The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural ...The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.展开更多
This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock pri...This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock price index correlations based on open-high-low-close(OHLC)data.More intraday information can be utilized compared with the widely used return-based method.Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold.The topologies of the global stock market complex network constructed using 70 important global stock price indices before(2017-2019)and after(2020-2022)the COVID-19 outbreak were examined.The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network.The global stock market co-movement characteristics are revealed,and the financial centers of the developed,emerging,and frontier markets are identified.Using centrality indicators,we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic.Based on these findings,we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.展开更多
基金the financial support from the Beijing Natural Science Foundation(Grant No.9244030)the National Natural Science Foundation of China(Grant Nos.72021001,11701023).
文摘The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial practice.However,the inherent constraints in OHLC data pose immense challenges to its structural modeling.Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure.To address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis.Extensive simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach.The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.
基金the financial support from the Beijing Municipal Social Science Foundation(No.20GLC054)the National Natural Science Foundation of China(Nos.72021001,72174020,71904009)+1 种基金the Natural Science Foundation of Beijing Municipality(No.9232014)the Humanities and Social Science Fund of Ministry of Education of China(No.18YJC840041).
文摘This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019(COVID-19)pandemic.We propose a novel method for calculating stock price index correlations based on open-high-low-close(OHLC)data.More intraday information can be utilized compared with the widely used return-based method.Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold.The topologies of the global stock market complex network constructed using 70 important global stock price indices before(2017-2019)and after(2020-2022)the COVID-19 outbreak were examined.The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network.The global stock market co-movement characteristics are revealed,and the financial centers of the developed,emerging,and frontier markets are identified.Using centrality indicators,we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic.Based on these findings,we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.
