Dynamic relationship between stock market trading volumes and investor fear gauges movements

Cai, Yuxin; Hong, Jianqiao

doi:10.1080/00036846.2019.1588954

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…Cai and Hong used the MFDFA to investigate the correlation between stock market trading volume and investor fear index. The empirical results show that the dynamic relationship between the volatility of stock market trading volume and different types of investor fear indicators has the characteristics of multifractal, and the dynamic relationship between them has a strong antipersistence [ 36 ]. With the application of the MFDFA, some scholars have found that the stock markets of China and the United States both have multifractal characteristics before and after the outbreak of COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Cai

Peng

et al. 2022

Entropy

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As a typical complex system, the stock market has attracted the attention of scholars and investors to comprehensively understand its fractal characteristics and analyze its market efficiency. Firstly, this paper proposes an asymmetric, detrended fluctuation analysis based on overlapping sliding windows (OSW-A-MFDFA). It reduces the generation of fluctuation errors, and the calculation results are more robust and reliable. The advantage of the OSW-A-MFDFA is that it not only can reveal the multifractal characteristics of time series clearly, but also can further accurately analyze the asymmetry of fractal characteristics under different trends. Secondly, this paper focuses on the variation in the width difference and height difference of the multifractal spectrum under different trends. Finally, based on multifractality, this paper proposes a comprehensive indicator MED that can be used to measure market efficiency, which is characterized by traversing all fluctuation orders. The application revealed many interesting findings in style stock indices. Style stock indices have asymmetric multifractal characteristics, and there are significant differences in the fractal spectrum of different style assets. Moreover, the market efficiency of style stock indices is time-varying, which can be reasonably explained from the perspective of the adaptive market hypothesis.

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Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Cai

Peng

et al. 2022

Entropy

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“…Inoue et al (2017) used macroeconomic time series to provide evidence that the choice of estimated window size is sensitive and proposed that an optimal size should be used for forecasting. Cai and Hong (2019) set the length of each window at approximately one year to research the cross-correlations between crude oil and investor fear gauges. We fix the length of each window at 230 that each stock market with business days (approximately one year), set the rolling step as one day, and calculate the scaling exponents for the four pairs of series in each window when q ¼ 2.…”

Section: Volatility Linkages Across Time Using Rolling-window Analysismentioning

confidence: 99%

“…Alaoui et al (2019) investigated the cross-correlation between Bitcoin prices and trading volumes, showing that Bitcoin prices changes and changes in trading volume mutually interact in a nonlinear way. Cai and Hong (2019) explored the volatility linkages between stock market trading volumes and investor fear gauges, showing that cross-correlations of large fluctuations are strongly anti-persistent in both short-and long-term. Hoque et al (2019) showed that global economic policy uncertainty exerted negative effects on the overall stock market.…”

Section: Introductionmentioning

confidence: 99%

Stock market trading volumes and economic uncertainty dependence: before and during Sino-U.S. trade friction

Cai

Tao

Yan

2020

Economic Research-Ekonomska Istraživanja

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This article mainly studies the interaction between the economic uncertainty and stock market trading volumes changes before and during Sino-U.S. trade friction using multifractal detrended fluctuation analysis (M.F.-D.F.A.) and multifractal detrended crosscorrelation analysis (M.F.-D.C.C.A.). Our research aims to reveal whether the economic uncertainty increased by Sino-U.S. trade friction affects stock market trading volume more susceptible, as well as how policymaker strengthen risk management and maintain financial stability. The results show that the dynamic volatility linkages between economic uncertainty and stock market trading volumes changes are multifractal, and the cross-correlation of volatility linkages are anti-persistent. Through the rolling-windows analysis, we also find that the economic uncertainty and trading volumes are anti-persistent dynamic cross-correlated. This means that while economic uncertainty increases, trading volume decreases. Besides, Sino-U.S. trade friction has impact on the cross-correlated behaviour significantly, suggesting that stock markets' risks are relatively large and trading volumes changes are more susceptible by economic uncertainty during Sino-U.S. trade friction in the U.S. Our study complements existing literature about the stock markets trading volumes and economic uncertainty dependence relationship by multifractal theory's methods. The overall findings imply that the increased economic uncertainty caused by Sino-U.S. trade friction exacerbates financial risks, which are useful for policymakers and investors.

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“…Multifractal detrended cross-correlation analysis (MF-DCCA) was proposed by Zhou [12] to reveal the multifractal features of two nonstationary time series, and it finds applications ranging from investment market [13][14][15], environmental analysis [16][17][18], biomedical [19][20][21], traffic data [22], and power industry [23]. The technical details of MF-DCCA is mentioned in [12].…”

Section: Cross-correlation Analysis Of Multiple Indicators Based On Mmentioning

confidence: 99%

An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators

et al. 2020

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Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification.

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Dynamic relationship between stock market trading volumes and investor fear gauges movements

Cited by 3 publications

References 24 publications

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Stock market trading volumes and economic uncertainty dependence: before and during Sino-U.S. trade friction

An Online Contaminant Classification Method Based on MF-DCCA Using Conventional Water Quality Indicators

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