中国股票市场长期记忆特征的实证研究

股票市场收益的长期记忆特征对于系统非线性结构的确定以及市场有效性的研究具有重要的意义.针对上海和深圳的周和日收益序列,采用非线性估计方法提高R/S系列分析估计H参数的精确度.并用ARFIMA模型对沪深股市的收益率的长期记忆性进行了检验,根据分段检验的结果,得出一些中国证券市场有效性的结论.

中国股票市场的分形结构——股票收益长期记忆特征的实证研究

股票市场收益的长期记忆特征对于系统非线性结构的确定以及市场有效性的研究具有重要的意义.针对上海和深圳A股算术加权和流通市值加权市场指数的周收益序列以及上证180指数和深圳成份指数中选取的12只代表性股票的周收益序列,采用重标级差分析(R/S分析)和ARF IM A模型对其进行了实证研究.从统计结果来看,样本序列呈现出尖峰和肥尾等有偏特征,明显不满足正态分布的假设,表明收益序列可能具有长程相关或记忆性.进一步的研究发现,沪深两市A股市场指数收益序列和大多数个股(10只股票)存在明显的长期记忆特征,收益分布表现出持久性.从划分不同时段的分析结果来看,中国股票市场渐进趋于弱势有效.

沪深股票市场长程记忆相关性研究

针对上海和深圳的日收益序列,采用重标级差(R/S分析)对其进行了实证研究.从统计结果来看,样本序列呈现出尖峰、胖尾等有偏特征,明显不满足正态分布的假设,表明收益序列可能具有长程相关或记忆性.采用ARFIMA模型对沪深股市收益率的长期记忆性进行了检验,根据分段检验的结果,得出了一些我国证券市场有效性的结论.

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.

中国证券市场收益分形分析

股票市场收益记忆特征对于系统非线性结构的确定以及市场有效性的研究具有重要的意义。本文针对上海和深圳的周和日收益序列,采用重标级差(R/S分析)对其进行了实证研究。从统计结果来看,样本序列呈现出尖峰、胖尾等有偏特征,明显不满足正态分布的假设,表明收益序列可能具有长程相关或记忆性。进一步的研究表明分形市场假说比传统的有效市场假说更好地描述了中国证券市场的非线性特征。