。     

Predicting published news effect in the Brazilian stock market

The Efficient Market Hypothesis states that the value of an asset is given by all information available in the present moment. However, there is no possibility that a single financial analyst be aware of all published news which refers to a collection of stocks in the moment they are published. Thus, a computer system that applies text mining techniques and the GARCH model for predicting the volatility of financial assets may helps analysts and simple investors classifying automatically the news which cause the higher impact on stock market behavior. This work has the goal of creating a method for analyzing Portuguese written news’s content about companies that have their stocks negotiated in a stock market and trying to predict what kind of effect these news will cause in the Brazilian stock market behavior. Also, it was demonstrated in this study that it is possible to find out whether certain news may cause a considerable impact on prices of a negotiated stock.