Cross-sample entropy statistic as a measure of synchronism and cross-correlation of stock markets

Shi, Wenbin; Shang, Pengjian

doi:10.1007/s11071-012-0680-z

Cited by 80 publications

(34 citation statements)

References 18 publications

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“…Figure 6 shows relation between uncertainty and dependence with changes of different characteristics of common factors. Scatter plots illustrate strong positive relation between them, which consisted of findings in [44]. Particularly when = 4, scattered points are fitted by straight lines with high goodness of fit.…”

Section: Relation Between Uncertainty and Dependencementioning

confidence: 88%

Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity

Wang

2017

Discrete Dynamics in Nature and Society

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An artificial stock market with agent-based model is built to investigate effects of different information characteristics of common factors on the dynamics stock returns. Investors with limited information capacity update their beliefs based on the information they have obtained and processed and optimize portfolios based on beliefs. We find that with changing of concerned information characteristics the uncertainty of stock price returns rises and is higher than the uncertainty of intrinsic value returns. However, this increase is constrained by the limited information capacity of investors. At the same time, we also find that dependence between returns of stock prices also increased with the changing information environment. The uncertainty and dependency pertaining to prices show a positive relationship. However, the positive relationship is weakened when taking into account the features of intrinsic values, based on which prices are generated.

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Section: Relation Between Uncertainty and Dependencementioning

confidence: 88%

Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity

Wang

2017

Discrete Dynamics in Nature and Society

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“…In this paper we follow Shi (2013) and Zhang (2007) to take 2 m = . MSCE is the Cross-SampEn at different scale.…”

Section: Datamentioning

confidence: 99%

“…In this paper we follow Shi (2013) and Zhu (2010) to standardize the original time series U and V with formula (8) and (9): Figure 2 shows the MSCE value when 0.2 r = . The horizontal axis is the scale factor t .…”

Section: Datamentioning

confidence: 99%

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Multiscale Cross Sample Entropy Analysis for China Stock Markets and International Crude Oil Price

Wang¹,

Xia²

2017

Proceedings of the International Conference on Transformations and Innovations in Management

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This paper presents multiscale cross sample entropy (MSCE) analysis for the synchrony relationship between China stock markets and international crude oil price. Shanghai and Shenzhen Stock Exchange (SSE, SZSE) Composite Indexes are used to represent China stock markets. WTI (West Texas Intermediate grade) and Europe Brent crude oil spot price is employed as benchmark for international oil price. MSCE is calculated and analyzed with different parameters. The results show that the MSCE between the stock indexes and the oil price decreases as the time scale increases. It implies that they are more synchronous at greater time scale. Under the same condition, the MSCE between SSE and oil price is smaller than the value between SZSE and oil price. This indicates that SSE is more synchronized with world oil price than SZSE. Meanwhile, the MSCE of WTI and stock markets is smaller than the value of Brent oil price and stock markets. This suggests that China stock markets are more synchronized with WTI than Brent oil price.

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“…A variety of methods can be used to extract the fundamental features caused by internal and external environments, such as cross-correlation [4], autocorrelation [5], and complexity [6,7]. However, although appropriate for measuring the relationships, they fail to illustrate the asymmetry and directionality among components of a system.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Transfer Entropy Networks on Industrial Electricity Consumption

Yao

Kuang

Lin

et al. 2017

Entropy

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Abstract:We study information transfer routes among cross-industry and cross-region electricity consumption data based on transfer entropy and the MST (Minimum Spanning Tree) model. First, we characterize the information transfer routes with transfer entropy matrixes, and find that the total entropy transfer of the relatively developed Guangdong Province is lower than others, with significant industrial cluster within the province. Furthermore, using a reshuffling method, we find that driven industries contain much more information flows than driving industries, and are more influential on the degree of order of regional industries. Finally, based on the Chu-Liu-Edmonds MST algorithm, we extract the minimum spanning trees of provincial industries. Individual MSTs show that the MSTs follow a chain-like formation in developed provinces and star-like structures in developing provinces. Additionally, all MSTs with the root of minimal information outflow industrial sector are of chain-form.

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Cross-sample entropy statistic as a measure of synchronism and cross-correlation of stock markets

Cited by 80 publications

References 18 publications

Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity

Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity

Multiscale Cross Sample Entropy Analysis for China Stock Markets and International Crude Oil Price

A Study of the Transfer Entropy Networks on Industrial Electricity Consumption

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