生产计划中阶梯型价格原料最优配置

按照规模经济效应,商品价格会随着订购量的增大而减小(即打折).根据生产实际,本文提出了商品的阶梯型价格,建立了最优生产计划模型.该计划模型有选择性地生产能够获最大利润的产品,并且可以对生产所需的原料进行最优配置;基于最优化理论与方法,给出了求解所建模型的有效算法,通过实例验证了算法的可行性和有效性.     

期权思想在多阶段项目投资中的应用

本文主要借助期权理论,讨论项目投资分多阶段进行时选择最佳投资的问题.首先通过在单阶段投资下建立项目投资的最佳选择框架,然后展开到项目投资分两个阶段进行的情形上进行讨论分析,得出此情形下的投资选择结论,最后把这一结论扩展到项目投资分多阶段进行的项目上.     

上海证券市场回报率分析——基于ARIMA模型和GARCH模型

本文分析中国上海证券市场回报率。分别通过APdMA模型和GARCH模型，发现若用APdMA模型分析和建立时间序列模型，一次自回归项是不够的，需要高次项，在大多数情形，若运用GARCH模型，则GARCH（1，1）就能够很好的拟合数据。     

稀缺资源的节省利用研究

本在建立模型的基础上，对稀缺资源的节省利用问题进行了研究，给出了稀缺资源能否节省的判别条件和具体作法。     

中国股市基本面价值加权投资组合研究

基本面价值加权投资组合是近年来在美国兴起的新型投资理论和技术.其基础是噪声市场假说和价格回归价值。本文回顾了基本面投资组合的相关文献,解释了其建立的理论基础和模型.本文还根据平滑市值权重技术,构建了基于中国股票市场的基本面投资组合,并和传统的市值加权投资组合进行比较。本文的主要结论是,中国市场1992年到2002年和美国市场类似呈现平稳价格噪声,基本面投资组合总体优于市值加权组合.股权分置改革初期市场低落,市场价格过度低估基本面价值,市值组合占优势。2005年下半年来股市呈现一定泡沫特征,市场价格对基本面价值有过度高估趋势,间接地证明了相关部门对股市过热的警告和监管是合理的.     

A multicriteria DSS for stock evaluation using fundamental analysis

The paper describes a multicriteria decision support system which aims at presenting an evaluation of the Athens Stock Exchange (ASE) stocks, on the basis of fundamental analysis. The system evaluates the stocks based on the method of fundamental analysis ratios, which is the most appropriate evaluation approach regarding investment decisions within a long term horizon. In addition to quantitative data deriving from fundamental analysis, the system uses qualitative data as well, in order to improve the reliability of the evaluation. The system introduced in this paper, utilises multicriteria analysis methodologies in order to rank the stocks by placing the best stock first and the worst last. Stock evaluation considers the specific characteristics of the potential investor, as well as his attitude towards undertaken risk. The final output of the system is four stock rankings which respond to four different criteria groups, depending on the type of accounting plan each listed company belongs to. The system incorporates a large volume of relevant information and operates in ‘real world conditions’ since its data are constantly updated. Finally, the system is intended for both institutional and private investors.     

中国棉花期货市场价格发现功能研究

期货市场和现货市场之间的价格发现功能一直是监管部门和投资者十分关心的问题。本文借助信息共享模型、脉冲响应函数和方差分解等方法，对中国棉花期货市场和现货市场的价格关系进行了实证研究。研究结果表明：棉花期货价格和现货价格之间存在显著的双向引导关系和长期均衡关系；期货市场和现货市场都扮演价格发现角色，且期货市场在价格发现中处于主导地位。     

不同质量调整法在消费者价格指数(CPI)中的应用

传统的消费者价格指数(CPI)存在着一定的质量偏差,不能真实地反映实际价格的变化.直接比较法、交叠法、生产成本法、链接法等质量调整法的应用均有一定的局限性,且缺陷较多,均指插补调整借助于缺失值插补的原理,以同一组内商品价格的平均变化率作为质量调整系数,能有效地降低价格指数的质量偏差。Hedonic质量调整法是以实际资料建立的回归方程为基础,运用数学的手段科学的将质量变化程度加以量化,因此与传统的质量调整法相比含有较少的主观因素,为CPI指数的准确度量提供了科学的质量调整依据.     

Memory effect and multifractality of cross-correlations in financial markets

We introduce an instantaneous and an average instantaneous cross-correlation function to detect the temporal cross-correlations between individual stocks based on the daily data of the United States and the Chinese stock markets. The memory effect of the instantaneous cross-correlations is investigated by applying the detrended fluctuation analysis (DFA), where the DFA exponents can be partly explained by the correlation function from the common sense. Long-range memory is observed for the average instantaneous cross-correlations, and persists up to a month magnitude of timescale for the United States stock market and half a month magnitude of timescale for the Chinese stock market. In addition, multifractal nature is investigated by a multifractal detrended fluctuation analysis.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.