Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?

This article investigates the merits of high-frequency intraday data when forming mean-variance efficient stock portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency as judged by the performance of these portfolios. The optimal sampling frequency ranges between 30 and 65 minutes, considerably lower than the popular five-minute frequency, which typically is motivated by the aim of striking a balance between the variance and bias in covariance matrix estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. Bias-correction procedures, based on combining low-frequency and high-frequency covariance matrix estimates and on the addition of leads and lags do not substantially affect the optimal sampling frequency or the portfolio performance. Our findings are also robust to the presence of transaction costs and to the portfolio rebalancing frequency.     

电话社会网络结构的实证分析

根据城市中的电话通话记录建立了一个大型的社会网络,研究了这个网络的有关结构特性.网络呈现了与相关社会网络相似的特性,如度分布的幂律特性、小世界效应、非平凡聚集或网络传递的特点等,同时该网络也表现出与其他社会网络不同的特性,如度的异向匹配.通过给边赋予权重,分析了网络在加权情况下的一些静态结构性质,其中,包括点强度分布、强度相关性及群聚系数等.     

我国证券投资基金持股与股价波动的关系——基于动态面板数据模型的实证研究

本文从证券投资基金持股比例与股价波动关系的角度，分析基金的参与对股价波动的影响。本文选取2002—2007年的基金与股票市场相关数据，运用动态面板数据模型分析后发现：一方面，基金持股比例变化对股票价格波动有推波助澜作用：另一方面，基金持股比例受股价波动的影响，基金更偏向于购买股价波动较大的股票。     

企业持续改进能力的组织与运作——贵州省企业与全球企业之比较分析

主要采用主成分分析方法,综合主成分分析方法和系统聚类方法等多元统计中的数据处理手段,对全球可持续创新网络(CInet)于2004年组织调查的全球近500家企业所得数据进行分析。通过贵州省企业与全球其他国家的比较,发现在企业持续改进能力的组织与运作方面,贵州省企业与全球其他国家之间存在较大差异。为寻找造成这些差异的原因,采用综合主成分分析方法和系统聚类方法,建立了在持续改进的组织与运作方面能力强的目标企业群。然后通过贵州省企业与目标企业之间在企业组织与运作构成因子的对比分析,指出了贵州省企业在持续改进的组织与运作中所存在的问题,进而对贵州省企业提出了相应改进的建议及其对策。其中,目标企业的选取及其创新能力检验、数据表缺省项的填充方法、在分析数据时所采用的因子对比分析方法等对其他大型调研数据分析均具有一定的借鉴意义。     

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black-Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.     

网格和密度的聚类算法在CRM中的应用

聚类分析是数据挖掘领域中一种非常有用的技术,它用于从大量数据中寻找隐含的数据分布模式,主要有分割法、层次法、密度法、网格法和模型法等。该文主要讨论数据挖掘中一种基于密度和网格的聚类分析算法及其在客户关系管理中的应用。该算法具有较高的聚类效率而且容易实现,可以发现任意形状的聚类,时间复杂度低,聚类精度高,适用于数据的批量更新。该文还提出增量式聚类技术,它不仅能够利用前期聚类的结果,充分提高聚类分析的效率,而且可以降低维护知识库所带来的巨大开销。实验证明了算法的有效性。     

网格聚类在多雷达数据融合算法中的应用

应用网格聚类的方法区分同一雷达接收的不同目标的观测数据,通过类间数据融合,实现同一目标不同雷达接收数据的融合,以便对多目标进行实时跟踪。研究了观测数据网格聚类的基本思想、形式化描述及算法实现,讨论了对机动目标跟踪的Kalman滤波方程及空管系统中易于计算的各参数矩阵理论依据及相应的初值。仿真结果表明,通过网格聚类能很好地区分不同目标,聚类后再进行跟踪融合更加准确。     

国内深度学习研究热点及现状述评——基于2005-2019年间文献关键词可视化分析

以中国知网(CNKI)收录的2005-2019年间的2780篇深度学习相关文献为研究对象,采用共词聚类分析方法,通过谱系图梳理出我国当前深度学习领域的研究现状和热点。国内以深度学习和深度教学为主线,主要研究深度学习的发展脉络、课堂发生机制、内涵及深度教学、深度学习背景下的信息化教学模式四大类。为了获得深度学习领域更多的创新性成果,未来研究应深挖理论体系,扎根实践教学;在实证研究中深化教育机制体制建设;从学生深度学习延伸到教师深度教学;拓宽研究视角和研究场域。     

Robust multivariate mixture regression models with incomplete data

Multivariate mixture regression models can be used to investigate the relationships between two or more response variables and a set of predictor variables by taking into consideration unobserved population heterogeneity. It is common to take multivariate normal distributions as mixing components, but this mixing model is sensitive to heavy-tailed errors and outliers. Although normal mixture models can approximate any distribution in principle, the number of components needed to account for heavy-tailed distributions can be very large. Mixture regression models based on the multivariate t distributions can be considered as a robust alternative approach. Missing data are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this paper, we propose a multivariate t mixture regression model with missing information to model heterogeneity in regression function in the presence of outliers and missing values. Along with the robust parameter estimation, our proposed method can be used for (i) visualization of the partial correlation between response variables across latent classes and heterogeneous regressions, and (ii) outlier detection and robust clustering even under the presence of missing values. We also propose a multivariate t mixture regression model using MM-estimation with missing information that is robust to high-leverage outliers. The proposed methodologies are illustrated through simulation studies and real data analysis.     

Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.