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Entropy
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Complexity in Financial Networks
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Complexity in Financial Networks

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2317

Special Issue Editors

Dr. Shupei Huang

E-Mail Website
Guest Editor
School of Economics and Management, China University of Geosciences, Beijing 100083, China
Interests: financial complexity; mining finance; resource and environmental economics and management; managing scientific complexity
Prof. Dr. Brian Lucey

grade E-Mail Website
Guest Editor
Trinity Business School, Trinity College Dublin, Dublin, Ireland
Interests: applied finance; behavioral finance; corporate finance derivatives; finance theory; financial management; integrative cases in finance; introduction to finance; introduction to organizations and management investments port
Dr. Xueyong Liu

E-Mail Website
Guest Editor
School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China
Interests: energy economics; time series analysis; Chinese economic studies; energy investment; energy policy; networks; econophysics; complex systems
Dr. Xinya Wang

E-Mail Website
Guest Editor
University of International Business and Economics, Beijing 100029, China
Interests: financial market; financial network; financial complexity; financial risk

Special Issue Information

Dear Colleagues,

A financial system is a type of nonlinear dynamic system, but it is much more complex since human behavior is involved. One significant and effective way to explore and understand nonlinear dynamic systems is through complex network theory. By considering the financial system as a multilayer network with heterogeneous entities, including various financial markets, institutions, and stakeholders from different countries and regions, all these entities interact with each other through financial activities determined mainly by the available information. This network understanding of the financial system sheds light on the complexity of the financial system following the 2008 global financial crisis.

Information theory is a branch of applied mathematics that involves the quantification of information, while financial complexity is closely related to information. Specifically, key concepts from information theory involving entropy and mutual information offer effective tools for measuring uncertainties associated with a random variable and how knowledge about one random variable reduces uncertainty about another. All these contents are well aligned with the uncertainty and complex nature of the financial network.

Currently, the entire financial system is accelerating to face the occurrence of various uncertainties, such as geopolitical tensions (Israel and Iran in 2023), the Russian–Ukrainian crisis (since February 2022), and long COVID-19 (since 2020), leading to the highest degrees of complexity and uncertainties observed so far in the financial system. Therefore, it is necessary to further develop tools based on complexity, network, and information theory that could work well to complement existing economic modeling approaches.

Here, we collect various theoretical, modeling, and empirical contributions from the field of financial network. In particular, as we aim to align with the scope of the Entropy journal, manuscripts that integrate information theory are welcome.

Dr. Shupei Huang
Prof. Dr. Brian Lucey
Dr. Xueyong Liu
Dr. Xinya Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • financial network analysis
  • information spillover in financial systems
  • multilayer networks in finance
  • uncertainties in financial network

Published Papers (3 papers)

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Research

22 pages, 934 KiB  
Article
SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices
by Weifang Mao, Pin Liu and Jixian Huang
Entropy 2024, 26(6), 478; https://doi.org/10.3390/e26060478 - 30 May 2024
Abstract
The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely [...] Read more.
The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China’s stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model’s efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting. Full article
(This article belongs to the Special Issue Complexity in Financial Networks)
26 pages, 887 KiB  
Article
Underwriter Discourse, IPO Profit Distribution, and Audit Quality: An Entropy Study from the Perspective of an Underwriter–Auditor Network
by Songling Yang, Yafei Tai, Yu Cao, Yunzhu Chen and Qiuyue Zhang
Entropy 2024, 26(5), 393; https://doi.org/10.3390/e26050393 - 30 Apr 2024
Viewed by 562
Abstract
Underwriters play a pivotal role in the IPO process. Information entropy, a tool for measuring the uncertainty and complexity of information, has been widely applied to various issues in complex networks. Information entropy can quantify the uncertainty and complexity of nodes in the [...] Read more.
Underwriters play a pivotal role in the IPO process. Information entropy, a tool for measuring the uncertainty and complexity of information, has been widely applied to various issues in complex networks. Information entropy can quantify the uncertainty and complexity of nodes in the network, providing a unique analytical perspective and methodological support for this study. This paper employs a bipartite network analysis method to construct the relationship network between underwriters and accounting firms, using the centrality of underwriters in the network as a measure of their influence to explore the impact of underwriters’ influence on the distribution of interests and audit outcomes. The findings indicate that a more pronounced influence of underwriters significantly increases the ratio of underwriting fees to audit fees. Higher influence often accompanies an increase in abnormal underwriting fees. Further research reveals that companies underwritten by more influential underwriters experience a decline in audit quality. Finally, the study reveals that a well-structured audit committee governance and the rationalization of market sentiments can mitigate the negative impacts of underwriters’ influence. The innovation of this paper is that it enriches the content related to underwriters by constructing the relationship network between underwriters and accounting firms for the first time using a bipartite network through the lens of information entropy. This conclusion provides new directions for thinking about the motives and possibilities behind financial institutions’ cooperation, offering insights for market regulation and policy formulation. Full article
(This article belongs to the Special Issue Complexity in Financial Networks)
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20 pages, 3707 KiB  
Article
Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis
by Qianqian Gao, Hong Fan and Chengyang Yu
Entropy 2024, 26(5), 378; https://doi.org/10.3390/e26050378 - 29 Apr 2024
Viewed by 657
Abstract
Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China’s multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China’s banking system data in 2021. An improved PageRank [...] Read more.
Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China’s multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China’s banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China’s multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks’ transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions. Full article
(This article belongs to the Special Issue Complexity in Financial Networks)
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