The Stock Market Model with Delayed Information Impact from a Socioeconomic View.

Zhiting Wang, Guiyuan Shi, Mingsheng Shang, Yuxia Zhang

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The Linear Relationship Model with LASSO for Studying Stock Networks.Entropy (Basel, Switzerland) · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhiting WangPhysics and Photoelectricity School, South China University of Technology, Guangzhou 510640, China.

Guiyuan ShiInternational Academic Center of Complex Systems, Beijing Normal University at Zhuhai, Zhuhai 519087, China.

Mingsheng ShangChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.

Yuxia ZhangPhysics and Photoelectricity School, South China University of Technology, Guangzhou 510640, China.

Funding

National Natural Science Foundation of China No. 61503140

6 · The paper itself

Abstract

Finding the critical factor and possible "Newton's laws" in financial markets has been an important issue. However, with the development of information and communication technologies, financial models are becoming more realistic but complex, contradicting the objective law "Greatest truths are the simplest." Therefore, this paper presents an evolutionary model independent of micro features and attempts to discover the most critical factor. In the model, information is the only critical factor, and stock price is the emergence of collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provides a new idea for studying critical factors and core structures in the financial markets.

Indexed as

collective intelligencedetrended cross-correlation analysiseconophysicsemergent propertyfinancial complexitystock correlation

Identifiers

PMID34356434

DOI10.3390/e23070893

PMCPMC8304283

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.