Market Incompleteness in Regional Electricity Transmission. Part II: The Forward and Real Time Markets

The paper extends our previous analysis of market incompleteness to the case where there exist both a forward and real time market. The discussion is conducted with reference to a two-stage framework commonly used in stochastic programming and finance, but also familiar to the power industry. The paper considers two market designs. The first organization assumes that physical transmission services are auctioned in the forward market. The second organization supposes a financial market of transmission services. We recover the known perfect hedging property of nodal pricing and show that the flowgate model does not share this property. A standard proposition in finance is that market completeness requires that the number of tradable instruments is equal to or exceeds the number of risk factors. We show that none of the nodal or flowgate models satisfy this property. Other instruments, of the option type, should thus be introduced in these systems in order to better trade risk and complete the market.     

The forecasting ability of Internet-based virtual futures market

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. While the virtual futures market has received much attention, questions remain as to the ideal number of participants. Results of Granger causality tests and analysis of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of the underlying stock price. Moreover, the participants were not professional investors but merely undergraduate finance students with only a cursory introduction to futures trading. Our results provide additional evidence supporting the use of VFMs in forecasting and show that VFMs are powerful forecasting tools.     

基于小波的金融时间序列波动性研究

文章针对描述金融时间序列波动性的问题,提出了一种基于小波Mallat算法的时间序列分析方法,即先用Mallat算法对金融时间序列进行分解与重构,继而对各分解层上的单支重构分量进行时间序列分析。在实证分析中,以宝钢股份的股票收益率序列为例,对这种综合分析方法的有效性与准确度进行了验证,并得到了较为满意的结果。     

基于自相似的金融时间序列波动聚集性研究

自相似与波动聚集性是金融时间序列的两个重要特征,文章将这两个特征结合,提出了一种基于自相似的波动聚集模型。基于该模型提出了一种基于拟合优度与趋势变动的联机时间序列分割算法,算法能够根据波动的自相似特征将序列分割为多个子序列,从而用于研究在不同时段金融时间序列波动的自相似性。对实际数据的实验结果表明,文章所提出的模型和分割算法是有效的。     

遗传算法在结构优化中的应用

采用遗传算法求解桁架结构优化设计问题,建立了平面桁架结构优化的数学模型,应用改进的自适应遗传算法对其进行求解。为了加快遗传算法进化过程,本文采用精英选择与轮盘赌选择相结合的策略,鲁棒性更好,收敛速度更快,拥有较强的寻优能力。算例表明,该遗传算法可用于桁架结构的优化设计,优化速度快,效率高,优化结果更加可靠。     

The Effect of Electronic Markets on Forecasts of New Product Success

In this paper, we extend field experiments of real money prediction markets to the problem of forecasting the success of a new product. We collect forecasts using a traditional survey mechanism and a market mechanism. Our results suggest that market prices summarize the information contained in survey forecasts and improve those forecasts by reducing the variability of the forecast. However, we find no evidence of a crystal ball equilibrium. Our markets have considerable variability and predict only as well as the public signal provided by the HSX movie market game.     

利用双向数据链表实现期货交易内存撮合

文章在分析期货交易系统数据来源及流向的基础上,提出了适合期货交易的内存数据结构,具体说明了如何通过双向链表对期货交易中的有关数据进行组织和管理,从而实现内存撮合,提高交易系统的响应速度。     

改进的遗传算法用于氩原子簇的结构优化

目的:研究一种改进的遗传算法对氩原子簇的结构进行优化;方法：描述了一种采用实数串编码,非一致性变异和数值交叉算子改进的遗传算法,同时结合了局部搜索技术来进一步优化遗传算法得到的最佳解;结论：将改进的遗传算法用于氩原子簇（Ar)N（N＝2.14）的结构优化后,其结果表明MGALM虽算法简单,但对较小原子簇体系（N≤14）,可以得到其已知的全局最优结构。     

The SIML estimation of realized volatility of the Nikkei-225 Futures and hedging coefficient with micro-market noise

For the estimation problem of the realized volatility and hedging coefficient by using high-frequency data with possibly micro-market noise, we use the Separating Information Maximum Likelihood (SIML) method, which was recently developed by Kunitomo and Sato [11], [12] and [13]. By analyzing the Nikkei-225 Futures data, we found that the estimates of realized volatility and the hedging coefficients have significant bias by using the traditional historical method which should be corrected. The SIML method can handle the bias problem in the estimation by removing the possible micro-market noise in multivariate high-frequency data. We show that the SIML method has the asymptotic robustness under non-Gaussian cases even when the market noises are autocorrelated and endogenous with the efficient market price or the signal term.     

一种新的递阶多变量模糊控制器的设计

针对多变量模糊控制器设计中存在规则库维数过高、隶属函数以及规则库难于获取的问题,提出一种新的递阶模糊控制器（NHFLC）,使得总的规则数大大减少,且无需了解各输入变量对系统的影响程度,基于该控制器结构下的模糊控制策略利用遗传算法来获得,仿真结果表明NHFLC与通常的MIMO模糊控制器相比,具有更好的动态性能和鲁棒稳定性。     

Neural network analysis and the characteristics of market sentiment in the financial markets

The properties of a modular neural network system designed to forecast movements of the long gilt futures contract are examnined. The examination illustrates that diagnostic measures can effectively inform end-users of the system when appropriately to switch modules given the likelihood of the degradation of forecasts. The modelling is based upon periods of market sentiment, and the inductive data models encapsulated, within the modules of the neural network system, are investigated. The models track, and also provide knowledge of , underlying market characteristics in different periods of market sentiment. The research provides deductions concerning the (dis-)similarity of underlying characteristics in differing periods of market sentiment.     

The Decoupling of Value Creation from Revenue: A Strategic Analysis of the Markets for Pure Information Goods

Information and telecommunications technologies have profoundly altered the distribution channels available for a wide range of goods and services. In this paper we analyze a particular class of products, information goods and develop a first framework for predicting which information goods are most likely to see their production, distribution, and consumption patterns altered by the net, which are likely to see shifts in power and profitability, and which are likely to remain unchanged for the foreseeable future. We focus on music and news as a selected couple of very different information goods industries that follow two very different trajectories. Our results suggest that the power structure in news distribution is unlikely to be transformed rapidly. In contrast, the power structure in music is transforming rapidly. Star acts no longer need their record labels to certify their music to their fans, and digital production and distribution have reduced or eliminated the value of other assets owned by the record companies. The framework we use to analyze these two industries can readily be applied to a range of others ... from the production of television soap opera series to the publication of academic journals in polymer chemistry.     

直线的中点Bresenham算法

直线Bresenham算法的基本原理是采取对整型参量的符号进行检测,整型参量的值正比于两像素与实际线段之间的偏移.直线的中点Bresenham算法是依据下一个点可能出现的两个点的中间点处在直线的位置来判断下一个点的取舍.     

一种自适应遗传BP神经网络模型研究及应用

如何更有效地提高神经网络的收敛速度和收敛质量。基于遗传算法的全局搜索和BP神经网络局部精确搜索的特性,提出一种自适应遗传BP神经网络模型,该模型的主要算法是先采用一种自适应遗传算法优化BP网络初始权重。而后再进行BP网络的训练过程。最后并研究如何利用该模型进行三级跳远成绩预测。实验结果表明该方法优于传统BP算法。有利于提高网络的收敛性以及学习能力,可在一定程度上提高三级跳远成绩预测的准确率,具有一定的实用价值。     

结构优化设计的遗传算法在多处理机上的实现

针对复杂工程实际问题计算量大,运算时间长的特点,提出结构优化设计的GA方法在多处理机上的实现（并行计算）技术,用来提高计算速度,并用实例验证了其可靠性。     

基于BIC测度和混合遗传算法的BNC结构学习

贝叶斯网络分类器(BNC)结构学习是一个NP难题。贪婪搜索(GS)算法是一种有效且准确性较高的结构学习算法,但贪婪搜索算法很容易陷入局部最优。标准遗传算法是一种全局搜索优化算法,它通过模拟生物种群的进化过程,得到全局最优解。但就其个体而言,个体局部解的质量无法保证,不具备局部寻优的能力。提出了将两种算法相结合,以贝叶斯信息标准(BIC)测度为评价函数,得到一种混合遗传算法,实现了它们的优势互补。实验表明:该算法优于单独利用GS算法进行Bayesian网络结构学习,从而说明该算法的正确性和有效性。     

An early warning system for detection of financial crisis using financial market volatility

Abstract: This study proposes an early warning system (EWS) for detection of financial crisis with a daily financial condition indicator (DFCI) designed to monitor the financial markets and provide warning signals. The proposed EWS differs from other commonly used EWSs in two aspects: (i) it is based on dynamic daily movements of the financial markets; and (ii) it is established as a pattern classifier, which identifies predefined unstable states in terms of financial market volatility. Indeed it issues warning signals on a daily basis by judging whether the financial market has entered a predefined unstable state or not. The major strength of a DFCI is that it can issue timely warning signals while other conventional EWSs must wait for the next round input of monthly or quarterly information. Construction of a DFCI consists of two steps where machine learning algorithms are expected to play a significant role, i.e. (i) establishing sub-DFCIs on various daily financial variables by an artificial neural network, and (ii) integrating the sub-DFCIs into an integrated DFCI by a genetic algorithm. The DFCI for the Korean financial market is built as an empirical case study.     

基于遗传算法的前馈神经网络结构优化

为防止交叉后优秀基因段的丢失,在随机非一致线性交叉的基础上,设计了一种与个体适应度相关的线性交叉方案。构造了一种使交叉率与变异率随进化过程自适应调整的方法,有效抑制了遗传算法的早熟收敛。然后,针对函数逼近问题用改进后的遗传算法去优化前馈神经网络的结构,降低了神经网络训练陷入局部最优的可能性,提高了网络的泛化能力。     

基于小生境遗传算法的贝叶斯网络结构学习算法研究*

在数据缺失的情况下讨论一种贝叶斯网络的结构学习算法.该算法结合了小生境遗传算法和EM算法,最后通过试验说明了该算法的有效性.     

遗传算法在PID参数优化中的应用

采用常规整定方式,往往费时且难以满足控制要求。本文使用浮点编码遗传算法优化PID控制器的参数,取得了很好的效果。