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
We construct a model to examine the time-varying ambiguity of investors.When ambiguity occurs concerning recent news,long(short)position investors who are averse to ambiguity reduce(increase)their holdings,resulting i...We construct a model to examine the time-varying ambiguity of investors.When ambiguity occurs concerning recent news,long(short)position investors who are averse to ambiguity reduce(increase)their holdings,resulting in price drops(rises).We empirically analyze how the“two sessions,”a significant event with high policy ambiguity in China,affect the financial market.Our findings suggest that institutional investors mainly sell their holdings between 15 and 5 days before the meetings.Furthermore,the delay in the“two sessions”in 2020 suggests that these selloffs are driven by ambiguity aversion rather than new information.展开更多
This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatili...This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.展开更多
The transaction-level analysis of security price changes by Madhavan,Richardson,and Roomans(1997,hereafter MRR)is a useful framework for financial analysis.The first-order Markov property of trading indicator variable...The transaction-level analysis of security price changes by Madhavan,Richardson,and Roomans(1997,hereafter MRR)is a useful framework for financial analysis.The first-order Markov property of trading indicator variables is a critical assumption in the MRR model,which contradicts the information lag empirically demonstrated in high-frequency trading processes.In this study,a nonparametric test is employed,which shows that the Markov property of the trading indicator variables is rejected on most trading days.Based on the spread decomposed structure,an MA-MRR model was proposed with a moving average structure adopted to absorb the information lag as an extension.The empirical results show that the information lag plays an important role in measuring the adverse selection risk parameter and that the difference in this parameter between the original and the extension is significant.Furthermore,our analysis suggests that the information lag parameter is a useful measure of the average speed at which information is incorporated into prices.展开更多
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
The literature confirms the existence of expense dispersion in active funds that cannot be fully explained by known factors.Why would investors pay for unexplained additional expenses?We develop a limited learning age...The literature confirms the existence of expense dispersion in active funds that cannot be fully explained by known factors.Why would investors pay for unexplained additional expenses?We develop a limited learning agent modeled by a dual-reasoning model inside an active fund market to reveal the mechanism of expense dispersion in such markets.Whether an agent learns depends on the tradeoff between the loss of decision errors and the associated cognitive cost.We derive an agent's conditions to fall into habitual decision-making steady states.Our simulation reveals that two types of steady-state agents allocate money to high-fee funds:agents with normal-level wealth who diversify due to concerns about diseconomies of scale in returns and agents with low-level wealth who allocate almost all of their money to high-fee funds and remain in a low-wealth steady state.Furthermore,by providing additional wealth,our supplementary experiments reveal a one-way effect of biased investment habits on the low-wealth steady state.Low-wealth investors still have a 53%–87%probability of falling back after additional funding,which is much greater than the probability of naïve investors falling into the low-wealth steady state(10%–12.7%).This finding corroborates the explanation of the unsophisticated or price insensitivity of low-wealth agents and is consistent with existing evidence in the literature.展开更多
One of critical challenges in the development of pedestrian models is the lack of relevant data under emergency conditions.In this paper,we reveal several behavior characteristics of passenger emergency evacuation fro...One of critical challenges in the development of pedestrian models is the lack of relevant data under emergency conditions.In this paper,we reveal several behavior characteristics of passenger emergency evacuation from a burning bus carriage,by observing and analyzing a video recording,attempting to close the gap between practical observation and theoretical modeling.The analysis results show that there are considerable differences between real emergency evacuation and experimental normal evacuation with respect to the cumulative flows,flows,and time gap distributions.Additionally,the behavior of falling-down,which contributes to the formation of movable obstacles,interrupts the continuity of the evacuation process.Then,we incorporate these behavioral characteristics into a microscopic pedestrian model with a fine lattice space representation.The simulation results indicate that the escape is slowed down by the falling behavior,as well as by the luggage-carrying behavior.展开更多
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.
基金National Natural Science Foundation of China(Grant No.71771006)Science and Technology Support Plan of Guizhou(Grant No.2023–221).
文摘We construct a model to examine the time-varying ambiguity of investors.When ambiguity occurs concerning recent news,long(short)position investors who are averse to ambiguity reduce(increase)their holdings,resulting in price drops(rises).We empirically analyze how the“two sessions,”a significant event with high policy ambiguity in China,affect the financial market.Our findings suggest that institutional investors mainly sell their holdings between 15 and 5 days before the meetings.Furthermore,the delay in the“two sessions”in 2020 suggests that these selloffs are driven by ambiguity aversion rather than new information.
基金National Natural Science Foundation of China(Grant no.71771006)Science and Technology Support Plan of Guizhou(Grant no.2023-221).
文摘This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.
基金supported by the National Natural Science Foundation of China(Grant number:71771008)Science and Technology Support Plan of Guizhou(Grant No.2023-221)the Funds for the First-class Discipline Construction(XK 1802-5).
文摘The transaction-level analysis of security price changes by Madhavan,Richardson,and Roomans(1997,hereafter MRR)is a useful framework for financial analysis.The first-order Markov property of trading indicator variables is a critical assumption in the MRR model,which contradicts the information lag empirically demonstrated in high-frequency trading processes.In this study,a nonparametric test is employed,which shows that the Markov property of the trading indicator variables is rejected on most trading days.Based on the spread decomposed structure,an MA-MRR model was proposed with a moving average structure adopted to absorb the information lag as an extension.The empirical results show that the information lag plays an important role in measuring the adverse selection risk parameter and that the difference in this parameter between the original and the extension is significant.Furthermore,our analysis suggests that the information lag parameter is a useful measure of the average speed at which information is incorporated into prices.
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supported by Aeronautical Science Foundation of China(Grant No.2024Z071051004)National Natural Science Foundation of China(Grant No.71771006).
文摘The literature confirms the existence of expense dispersion in active funds that cannot be fully explained by known factors.Why would investors pay for unexplained additional expenses?We develop a limited learning agent modeled by a dual-reasoning model inside an active fund market to reveal the mechanism of expense dispersion in such markets.Whether an agent learns depends on the tradeoff between the loss of decision errors and the associated cognitive cost.We derive an agent's conditions to fall into habitual decision-making steady states.Our simulation reveals that two types of steady-state agents allocate money to high-fee funds:agents with normal-level wealth who diversify due to concerns about diseconomies of scale in returns and agents with low-level wealth who allocate almost all of their money to high-fee funds and remain in a low-wealth steady state.Furthermore,by providing additional wealth,our supplementary experiments reveal a one-way effect of biased investment habits on the low-wealth steady state.Low-wealth investors still have a 53%–87%probability of falling back after additional funding,which is much greater than the probability of naïve investors falling into the low-wealth steady state(10%–12.7%).This finding corroborates the explanation of the unsophisticated or price insensitivity of low-wealth agents and is consistent with existing evidence in the literature.
基金the National Natural Science Foundation of China(72171007,71890972/71890970,72021001).
文摘One of critical challenges in the development of pedestrian models is the lack of relevant data under emergency conditions.In this paper,we reveal several behavior characteristics of passenger emergency evacuation from a burning bus carriage,by observing and analyzing a video recording,attempting to close the gap between practical observation and theoretical modeling.The analysis results show that there are considerable differences between real emergency evacuation and experimental normal evacuation with respect to the cumulative flows,flows,and time gap distributions.Additionally,the behavior of falling-down,which contributes to the formation of movable obstacles,interrupts the continuity of the evacuation process.Then,we incorporate these behavioral characteristics into a microscopic pedestrian model with a fine lattice space representation.The simulation results indicate that the escape is slowed down by the falling behavior,as well as by the luggage-carrying behavior.
