Long memory of abnormal investor attention and the cross-correlations between abnormal investor attention and trading volume, volatility respectively

2017 ◽  
Vol 469 ◽  
pp. 323-333 ◽  
Author(s):  
Xiaoqian Fan ◽  
Ying Yuan ◽  
Xintian Zhuang ◽  
Xiu Jin
Keyword(s):  
Long Memory ◽  
Trading Volume ◽  
Investor Attention ◽  
Cross Correlations ◽  
The Cross

Time-Series and Dynamic Cross-Correlations Analysis on Unexpected Information: Evidence from Media News and Online Postings

2019 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Yan Li ◽  
Xiangyu Kong ◽  
Xiao Li ◽  
Zuochao Zhang
Keyword(s):  
Mass Media ◽  
New Media ◽  
Trading Volume ◽  
Cross Correlation ◽  
Short Term ◽  
Cross Correlations ◽  
The Cross ◽  
The One ◽  
The Relationship

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet.

scholarly journals Multifractal Detrended Cross-Correlation Analysis of the Return-Volume Relationship of Bitcoin Market

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Wei Zhang ◽  
Pengfei Wang ◽  
Xiao Li ◽  
Dehua Shen
Keyword(s):  
Correlation Analysis ◽  
Trading Volume ◽  
Cross Correlation ◽  
Window Method ◽  
Market Shares ◽  
Cross Correlation Analysis ◽  
Volume Relationship ◽  
Cross Correlations ◽  
The Cross ◽  
Relationship Of

We investigate the cross-correlations of return-volume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended cross-correlation analysis (MF-DCCA) reveal that (1) the nonlinear dependencies and power-law cross-correlations in return-volume relationship are found; (2) all cross-correlations are multifractal, and there are antipersistent behaviors of cross-correlation for q=2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the cross-correlations of return-volume are antipersistent in the entire sample period.

scholarly journals Asymptotic analysis for dispersion relations and travel times in noise cross-correlations: spherically symmetric case

2018 ◽  
Vol 474 (2218) ◽  
pp. 20180382
Author(s):  
Tianshi Liu ◽  
Haiming Zhang
Keyword(s):  
Asymptotic Analysis ◽  
Surface Waves ◽  
Green's Functions ◽  
Green’S Functions ◽  
Dispersion Relations ◽  
Body Waves ◽  
Spherically Symmetric ◽  
Travel Times ◽  
Cross Correlations ◽  
The Cross

The cross-correlations of ambient noise or earthquake codas are massively used in seismic tomography to measure the dispersion curves of surface waves and the travel times of body waves. Such measurements are based on the assumption that these kinematic parameters in the cross-correlations of noise coincide with those in Green's functions. However, the relation between the cross-correlations of noise and Green's functions deserves to be studied more precisely. In this paper, we use the asymptotic analysis to study the dispersion relations of surface waves and the travel times of body waves, and come to the conclusion that for the spherically symmetric Earth model, when the distribution of noise sources is laterally uniform, the dispersion relations of surface waves and the travel times of SH body-wave phases in noise correlations should be exactly the same as those in Green's functions.

Intraday return inefficiency and long memory in the volatilities of forex markets and the role of trading volume

2018 ◽  
Vol 506 ◽  
pp. 433-450 ◽  
Author(s):  
Syed Jawad Hussain Shahzad ◽  
Jose Areola Hernandez ◽  
Waqas Hanif ◽  
Ghulam Mujtaba Kayani
Keyword(s):  
Long Memory ◽  
Trading Volume

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

scholarly journals Long Memory in Stock-Market Trading Volume

2000 ◽  
Vol 18 (4) ◽  
pp. 410-427 ◽  
Author(s):  
Ignacio N. Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Trading Volume ◽  
Stock Market Trading

scholarly journals Modeling Complex System Correlation Using Detrended Cross-Correlation Coefficient

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
Hong Zhang ◽  
You Gao
Keyword(s):  
Complex System ◽  
Time Series ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Correlation Property ◽  
Traffic Time Series ◽  
Cross Correlations ◽  
The Cross ◽  
Active Research

The understanding of complex systems has become an area of active research for physicists because such systems exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails, and fractality. We here focus on traffic dynamic as an example of a complex system. By applying the detrended cross-correlation coefficient method to traffic time series, we find that the traffic fluctuation time series may exhibit cross-correlation characteristic. Further, we show that two traffic speed time series derived from adjacent sections exhibit much stronger cross-correlations than the two speed series derived from adjacent lanes. Similarly, we also demonstrate that the cross-correlation property between the traffic volume variables from two adjacent sections is stronger than the cross-correlation property between the volume variables of adjacent lanes.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

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