Evidence map›Paper›PMID 40149195›Full record

ArticleEntropy (Basel, Switzerland)2025

Network Geometry of Borsa Istanbul: Analyzing Sectoral Dynamics with Forman-Ricci Curvature.

Ömer Akgüller, Mehmet Ali Balcı, Larissa Margareta Batrancea, Lucian Gaban

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. 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
–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

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.

Ömer AkgüllerDeparment of Mathematics, Faculty of Science, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey.ORCID 0000-0002-7061-2534
Mehmet Ali BalcıDeparment of Mathematics, Faculty of Science, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey.ORCID 0000-0003-1465-7153
Larissa Margareta BatranceaDepartment of Business, Babeş-Bolyai University, 7 Horea Street, 400174 Cluj-Napoca, Romania.ORCID 0000-0001-6254-2970
Lucian GabanFaculty of Economics, "1 Decembrie 1918" University of Alba Iulia, 510009 Alba Iulia, Romania.ORCID 0000-0001-7317-4798

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul-industrial, services, technology, banking, and electricity-using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman-Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods (k=1,2,3) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks-such as inflation surges, interest rate hikes, and currency depreciation-vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators.

Indexed as

financial networksForman–Ricci curvaturemutual informationsectoral dynamics

Identifiers

PMID40149195
PMCPMC11941537

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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.