本文基于台湾股市数据,主要研究个人投资者的交易行为。参照Kaniel et al.(2008)构建了个人投资者交易不平衡性指标─净交易,以反映投资者股票交易的强度。采用这种交易不平衡性指标来构建投资组合研究个人投资者的交易行为。首先研究...本文基于台湾股市数据,主要研究个人投资者的交易行为。参照Kaniel et al.(2008)构建了个人投资者交易不平衡性指标─净交易,以反映投资者股票交易的强度。采用这种交易不平衡性指标来构建投资组合研究个人投资者的交易行为。首先研究个人投资者交易和股票的收益之间的动态关系从而分析投资者的交易策略,然后研究个人投资者净交易的收益预测能力从而分析个人投资者交易的信息含量。本文研究发现:台湾股票市场的个人投资者采用负反馈的交易策略,并且个人投资者在交易中表现出很强的处置效应;个人投资者在交易中的信息含量不足;个人投资者交易中的盈利主要来自两个方面:过度反应和价格冲击。文章最后给出政策建议。展开更多
In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the...In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.展开更多
Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical le...Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index.Decile portfolios are formed according to rankings of the forecasted future cumulative returns.The equity market’s neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to,and a low correlation with,the Shanghai Composite Index.To make our strategy more useful to practitioners,we evaluate the proposed stock selection strategy’s performance by allowing only long positions,and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market.The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index.A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.展开更多
文摘本文基于台湾股市数据,主要研究个人投资者的交易行为。参照Kaniel et al.(2008)构建了个人投资者交易不平衡性指标─净交易,以反映投资者股票交易的强度。采用这种交易不平衡性指标来构建投资组合研究个人投资者的交易行为。首先研究个人投资者交易和股票的收益之间的动态关系从而分析投资者的交易策略,然后研究个人投资者净交易的收益预测能力从而分析个人投资者交易的信息含量。本文研究发现:台湾股票市场的个人投资者采用负反馈的交易策略,并且个人投资者在交易中表现出很强的处置效应;个人投资者在交易中的信息含量不足;个人投资者交易中的盈利主要来自两个方面:过度反应和价格冲击。文章最后给出政策建议。
文摘广义Pareto分布(generalized Pareto distribution,GPD)变点是指其超出量发生质变的点,具体表现为一个或多个参数的变化.本文将极端暴雨数据的三参数GPD变点检测问题表示为假设检验问题,通过极大似然比检验统计量解决.尽管不可能得到检验统计量的精确分布,但通过证明GPD变点的极限性质和检验统计量的渐近收敛定理,可得到它的极限分布.同时,利用GPD变点检测模型,对深圳55年的月最大暴雨数据进行了分析.结果发现20世纪80年代是深圳气象学的一个变点,这与最小超阈值(narrowest over threshold,NOT)方法所得结果一致.除此之外,本文方法的优势在于它统一了变点检测前后的分析框架.在此框架下,通过对变点的分析,发现GPD变点之前的极值指数为负.结合降雨量与重现期的关系可得,重现期小于百年的暴雨强度较之前有所减弱.该点之后,极值指数为正,百年以上重现期的暴雨灾害程度较之前严重且极端降雨现象较之前频繁.实证结果表明,GPD变点模型能够较好地捕获传统GPD模型所不能捕捉的内在规律,较好地弥补了传统GPD模型的不足.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
文摘Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index.Decile portfolios are formed according to rankings of the forecasted future cumulative returns.The equity market’s neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to,and a low correlation with,the Shanghai Composite Index.To make our strategy more useful to practitioners,we evaluate the proposed stock selection strategy’s performance by allowing only long positions,and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market.The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index.A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.
