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.     

基于复杂网络的证券市场智能建模与分析

股票市场作为金融系统的重要组成部分,是一个典型的具有结构复杂性和节点复杂性的复杂网络系统.作为拆分和了解复杂网络的有力工具,社团结构分析被广泛应用于社交网络、物流网络等多种复杂网络系统,并取得了突破性成果.论文采用Pearson相关系数来度量中国A股市场中股票价格波动的相关关系,构建股票市场加权网络,利用改进型社团相似性指标,选定了股票市场时序动态加权网络的步长与社团划分算法,并对社团结构进行了简要分析.     

基于复杂网络的证券市场智能建模与分析

股票市场作为金融系统的重要组成部分,是一个典型的具有结构复杂性和节点复杂性的复杂网络系统.作为拆分和了解复杂网络的有力工具,社团结构分析被广泛应用于社交网络、物流网络等多种复杂网络系统,并取得了突破性成果.论文采用Pearson相关系数来度量中国A股市场中股票价格波动的相关关系,构建股票市场加权网络,利用改进型社团相似性指标,选定了股票市场时序动态加权网络的步长与社团划分算法,并对社团结构进行了简要分析.     

基于多智能体的股票市场仿真研究

该文通过对股票市场的研究,提出采用基于多智能体的仿真方法来研究股票市场的复杂现象,并用此方法建立了股票市场的仿真模型,对模型中股民所处的环境进行了描述,定义模型中机构投资股民、技术投资股民和跟风投资股民的各种投资和行为规则,设计了股票市场对股民投资行为的撮合机制,并对模型进行了仿真,对仿真结果中股票价格的变化结果和原因,股票交易量的变化情况和原因,以及股民的行为进行了分析,最后指出了下一步的研究方向。     

小波神经网络在股票平均线交易规则中的应用

股票市场是一个复杂的非线性动态系统,利用传统的时间序列预测技术很难揭示其内在规律,而近十几年来发展起来的神经网络理论逐渐成为非线性动态系统预测与建模的强有力工其。该文介绍了小波分析中的趋势提取技术,建立小波分析与神经网络相结合的预测模型,将该模型应用于股票平均线交易规则中,同时还与普通神经网络预测模型进行厂对比,研究实例表明,小波神经网络方法提高了预测精度,对移动平均线交易规则作了一种有效的补允,是股市技术分析的一种自效实用的方法。     

基于元胞自动机的股票市场价格行为的研究

构建了基于元胞自动机的股票市场模型,用于模拟和分析股票市场价格行为的特点和原因.区分了股票市场上基于基本面因素和模仿者两类投资者,对于不同的投资者有不同的投资策略,由简到繁通过3个步骤建立最终的模型,刻画了股票市场的基本结构和行为.利用Matlab,通过计算机模拟发现,此模型能够反映股票市场中尖峰厚尾、波动聚集的特点.通过仿真结果分析得出模仿者对尖峰厚尾现象有较强的影响,波动聚集与投资者交易行为有关.     

基于股票市场灵敏度分析的神经网络预测模型

股票市场是非线性系统,具有内部结构复杂性和外部因素多变性,建立基于股票市场灵敏度分析的神经网络预测模型。针对神经网络结构设计问题,计算网络输入层与隐层神经元的灵敏度,并修剪网络中不敏感的神经元,在保证模型泛化能力的同时,实现网络结构精简;针对神经网络黑箱问题,根据输入层神经元灵敏度解决各输入变量对股票市场的重要性和反馈机制。以上证指数为例,在不同的时间跨度下对股票市场运行规律进行学习,并分析不同结构修剪模型的适用性和市场意义。最后,通过与其他神经网络预测模型比较,验证本文模型的有效性。     

基于元胞自动机的股票市场价格行为的研究

构建了基于元胞自动机的股票市场模型，用于模拟和分析股票市场价格行为的特点和原因，区分了股票市场上基于基本面因素和模仿者两类投资者，对于不同的投资者有不同的投资策略，由简到繁通过3个步骤建立最终的模型，刻画了股票市场的基本结构和行为。利用Matlab．通过计算机模拟发现，此模型能够反映股票市场中尖蜂厚尾、波动聚集的特点。通过仿真结果分析得出模仿者对尖峰厚尾现象有较强的影响，波动聚集与投资者交易行为有关。     

基于机制的复杂适应系统建模*

复杂适应系统建模是研究复杂适应系统演化趋势的重要环节。在引入机制相关概念的基础上,阐述了基于机制的复杂适应系统建模的方法及特点,通过在股票市场建模中的应用阐述了这种建模方法。     

虚拟股市中股民的学习对股市运动的影响

该文针对复杂系统难于模拟研究的问题，基于多主体理论建立了股市的计算机仿真系统，并用这个仿真系统，探讨了股民的学习行为与股市运动规律之间的关系，对新股民在不同学习阶段的行为变化、股民学习速度对股市行为的影响、股民资本分布的演变等问题进行了模拟研究。     

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).     

机器学习在股票预测中的应用综述

揭示股票市场运行规律一直是研究的热点，近些年机器学习方法在股票预测方面取得了不错的进展，相较于传统的基本面分析、技术分析等方法，显示了独特的优势。从股票预测研究的主要问题、特征工程和机器学习算法应用等三个方面，对近年来该领域的主要文献进行总结，并针对每种算法在应用中的特点与不足进行评述。围绕目前机器学习在股票预测上遇到的主要问题，从迁移学习、特征工程、深度学习模型融合等方面进行了深入的分析与展望。     

基于深度强化学习的金融交易算法研究

交易策略在金融资产交易中具有十分重要的作用,如何在复杂动态金融市场中自动化选择交易策略是现代金融重要研究方向.强化学习算法通过与实际环境交互作用,寻找最优动态交易策略,最大化获取收益.提出了一个融合了CNN与LSTM的端到端深度强化学习自动化交易算法,CNN模块感知股票动态市场条件以及抽取动态特征,LSTM模块循环学习...     

结合公司财务报表数据的股票指数预测方法

股票市场参与者的所有市场活动综合影响着股票市场的变化,使股票市场的波动充满复杂性,也使得准确预测股票价格成为难题。在这些影响股市变化的活动中,财务披露是预测股票指数变化的一种吸引人的且具有潜在财务回报的手段。为了应对股票市场的复杂变化,提出一种结合公司披露的财务报表数据进行股票指数预测的方法。该方法首先对股票指数历史数据和公司财务报表数据进行预处理,主要是对公司财务报表数据生成的高维矩阵进行降维,然后用双通道的长短期记忆（LSTM）网络对归一化后的数据进行预测研究。在上证50指数和沪深300指数数据集上的实验结果表明,该方法的预测效果优于仅使用股票指数历史数据的预测效果。     

Systematic analysis and review of stock market prediction techniques

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. Several techniques are devised in the existing techniques to predict the stock market trends. This work presents the detailed review of 50 research papers suggesting the methodologies, like Bayesian model, Fuzzy classifier, Artificial Neural Networks (ANN), Support Vector Machine (SVM) classifier, Neural Network (NN), Machine Learning Methods and so on, based on stock market prediction. The obtained papers are classified based on different prediction and clustering techniques. The research gaps and the challenges faced by the existing techniques are listed and elaborated, which help the researchers to upgrade the future works. The works are analyzed using certain datasets, software tools, performance evaluation measures, prediction techniques utilized, and performance attained by different techniques. The commonly used technique for attaining effective stock market prediction is ANN and the fuzzy-based technique. Even though a lot of research efforts, the current stock market prediction technique still have many limits. From this survey, it can be concluded that the stock market prediction is a very complex task, and different factors should be considered for predicting the future of the market more accurately and efficiently.     

基于数据挖掘的股票分析系统设计

由于股市内的数据的庞大和复杂,在进行有效的股票数据分析的时候我们需要更加精确的数据来为我们服务,基于以上股市数据的特点,利用数据挖掘技术对股市趋势的计算是符合实际的,在BP神经网络算法的基础上对股票分析系统进行设计。     

一种优化的模糊变权重组合预测在沪深300中的应用与研究

沪深300指数是沪深证券交易所联合发布的反映A股市场整体走势的指数.对沪深300指数的分析与研究可以帮助我们了解市场走势,同时也十分有利于投资者全面把握中国股票市场总体运行状况.针对股票市场的高度非线性与复杂性,本文首先利用霍尔特指数平滑法（Holt）和最小二乘支持向量机（LSSVM）模型对沪深300指数进行预测,然后提出一种基于Holt与LSSVM的模糊变权重模型进行预测,取得了令人满意的效果.在设计过程中,我们利用R语言作为模型拟合与预测的软件,R语言是一款免费的良好的数据处理软件.     

COVID-19 Pandemic Data Predict the Stock Market

Unlike the 2007–2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics’ economic effects, especially on the stock market, has not been urgent or feasible until recently. However, with advances in artificial intelligence (AI) and the inter-connectivity that social media provides, such research has become possible. In this paper, we propose a COVID-19-based stock market prediction system (C19-SM2) that utilizes social media. Our AI system enables economists to study how COVID-19 pandemic data influence social media and, hence, the stock market. C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area. The information is then fed to the support vector machine (SVM) and random forest and random tree classifiers along with current stock market values. Then, the system produces a projection of the stock market’s movement during the next day. We tested the system with the Dow Jones Industrial Average (DJI) and the Tadawul All Share Index (TASI). Our system achieved a stock market prediction accuracy of 99.71%, substantially higher than the 89.93% accuracy reported in the related literature; the inclusion of COVID-19 data improved accuracy by 9.78%.