Evidence map›Paper›PMID 35741529›Full record

ArticleEntropy (Basel, Switzerland)2022

The Linear Relationship Model with LASSO for Studying Stock Networks.

Muzi Chen, Hongjiong Tian, Boyao Wu, Tianhai Tian

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Muzi ChenSchool of Management Science and Engineering, Central University of Finance and Economics, Beijing 102206, China.
Hongjiong TianDepartment of Mathematics, Shanghai Normal University, Shanghai 200234, China.
Boyao WuSchool of Banking and Finance, University of International Business and Economics, Beijing 100029, China.ORCID 0000-0003-2186-808X
Tianhai TianSchool of Mathematics, Monash University, Clayton, VIC 3800, Australia.ORCID 0000-0001-6191-0209

Funding

National Natural Science Foundation of China 11671266, 71673315, 71850008, 72003110 and 11931019
6 · The paper itself

Abstract

The correlation-based network is a powerful tool to reveal the influential mechanisms and relations in stock markets. However, current methods for developing network models are dominantly based on the pairwise relationship of positive correlations. This work proposes a new approach for developing stock relationship networks by using the linear relationship model with LASSO to explore negative correlations under a systemic framework. The developed model not only preserves positive links with statistical significance but also includes link directions and negative correlations. We also introduce blends cliques with the balance theory to investigate the consistency properties of the developed networks. The ASX 200 stock data with 194 stocks are applied to evaluate the effectiveness of our proposed method. Results suggest that the developed networks not only are highly consistent with the correlation coefficient in terms of positive or negative correlations but also provide influence directions in stock markets.

Indexed as

LASSOlinear relationship modelnegative correlationstock relationship network

Identifiers

PMID35741529
PMCPMC9222946

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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.