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Complexity in Economics and Finance: New Directions and Challenges

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 16941

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Special Issue Editors

Dr. Paolo Barucca

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Guest Editor

Department of Computer Science, University College London, London WC1E 7HU, UK
Interests: complexity theory in finance and economics; temporal networks; statistical physics; machine learning

Prof. Dr. Irena Vodenska

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Guest Editor

Administrative Sciences Department, Metropolitan College, Boston University, Boston, MA 02215, USA
Interests: complexity theory in finance and economics; responsible and ESG investing; machine learning; natural language processing

Dr. Giulio Cimini

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Guest Editor

Department of Physics, University of Rome di Tor Vergata, 00133 Rome, ‎Italy
Interests: complexity theory in finance and economics; network reconstruction and validation; dynamics on networks; graph neural networks

Dr. Carolina Mattsson

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Guest Editor

Computational Network Science Lab, Leiden University, 2311 EZ Leiden, The Netherlands
Interests: complexity theory in finance and economics; network science; payment systems; local development

Special Issue Information

Dear Colleagues,

Economic and financial systems form the basis of the wellbeing of our society. However, several recent events, from the global financial crisis of 2007/08 to the supply chain disruptions due to COVID-19 and the current war in Ukraine, have highlighted how the complex pattern of interconnections that characterizes these systems allows local failures to spread to the system as a whole. Over the past 15 years, complex systems and network science have played a key role in our understanding of the systemic dimension of such risks, as well as in devising strategies to improve the resilience and stability of such systems through a fruitful interplay of basic research and policy applications. Furthermore, researchers active in these fields have produced effective tools with which to understand and predict the patterns of economic development and innovation, as well as to further the societal aim of attaining more equal, sustainable and green growth. Rapidly emerging digital technologies and financial infrastructures have also received considerable attention.

We invite papers on topics including, but not limited to:

Statistical and probabilistic methods in economics and finance;
Economic behavior, market dynamics and agent-based modeling;
Empirical and big data analysis of economic and financial systems;
Network modeling and contagion dynamics for economic and financial relations: production and trade, supply chains, bank–firm lending, bilateral exposures, derivatives and CDS, portfolios of asset ownership, stock price correlation and monetary transactions;
Multilayer or interconnected network representation of such systems;
Early-warning signal detection and network reconstruction techniques;
Quantification of systemic risk and policy implications;
Economic complexity, development and innovation patterns and green economy;
Sustainability, inequality, ESG and socially responsible investing;
Fintech, cryptocurrencies and retail trading.

Dr. Paolo Barucca
Prof. Dr. Irena Vodenska
Dr. Giulio Cimini
Dr. Carolina Mattsson
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

economic networks
supply networks
financial networks
temporal networks
probabilistic graphical models
statistical physics

Published Papers (10 papers)

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Research

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24 pages, 637 KiB

Open AccessArticle

Ideal Agent System with Triplet States: Model Parameter Identification of Agent–Field Interaction

by Christoph J. Börner, Ingo Hoffmann and John H. Stiebel

Entropy 2023, 25(12), 1666; https://0-doi-org.brum.beds.ac.uk/10.3390/e25121666 - 16 Dec 2023

Cited by 1 | Viewed by 965

Abstract

On the capital market, price movements of stock corporations can be observed independent of overall market developments as a result of company-specific news, which suggests the occurrence of a sudden risk event. In recent years, numerous concepts from statistical physics have been transferred to econometrics to model these effects and other issues, e.g., in socioeconomics. Like other studies, we extend the approaches based on the “buy” and “sell” positions of agents (investors’ stance) with a third “hold” position. We develop the corresponding theory within the framework of the microcanonical and canonical ensembles for an ideal agent system and apply it to a capital market example. We thereby design a procedure to estimate the required model parameters from time series on the capital market. The aim is the appropriate modeling and the one-step-ahead assessment of the effect of a sudden risk event. From a one-step-ahead performance comparison with selected benchmark approaches, we infer that the model is well-specified and the model parameters are well determined. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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19 pages, 1057 KiB

Open AccessArticle

Market Impact Analysis of Financial Literacy among A-Share Market Investors: An Agent-Based Model

by Rongtian Zhou, Xiong Xiong, Bàrbara Llacay and Gilbert Peffer

Entropy 2023, 25(12), 1602; https://0-doi-org.brum.beds.ac.uk/10.3390/e25121602 - 29 Nov 2023

Cited by 1 | Viewed by 782

Abstract

Financial literacy has become increasingly crucial in today’s complex financial markets. This paper explores the impact of financial literacy on the stock market by establishing an artificial financial market that aligns with the characteristics of the Chinese A-share market using agent-based modeling. The study incorporates financial literacy into investors’ mixed beliefs and simulates their behavior in the market. The results show that improving individual investors’ financial literacy can improve market quality and investor performance, as well as reduce the unequal distribution of wealth to some extent. However, the phenomenon of speculative trading and irrational behavior in the market can pose potential risks that require regulatory measures. Thus, policy recommendations to improve individual investors’ financial literacy and establish corresponding regulatory measures are proposed. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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25 pages, 5402 KiB

Open AccessArticle

A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network

by Yu Lin and Ben Liu

Entropy 2023, 25(11), 1500; https://0-doi-org.brum.beds.ac.uk/10.3390/e25111500 - 30 Oct 2023

Viewed by 1024

Abstract

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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35 pages, 4032 KiB

Open AccessArticle

Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning

by Fatih Ozhamaratli and Paolo Barucca

Entropy 2023, 25(7), 977; https://doi.org/10.3390/e25070977 - 25 Jun 2023

Viewed by 973

Abstract

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents’ profiles and incorporates the parameterisation of agents’ behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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18 pages, 2352 KiB

Open AccessArticle

Forecasting Commodity Market Synchronization with Commodity Currencies: A Network-Based Approach

by Nicolas S. Magner, Nicolás Hardy, Jaime Lavin and Tiago Ferreira

Entropy 2023, 25(4), 562; https://0-doi-org.brum.beds.ac.uk/10.3390/e25040562 - 25 Mar 2023

Viewed by 1386

Abstract

This paper shows that some commodity currencies (from Chile, Iceland, Norway, South Africa, Australia, Canada, and New Zealand) predict the synchronization of metals and energy commodities. This relationship links the present-value theory for exchange rates and its connection with commodity export economies’ fundamentals, where prospective commodity price fluctuations affect exchange rates. Predicting commodity market return synchronization is critical for dealing with systemic risk, market efficiency, and financial stability since synchronization reduces the benefits of diversification and increases the probability of contagion in financial markets during economic and financial crises. Using network methods coupled with in-sample and out-of-sample econometrics models, we find evidence that a fall in the return of commodity-currencies (dollar appreciation) predicts an increase in commodity market synchronization and, consequently, in commodity market systemic risk. This discovery is consistent with a transitive capacity phenomenon, suggesting that commodity currencies have a predictive ability over commodities that extend beyond the commodity bundle that a country produces. The latter behavior would be exacerbated by the high financialization of commodities and strong co-movement of commodity markets. Our paper is part of a vigorously growing literature that has recently measured and predicted systemic risk caused by synchronization, combining a complex systems perspective and financial network analysis. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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18 pages, 3117 KiB

Open AccessArticle

Dollar-Yuan Battle in the World Trade Network

by Célestin Coquidé, José Lages and Dima L. Shepelyansky

Entropy 2023, 25(2), 373; https://0-doi-org.brum.beds.ac.uk/10.3390/e25020373 - 17 Feb 2023

Cited by 2 | Viewed by 2530

Abstract

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in world trade. However, the rise of the Chinese economy has recently led to the emergence of trade transactions in Chinese yuan. Here, we mathematically analyze how the structure of international trade flows would favor a country to trade whether in US dollar or in Chinese yuan. The trade currency preference of a country is modeled as a binary variable with the properties of a spin in an Ising model. The computation of this trade currency preference is based on the world trade network built from the 2010–2020 UN Comtrade data and is determined by two multiplicative factors: the relative weight of trade volume exchanged by the country with its direct trade partners and the relative weight of its trade partners in global international trade. The performed analysis, based on the convergence of the Ising spin interactions, shows that from 2010 to present a transition took place, and the majority of the world countries would now have a preference to trade in Chinese yuan if one only considers the world trade network structure. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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12 pages, 1088 KiB

Open AccessArticle

Impact of the Global Fear Index (COVID-19 Panic) on the S&P Global Indices Associated with Natural Resources, Agribusiness, Energy, Metals, and Mining: Granger Causality and Shannon and Rényi Transfer Entropy

by Pedro Celso-Arellano, Victor Gualajara, Semei Coronado, Jose N. Martinez and Francisco Venegas-Martínez

Entropy 2023, 25(2), 313; https://0-doi-org.brum.beds.ac.uk/10.3390/e25020313 - 8 Feb 2023

Cited by 2 | Viewed by 3228

Abstract

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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12 pages, 1626 KiB

Open AccessArticle

Economic Trading Susceptibility: Constructing Networks of Mutual Influence through the Fitness of Countries

by Nishanth Kumar, Henrik Jeldtoft Jensen and Eduardo Viegas

Entropy 2023, 25(1), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/e25010141 - 10 Jan 2023

Cited by 1 | Viewed by 1151

Abstract

The emergence of economic blocks and the level of influence countries exert on each other are fundamental features of the 21st century globally interconnected economy. However, limited quantitative research exists measuring the level of influence among countries and quantitatively determining economic blocks. This research develops a method to quantify the mutual influence of countries by making use of relatively standard procedures for complex networks in order to assemble non-trivial networks of influences and to identify symbiotic relationships. The methods are of significant help to an enhanced understanding of the global politics of trading and associations. Moreover, we develop the Mutual Influence Robustness (MIR) metric to work together with the Economic Fitness metric to provide some level of predictive modeling for the trends and future paths of countries. Our key results show the existence of a mutually influencing network around East and Southeast Asia, developed North America, and the northern and Iberian countries. Moreover, we find that it is possible to do some level of path predictability for the fitness and mutual influence of countries. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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19 pages, 3080 KiB

Open AccessArticle

CEGH: A Hybrid Model Using CEEMD, Entropy, GRU, and History Attention for Intraday Stock Market Forecasting

by Yijiao Liu, Xinghua Liu, Yuxin Zhang and Shuping Li

Entropy 2023, 25(1), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/e25010071 - 30 Dec 2022

Cited by 10 | Viewed by 1988

Abstract

Intraday stock time series are noisier and more complex than other financial time series with longer time horizons, which makes it challenging to predict. We propose a hybrid CEGH model for intraday stock market forecasting. The CEGH model contains four stages. First, we use complete ensemble empirical mode decomposition (CEEMD) to decompose the original intraday stock market data into different intrinsic mode functions (IMFs). Then, we calculate the approximate entropy (ApEn) values and sample entropy (SampEn) values of each IMF to eliminate noise. After that, we group the retained IMFs into four groups and predict the comprehensive signals of those groups using a feedforward neural network (FNN) or gate recurrent unit with history attention (GRU-HA). Finally, we obtain the final prediction results by integrating the prediction results of each group. The experiments were conducted on the U.S. and China stock markets to evaluate the proposed model. The results demonstrate that the CEGH model improved forecasting performance considerably. The creation of a collaboration between CEEMD, entropy-based denoising, and GRU-HA is our major contribution. This hybrid model could improve the signal-to-noise ratio of stock data and extract global dependence more comprehensively in intraday stock market forecasting. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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12 pages, 4368 KiB

Open AccessPerspective

An Introduction to Complex Networks in Climate Finance

by Alexander P. Kartun-Giles and Nadia Ameli

Entropy 2023, 25(10), 1371; https://0-doi-org.brum.beds.ac.uk/10.3390/e25101371 - 22 Sep 2023

Viewed by 1068

Abstract

In this perspective, we introduce recent research into the structure and function of complex investor networks supporting sustainability efforts. Using the case of solar, wind and hydro energy technologies, this perspective explores the complexity in low-carbon finance markets, defined as markets that direct capital flows towards low-carbon technologies, using network approaches to study their structure and dynamics. Investors are modeled as nodes which form a network or higher-order network connected by edges representing projects in which joint funding or security-related insurance was provided or other investment-related interaction occurred. We review the literature on investor networks generally, particularly in the case of complex networks, and address areas where these ideas were applied in this emerging field. The complex investor dynamics which emerge from the extant funding scenarios are not well understood. These dynamics have the potential to result in interesting non-linear behaviour, growth, and decline, which can be studied, explained and controlled using the tools of network science. Full article

(This article belongs to the Special Issue Complexity in Economics and Finance: New Directions and Challenges)

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