Complex statistical signatures of stock markets: Scaling laws, multifractality, temporal recurrence networks of price and the Ising model
Clicks: 1
ID: 287227
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #3,511 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Financial markets, such as stock markets, are good representative examples of complex systems because they are composed of a large number of agents interacting in a non-linear manner. As such, there has now been a paradigm shift towards the treatment of stock markets as complex systems. A defining feature of complex systems is emergence, presenting themselves as statistical signatures in the macroscopic scale. In financial markets, this is observed in the main stylized facts: fat-tails in the distribution of large returns, volatility clustering, absence of autocorrelation in daily returns, and multifractality. Using log-returns data on sixteen global market indices over varying time scales, we verify the main stylized facts and found empirical results consistent with previous works. In addition to the main stylized facts, this work has uncovered nearly-universal statistical properties in financial markets from the perspective of the theory of records and complex networks. By creating a temporally directed network of prices using global stock market data, we recover robust power-law statistics in the distribution of records and link separation times, and find Poisson statistics in the in- and out-degree link distributions, quantifying a high level of activity in financial markets, i.e., price recurrences are retrieved over fewer connections than that of completely random sequences. These empirical facts are benchmarks of complex and agent-based financial market models. As such, valid models must be able to retrieve the main stylized facts. In this work, we demonstrate that the self-organizing Ising model is one such model capable of replicating the empirical stylized facts. We generate synthetic return series over a broad set of model parameters (bmax, σmax,CV) and identified bmax ∈ [0.2,03], σmax ∈ [0.15,0.45] and CV = 0.50 as the range of parameters that retrieve the main stylized facts with a minimal degree of multifractality. These simulated findings imply that agents in stock markets tend to follow the external news, conform with neighborhood trends, and that agents are neither too timid or too bold in introducing idiosyncratic actions when making decisions.
| Reference Key |
persistent_1760660959_68f18ddfc9307
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Antenorcruz, Jude Maria V. |
| Journal | Malay Journal |
| Year | 2025 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.