基于遗传神经网络的股指预测

股票市场是一项集合许多市场复杂因素的活动,股票分析的方法非常多。将遗传算法用于BP神经网络的训练过程对股票价格的预测,设计一个三层的BP神经网络,优化网络输入,在传统BP神经网络的基础上加入遗传算法。通过实例分析及实际结果表明这种BP神经网络的准确性和科学性。     

生产装置中备件智能化库存管理

分析了大型生产企业中影响备件库存的众多因素,提出了一个简单实用的备件库存数学计算公式,并以此为基础开发了备件智能化库存管理软件。     

基于参数模型的利率期限结构估计的实证

为了获得我国利率期限结构估计的可靠模型,对已有的经典模型进行实证检验.选用1997年6月至2004年10月上证所国债市场的月度数据,对利率期限结构估计参数模型中的Nelson-Siegel模型(NS模型)和Svensson模型(SV模型)进行了似然比检验、样本内和样本外的分组拟合优度检验.检验结果表明,NS模型比SV模型更适合于上证所的利率期限结构估计;上证所利率期限结构在1～10a期限期间尤其是5～7a期限期间能够获得可靠的估计;在0～1a和10～20a期限期间估计的可靠性不高.     

基于高频数据的股指期货期现统计套利程序交易

我国最近推出股指期货后,大量投资者采用传统的基于持有成本理论的日间股指期货期现套利策略进场套利,使得期现套利的价差很快收窄,可套利机会越来越少。从两个角度对传统的股指期货期现套利策略进行拓展:一方面,将获取绝对收益的统计套利策略引入到股指期货期现套利中,并解决统计套利策略在进行股指期货期现套利时遇到的问题;另一方面,将交易标点的交易周期深入到分钟级别的高频行情。通过这两个方面的拓展,不仅开阔了股指期货期现套利的理论研究思路,而且获得了较好的收益风险系数,对投资实践也具有一定的指导意义。     

Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks

With the growing importance of Internet-based businesses, malicious code attacks on information technology infrastructures have been on the rise. Prior studies have indicated that these malicious attacks are associated with detrimental economic effects on the attacked firms. On the other hand, we conjecture that more intense malicious attacks boost the stock price of information security firms. Furthermore, we use artificial neural networks and vector autoregression analyses as complementary methods to study the relationship between the stock market returns of information security firms and the intensity of malicious attacks, computed as the product of the number of malicious attacks and their severity levels. A major contribution of this work is the resulting time-delayed artificial neural network model that allows stock return predictions and is particularly useful as an investment decision support system for hedge funds and other investors, whose portfolios are at risk of losing market value during malicious attacks.     

基于熵的自适应门限小波去噪股市价格预测

本文针对传统软阈值法小波去噪采用统一门限而引起的过平滑问题,根据熵的特性,在各层自适应调整去噪门限,提出一种改进的小波去噪算法,采用Hurst指数和盒维数作为判决准则抑制过平滑。最后将算法应用于股市价格时间序列去噪,并用BP神经网络对去噪后的深发展A近20年的收盘价格进行了分段预测。仿真表明,本文方法与传统方法相比,误差明显减小,预测结果更为理想。     

基于LDAP的政府采购统一用户管理的研究与实现

为了更好地解决电子化政府采购平台对组织机构、部门、用户的管理问题,减少授权管理复杂性,使电子化政府采购平台适应不同的机构及部门,实现电子化政府采购平台用户管理的高效和安全,本文介绍了采用RBAC和LDAP技术的统一用户管理系统的设计和实现。     

灰色局部支持向量回归机及应用

为了解决全局支持向量回归机(Global-SVR)在大样本数据集中计算效率低下的问题,将局部支持向量回归机与灰色系统理论有机结合,并利用灰色关联度作为局部邻域函数构造灰色局部支持向量回归机(GL-SVR),该做法具有一定的理论价值.优化过程中采用留一法评估学习机的泛化性能,利用模式搜索法选择模型参数.真实的股价涨跌幅预测实验结果表明,该方法既加快了运算速度,又提高了预测精度.     

Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems

Modern computerized stock trading systems (mechanical trading systems) are based on the simulation of the decision-making process and generate advice for traders to buy or sell stocks or other financial tools by taking into account the price history, technical analysis indicators, accepted rules of trading and so on. Two stock trading simulating systems based on trading rules defined using fuzzy logic are developed and compared. The first is based on the so-called “Logic-Motivated Fuzzy Logic Operators” (LMFL) approach and aims to avoid certain disadvantages of the classical Mamdani’s method, which has been developed for use in fuzzy logic controllers and not for solving the decision-making problems of stock trading. The LMFL   approach is based on the modified mathematical representation of t$t$-norm and Yager’s implication rule. The second trading system combines the tools of fuzzy logic and Dempster–Shafer Theory (DST  ) to represent the features of the decision-making process more transparently. The fuzzy representation of trading rules based on the theory of technical analysis is used in these expert systems. Since the theory of technical analysis is based on the indicators used by experts to predict stock price movements, the method maps these indicators into new inputs that can be used in a fuzzy logic system. The only required inputs to calculate these indicators are past sequences (history) of stock prices. The method relies on fuzzy logic to choose an appropriate decision when certain price movements or certain price formations occur. The optimization procedure based on historical (teaching) data is used as it significantly improves the performance of such expert systems. The efficiency of the developed expert systems is measured by comparing their outputs versus stock price movements. The results obtained using real NYSE$NYSE$ data allow us to say that the developed expert system based on the synthesis of fuzzy logic and DST provides better results and is more reliable. Moreover, such a conjunction of fuzzy logic, DST and technical analysis, makes it possible to make a profit even when trading against a dominating trend.     

Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange

In the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987–22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics.