基于GNP的多Agent人工股市模型

结合奥地利学派的经济思想，本文介绍了一种新的基于GNP算法的多Agent人工股市模型。该模型采用GNP算法来模拟交易个体的行为模式，进化他们的决策规则；同时在设计上强化Agent的异质性，并利用GA算法来优化模型参数。仿真结果表明，GNP-ASM模型表现出很好的统计性能，能够体现真实股市的一些基本特征。     

基于多代理的智能化远程教学模型

利用多代理技术建立了一个智能化的远程教学模型。该模型可根据学生的情况建立个性化的学习环境,从而实现学生按需学习和教师因材施数。文中详细描述了利用超媒体技术建立的知识结构模型以及利用多代理技术实现的教学代理的基本结构,并给出了主要的智能化模型。     

一个基于多代理的并行工程设计系统模型研究

并行工程设计方法试图在产品的早期设计时期就考虑产品生命周期中的各方面因素,但是要真正实现却是很困难的,该文给出了一个基于多代理的并行工程设计系统框架,来解决“考虑制造的设计（ＤＦＭ）”这一总理２,同样地,并行工程的其它考虑,如可装配性,可维护性与可服务性都能以同样的方式集成到这个框架中来。     

人工神经元BP网络在股市预测方面的应用

介绍了人工神经元网络在经济领域的应用，主要探讨BerndFreisleben的研究方法，即利用神经BP网络对股票市场股份走势进行预测的方法，重点对利用各种不同网络结构和网络参数所得预测结果进行分析。提出了综合股票历史价格和其他经济因素的精确预测方法。     

基于Java卡的移动代理系统安全模型

本文给出一个基于Java卡自行开发的移动代理系统JC-MAS，并从通信过程、主机执行环境和代理程序三方面研究系统安全策略，针对使用Java卡构建的JC-MAS安全模型设计了详细解决方案。     

基于多代理体的供应链管理模型

首先对供应链管理进行功能模块划分，接着，在结合供应链的功能模块需求和代理体特性的基础上提出了一个基于多代理的供应链管理模型，阐述了模型的总体控制策略和各个构成模块的功能。     

一个基于对象代理模型的多表现地理信息系统

文中提出了一种基于对象代理模型的实现多表现GIS的新方法。通过地理对象的代理对象来定义多表现,因此代理对象可以用来表示对象的视角多样性和角色多样性。通过对象更新迁移可以支持动态分类和系统一致性维护。另外,对象及其代理对象之间的双向指针使得跨类查询变得非常容易,从而可以扩展查询的范围。文中实现了一个基于对象代理模型的多表现GIS原型,性能测试表明该方法比传统的方法更加有效。     

基于生物免疫学的多代理IDS模型设计

在IDS中,一个Agent可以在一个主机上完成一项专门的安全监视功能。由于Agent是独立运行的实体,因此可以在不改变其他部件且不需要重新启动IDS时加入、删除和重新配置。也可以用一组Agent来分别完成简单的功能,彼此交换信息以得出更复杂的结果。因此,可以使用Agent技术构造分布式IDS。     

基于约束的授权代理模型

在对授权代理模型深入分析的基础上,提出了一种基于约束的用户-用户授权代理模型,给出了该模型的构成要素和体系结构,并且详细描述了该模型的职责分离约束以及代理判定关系,并在判定关系中描述了角色基数约束、用户基数约束和权限基数约束,最后给出了代理实施策略.     

网络数据库角色代理安全模型

网络数据库安全性是一个非常复杂的问题。以Oracle数据库为例，讨论了网络环境下数据库系统的安全性策略，提出了“角色代理模型”，加强了数据库安全。     

An artificial stock market

The Santa Fe Artificial Stock Market consists of a central computational market and a number of artificially intelligent agents. The agents choose between investing in a stock and leaving their money in the bank, which pays a fixed interest rate. The stock pays a stochastic dividend and has a price which fluctuates according to agent demand. The agents make their investment decisions by attempting to forecast the future return on the stock, using genetic algorithms to generate, test, and evolve predictive rules. The artificial market shows two distinct regimes of behavior, depending on parameter settings and initial conditions. One regime corresponds to the theoretically predicted rational expectations behavior, with low overall trading volume, uncorrelated price series, and no possibility of technical trading. The other regime is more complex, and corresponds to realistic market behavior, with high trading volume, high intermittent volatility (including GARCH behavior), bubbles and crashes, and the presence of technical trading. One parameter that can be used to control the regime is the exploration rate, which governs how rapidly the agents explore new hypotheses with their genetic algorithms. At a low exploration rate the market settles into the rational expectations equilibrium. At a high exploration rate it falls into the more realistic complex regime. The transition is fairly sharp, but close to the boundary the outcome depends on the agents’ initial “beliefs”—if they believe in rational expectations they occur and are a local attractor; otherwise the market evolves into the complex regime. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Bifurcations in a stock market model

A theoretical framework is laid out, where a Stock Exchange is represented as a process under decentralized control. Attention is devoted to a specific case, in which the trading activity is described by a second order dynamical system. Three economically significant modes of behavior are identified. The stock market can (1)_adjust to a stable equilibrium, (2) approach a stable limit cycle, (3) diverge to infinity. The transition from mode (1) to mode (2) is a supercritical Hopf bifurcation, whereas the transition from mode (2) to mode (3) is a homoclinic bifurcation.     

A Bayesian regularized artificial neural network for stock market forecasting

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.     

基于隐马尔可夫模型的股票价格预测组合模型

提出了一种用于股票价格预测的人工神经网络(ANN),隐马尔可夫模型(HMM)和粒子群优化算法(PSO)的组合模型-APHMM模型.在APHMM模型中,ANN算法将股票的每日开盘价、最高价、最低价与收盘价转换为相互独立的量并作为HMM的输入.然后,利用PSO算法对HMM的参数初始值进行优化,并用Baum-Welch算法进行参数训练.经过训练后的HMM在历史数据中找出一组与今天股票的上述4个指标模式最相似数据,加权平均计算每个数据与它后一天的收盘价格差,则今天的股票收盘价加上这个加权平均价格差便为预测的股票收盘价.实验结果表明,APHMM模型具有良好的预测性能.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.     

基于Agent的股市随机过程方法预测

利用Swarm仿真平台和Agent建模技术,结合马尔可夫过程对证券市场的股指进行了研究,提出多Agent变区间马尔可夫预测方法,并对模型进行了仿真,仿真结果验证了模型的有效性。     

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.     

Intelligent agent-assisted adaptive order simulation system in the artificial stock market

Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.     

A double-threshold GARCH model of stock market and currency shocks on stock returns

International integration of financial markets provides a channel for currency movements to affect stock prices. This paper applies a four-regime double-threshold GARCH (DTGARCH) model of stock market returns to investigate empirically the effects of daily currency movements on five stock market returns, namely in Taiwan, Singapore, South Korea, Japan and the USA. The asymmetric reactions of the mean and volatility stock returns in five markets to stock market and foreign exchange news are investigated using linear and nonlinear models. We discuss a four-regime DTGARCH model, which allows for asymmetry in both the conditional mean and conditional variance simultaneously by using two threshold variables to analyze stock market reactions to different types of information (that is, positive and negative news) that are generated from stock and foreign exchange markets. By applying the four-regime DTGARCH model, this paper finds that the interactions between the information of stock and foreign exchange markets lead to asymmetric reactions of stock returns and their associated variability. The empirical results show that international fund managers who invest in newly emerging stock markets need to evaluate the value and stability of domestic currencies as part of their stock market investment decisions.     

Forecasting daily stock market return using dimensionality reduction

In financial markets, it is both important and challenging to forecast the daily direction of the stock market return. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Three mature dimensionality reduction techniques, including principal component analysis (PCA), fuzzy robust principal component analysis (FRPCA), and kernel-based principal component analysis (KPCA) are applied to the whole data set to simplify and rearrange the original data structure. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data using each of the three different dimensionality reduction methods. Artificial neural networks (ANNs) are then used with the thirty-six transformed data sets for classification to forecast the daily direction of future market returns. Moreover, the three different dimensionality reduction methods are compared with respect to the natural data set. A group of hypothesis tests are then performed over the classification and simulation results to show that combining the ANNs with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANNs gain significantly higher risk-adjusted profits than the comparison benchmarks, while also being slightly higher than those strategies guided by the forecasts based on the FRPCA and KPCA models.