Scaling features of price–volume cross correlation

Ardalankia, Jamshid; Osoolian, Mohammad; Haven, Emmanuel; Jafari, G. R.

doi:10.1016/j.physa.2019.124111

Cited by 6 publications

(8 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using the data of 2013‐2018, Ardalankia et al. (2020) attempt to investigate fractal features of volatility and correlation of price–volume couplings in selected stock markets. For developed markets, their results demonstrate that price–volume couplings are negatively correlated in different scales.…”

Section: The Evolution Of Informational Efficiency From 1934 To 2020:...mentioning

confidence: 99%

“…In the Warsaw Stock Exchange during 1996−2000, Gebka (2005) finds that the prevalence of uninformed traders would lead to high volume stocks experience strong price reversals, while low volume stocks experience weak price reversals. By using the data of 2013-2018, Ardalankia et al (2020) attempt to investigate fractal features of volatility and correlation of price-volume couplings in selected stock markets. For developed markets, their results demonstrate that pricevolume couplings are negatively correlated in different scales.…”

Section: Number Of Studies (Total)mentioning

confidence: 99%

See 1 more Smart Citation

What do we know about informational efficiency? Three puzzles and the new direction forward

Lau

2021

Journal of Economic Surveys

View full text Add to dashboard Cite

Informational efficiency generally indicates the prevailing market price of assets by focusing on the connection between market participants in reacting to the arrival of new information. However, several authors have suggested that uncorrelated trading strategies across markets are meant for traders who genuinely know the best. Our study aims to survey the most recent literature on informational efficiency. We examine the empirical literature on informational efficiency by discussing the findings from dual perspectives, namely price–volume and spot–futures relations. More research needs to be directed toward understanding how market efficiency responds to impending uncertainties in the real economy. By combining the findings of 109 studies from different financial markets, we discuss essential lessons from market microstructures and behavioral biases in decision‐making. Further, we identify three contentious issues yet to be explored in the literature. This study concludes by discussing a new direction for future research.

show abstract

Section: The Evolution Of Informational Efficiency From 1934 To 2020:...mentioning

confidence: 99%

Section: Number Of Studies (Total)mentioning

confidence: 99%

What do we know about informational efficiency? Three puzzles and the new direction forward

Lau

2021

Journal of Economic Surveys

View full text Add to dashboard Cite

show abstract

“…There are different methods for quantifying the edge weight in an IG. Among these methods, we can mention the Pearson Correlation (PerC) coefficient (Patro et al, 2013;Wiliński et al, 2013) and, Transfer Entropy (TE) (Ardalankia et al, 2020;Kwon & Yang, 2008;Osoolian & Koushki, 2020). Here we use Lower Tail Dependence (LTD) to measure the edge weight in the IG, which is a measure of the coordinated behavior of companies in negative returns (Wang & Xie, 2016).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Importance of Different Companies of Tehran Stock Exchange using Lower Tail Dependency based Interaction Network

Osoolian

Esfidvajani

Ramezani

2023

IJFIFSA

View full text Add to dashboard Cite

Examining the importance and influence of financial market companies is one of the main issues in the field of financial management because sometimes the collapse of a stock exchange company can affect an entire financial market. One systematic way to analyze the significance and impacts of companies is to use complex networks based on Interaction Graphs (IGs). There are different methods for quantifying the edge weight in an IG. In this method, the graph vertices represent the stock exchange companies that are connected by weighted edges (corresponding to the extent to which they relate to each other). In this paper, using the GARCH model (1,1) and the Clayton copula, we obtained the lower tail dependence interaction network of the first 52 companies of the Tehran Stock Exchange in terms of average market value, between June 2017 and October 2020. Then, based on the minimum spanning tree of the interaction network, we divided the companies into different communities. Using this classification, it was observed that the companies of the first group (Food Industry) and the second group (Oil Refinery) have the greatest impact on other companies. We also calculated the central indexes of the minimum spanning tree for each company. According to the results, the companies of the third group (Steel) have the highest average in the central indicators.

show abstract

“…fGns are known as specific random series with the range of anti-persistent, white noise, and persistent behavior where the so-called Hurst exponent has relevance. The Hurst exponent is a criterion which informs to what extent two time-series are coupled in various time-scales [33][34][35]. The pattern of some measurements in network science shows that there is coupled information embedded in the joint systems.…”

mentioning

confidence: 99%

“…Based on the segregation of those networks, the information transitions and measurements with closer values are revealed. The coupling and cross-correlation in financial time-series intrinsically contain scaling behaviors [33][34][35]. Those scaling behaviours not only emerge in temporal aspects, but they also appear in higher statistical moments of price return distributions.…”

mentioning

confidence: 99%

Mapping coupled time-series onto a complex network

Ardalankia¹,

Askari²,

Sheykhali³

et al. 2020

EPL

Self Cite

View full text Add to dashboard Cite

In order to extract hidden joint information from two possibly uncorrelated time-series, we explored the measures of network science. Alongside common methods in time-series analysis of the economic markets, the mapping joint structure of two time-series onto a network provides insight into hidden aspects embedded in the couplings. We quantise the amplitude of two time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a quantised amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the links in the network. The frequency of occurrences forms the weighted links. In order to extract information, we need to measure to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure to what extent the couplings inherit their charachteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series. The results show that the couplings of markets possess some features which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying network's topological and statistical measures and the deformation ratio in joint probability distribution, we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncorrelated markets may possess some joint patterns with each other. Thereby, those markets should be examined as coupled and weakly coupled markets.

show abstract

Scaling features of price–volume cross correlation

Cited by 6 publications

References 61 publications

What do we know about informational efficiency? Three puzzles and the new direction forward

What do we know about informational efficiency? Three puzzles and the new direction forward

Investigating the Importance of Different Companies of Tehran Stock Exchange using Lower Tail Dependency based Interaction Network

Mapping coupled time-series onto a complex network

Contact Info

Product

Resources

About