Financial Networks Research Articles

How does node centrality in a financial network affect asset price prediction?

In complex financial networks, systemically important nodes usually play crucial roles. Asset price forecasting is important for describing the evolution of a financial network. Naturally, the question arises as to whether node centrality affects the effectiveness of price forecasting. To explore this, we examine networks composed of major global assets and investigate how node centrality affects price forecasting using a hybrid random forest algorithm. Our findings reveal two counterintuitive phenomena: (i) factors with low centrality usually have better prediction ability, and (ii) nodes with low centrality can be predicted more accurately in direction. These unexpected observations can be explained from the perspective of information theory. Moreover, our research suggests a criterion for factor selection: when predicting an asset price in a complex system, factors with low centrality should be selected rather than only factors with high centrality.

Contagion in debt and collateral markets

This paper investigates contagion in financial networks through collateralized debt and its effects on social welfare. Our model incorporates contagion through both counterparty debt exposures and endogenous collateral asset pricing. We find that collateral mitigates counterparty exposures and reduces social inefficiency when faced with negative shocks, but not always. We also show the importance of the interaction between the level of collateral and network structure as contagion can change dramatically depending on that interaction. The model also provides policy-relevant collateral-to-debt ratios (haircuts) to attain robust and fully insulated macroprudential states for any network and also the optimal collateral ratio to attain full insulation for a specific network.

Dynamics in digital finance and its impact on SME financing

Employing the Complex Adaptive System (CAS) framework, we analyzed the evolution of digitally transformed financial networks, underscoring the pivotal role of adaptation and learning among network participants. Government funds supporting banks and platforms in digital transformation prove more effective than direct subsidies to SMEs. Achieving sustainable development requires an optimal balance between investment and efficiency due to the impact of digitalization costs on constructing digital financial networks. The study advocates for a long-term microcredit financing mode tailored to SMEs, aiding them in maintaining financial stability. Additionally, intermediary nodes within the network, such as credit rating companies, can facilitate information sharing, thereby fostering the growth of digital financial networks. This heightened information sharing enhances the payoff for each subject and reduces financing constraints. Furthermore, the study establishes that digital financial networks can alleviate the repercussions of unforeseen events, such as the COVID-19 pandemic. Finally, empirical analysis of the Beijing University Inclusive Finance Index data and financial records of Chinese SMEs listed on the New Third Board from 2016 to 2018 confirms that digital finance can alleviate the financing constraints of SMEs. In summary, the progression of digital financial networks is intricate, necessitating a thorough comprehension of diverse factors. The government is encouraged to guide the enhancement of relevant systems and regulations, bolstering supervision, and facilitating the high-quality development of digital finance networks.

Sustainable supply chain financing models: Integrating banking for enhanced sustainability

Sustainable supply chain financing has emerged as a pivotal strategy in contemporary business paradigms, aiming to harmonize economic growth with environmental stewardship and social responsibility. This review delves into the integration of banking mechanisms within sustainable supply chain financing models to foster enhanced sustainability across diverse industries. In recent years, the concept of sustainability has gained momentum, prompting organizations to reevaluate their operational frameworks to mitigate environmental impacts and address societal concerns. Supply chain financing, a financial tool facilitating transactions among interconnected entities within the supply chain, plays a crucial role in advancing sustainability objectives. By integrating banking institutions into these models, businesses can leverage financial expertise, resources, and networks to bolster sustainable practices throughout the supply chain. This review explores various dimensions of sustainable supply chain financing models, highlighting the significance of banking integration. Firstly, it elucidates the evolving landscape of sustainability in supply chains, emphasizing the imperative for concerted efforts to reconcile profitability with environmental preservation and social welfare. Secondly, it examines the multifaceted benefits of integrating banking institutions into supply chain financing mechanisms, including access to capital, risk mitigation, and expertise in sustainable finance. Moreover, the review discusses the emergence of innovative financial instruments such as green bonds, sustainability-linked loans, and supply chain finance programs tailored to promote sustainability goals. These instruments not only provide financial incentives for sustainable initiatives but also foster transparency and accountability throughout the supply chain ecosystem. Furthermore, the review addresses challenges and opportunities associated with integrating banking into sustainable supply chain financing, including regulatory complexities, technological advancements, and stakeholder collaboration. It underscores the need for strategic partnerships between businesses, financial institutions, and regulatory bodies to navigate these challenges effectively and realize the full potential of sustainable supply chain financing. The integration of banking institutions into sustainable supply chain financing models presents a promising avenue for advancing sustainability agendas across industries. By harnessing financial innovation and collaboration, organizations can foster resilience, efficiency, and ethical conduct within their supply chains, contributing to a more sustainable and inclusive global economy.

Artificial Intelligence and Machine Learning in Financial Services: Risk Management and Fraud Detection

The field of financial risk management is undergoing a significant transformation due to the advancements in artificial intelligence (AI) and the underlying machine learning (ML) techniques that provide the foundation of AI. These developments hold the potential to revolutionize the way the user’s approach and address financial risk. The expansion of AI-driven solutions has opened up various opportunities for comprehending and managing risk. These opportunities encompass a wide range of activities, such as determining appropriate lending amounts for customers in banking, issuing warning signals to financial market traders regarding position risk, identifying instances of customer and insider fraud, enhancing compliance efforts, and mitigating model risk. The prime objective of this study is to investigate the application of AI and ML in the Financial Services industry, with a specific focus on Risk Management and Fraud Detection. This study proposes an intelligent and distributed approach for detecting Internet financial fraud using Big Data. The methodology entails the utilization of the graph embedding algorithm Node2Vec for the purpose of acquiring knowledge and representing the structural characteristics of the financial network graph in the form of compact vectors with reduced dimensions. This facilitates the intelligent and effective categorization and forecasting of data samples from a dataset of significant magnitude through the utilization of a deep neural network. Based on the study's findings, it was observed that the F1-Score test outcomes obtained from the Node2Vec algorithm range from 67.1% to 73.4%. These results surpass the outcomes achieved by the other two algorithms used for comparison. This finding demonstrates that Node2Vec has greater stability in terms of overall performance and yields superior categorization outcomes.

Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis.

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.

Socio-economic inequities in mental health problems and wellbeing among women working in the apparel and floriculture sectors: testing the mediating role of psychological capital, social support and tangible assets

BackgroundIt is still unknown whether the mechanisms proposed by the Reserve Capacity Model (RCM) explaining socio-economic health and wellbeing inequities in high income countries can be applied to low-income countries. This study investigates whether different reserve capacities (intra-, inter-personal, and tangible) can explain the association between relative socio-economic position (SEP) and wellbeing outcome measures among Ethiopian women working in Foreign Direct Investment (FDI).MethodUsing a cross-sectional design, we collected quantitative survey data among 2,515 women working in the apparel and floriculture sectors in Ethiopia, measuring GHQ-12 mental health problems, multi-dimensional wellbeing, relative SEP, psychological capital (PsyCap), social support (emotional and financial social support network), and tangible assets (e.g., owning mobile phone, having access to toilet facilities). We used cluster-adjusted structural equation modelling to test whether PsyCap, social support, and/or tangible assets mediate the association between relative SEP (IV) and GHQ-12 mental health problems and multi-dimensional wellbeing (DVs).ResultsPsyCap and the size of the financial support network significantly mediate the socio-economic gradient in both wellbeing outcomes. The size of the emotional social support network shows no association with multi-dimensional wellbeing and shows an unexpected negative association with GHQ-12 mental health problems scores, including a significant mediation effect. Tangible assets show no association with the wellbeing outcome measures and do not mediate socio-economic mental health problems and wellbeing inequities.ConclusionsThe RCM can be applied in low-income countries, although in unexpected ways. Similar to findings from high-income countries, PsyCap and size of the financial social support network show significant mediation effects in explaining mental health problems and wellbeing inequities in Ethiopia. These reserves could therefore serve as a buffer for socio-economic inequities in mental health and wellbeing and can therefore assist in decreasing these inequities for women working in FDI sectors in Ethiopia.

On the unification of centralized and decentralized clearing mechanisms in financial networks

We analyze clearing mechanisms in financial networks in which agents may have both monetary individual assets and mutual liabilities. A clearing mechanism prescribes mutual payments between agents in a financial network to settle their mutual liabilities. The corresponding payments, summarized in a payment matrix, are made in accordance with agent-specific claims rules that stem from the vast literature on claims situations. The bottom payment matrix is the payment matrix that contains the minimal amount of payments required to clear the network. We explicitly characterize the bottom payment matrix as the result of a recursive centralized mechanism. We subsequently show that several types of decentralized clearing mechanisms lead to the bottom payment matrix as well. In fact, we show that this unification of centralized and decentralized clearing mechanisms relies on the composition property of the underlying agent-specific claims rules that dictate the payment mechanism.

Influential risk spreaders and systemic risk in Chinese financial networks

A novel approach of gravity strength centrality (GSC) model is proposed to identify the influential risk spreaders in Chinese financial networks. We also measure the systemic risk contribution of financial institutions via ΔCoVaR and detect the relationship between the risk spreading ability and the systemic risk contribution of financial institutions. Our findings show that (i) the novel GSC model has the best performance on identifying influential risk spreaders, (ii) financial institutions with larger risk spreading ability contribute more to the systemic risk, (iii) the COVID-19 pandemic has significantly enhanced the contribution of influential risk spreaders to the systemic risk.

Global Financial Investment Connectedness, ICT, and Intellectual Property Strategies: A Country-Level Empirical Analysis

This study investigated the direct and indirect impacts of financial investment connectedness and Information and Communication Technology (ICT) on countries’ intellectual property (IP) strategies. By utilizing the panel logit model on longitudinal country-level data, we found that countries’ positions in the global financial investment network significantly affect their IP strategies. Furthermore, ICT usage weakens the IP strategies’ reliance on global financial investment connectedness. This study is among the first to link financial investment connectedness and ICT to intellectual property strategy. The implications for governments managing financial investment portfolios and making intellectual property strategies are derived.

Financial shock transmission in China's banking and housing sectors: A network analysis

Rising housing prices in China present a significant risk to financial and economic stability. Our study investigates this "gray rhino" risk by analyzing the financial shock transmission network, using a novel high-dimensional network approach applied to data from 45 real estate firms and 15 banks from 2011 to 2023. We discover that the network's connectedness is predominantly short-term and internal to each sector, with weaker inter-sectoral links. Key macroeconomic indicators, notably monetary policy uncertainty, and inflation, significantly drive this connectedness. Our results indicate that, despite concerns, China's real estate risk remains within manageable bounds, offering critical insights for market stakeholders.

Early Warning of Financial Risk Management using AGRNN-COA Approach

Financial risk management was used to protect an entity's financial performance and stability, entails detecting, evaluating, and reducing potential risks, such as credit defaults and market swings. It includes tactics including hedging, diversification, and risk limitation. Financial authorities must immediately responsive for financial risk management system that works with the current economic development. This paper proposes a hybrid approach for early pre-alarm warning of financial risk management system and economic development. The proposed hybrid approach is the combined performance of both the Alternating Graph-Regularized Neural Network (AGRNN) and Crayfish Optimization Algorithm (COA).Commonly it is named as AGRNN-COA technique. The major objective of the proposed approach is to give early pre-alarm warning of financial risk management. AGRNN is designed to give early pre-alarm warning of the financial system of the country. The financial risk management from the AGRNN are optimized by using the COA. By then, the proposed model is implemented in the MATLAB/Simulink working platform and the execution is calculated with the present procedures. The proposed method shows better results in all existing like Financial Risk Management Supervised Algorithm (FRM-SVM), Financial Risk Management Deep Neural Network (FRM-DNN) and Financial Risk Management Heap Based Optimization (FRM-HBO). The accuracy level of proposed FRM-AGRNN-COA approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed FRM-AGRNN-COA approach is 99% and 97%. The error rate of the proposed FRM-AGRNN-COA approach is 1.8%, which is very less compared to other existing techniques. From the result, it is conclude that the proposed approach based error is less compared to existing techniques.

Globalization in Practice: Does political participation change a financial centre’s competitiveness?

What role does polyarchy (and thus increased democracy) play in aiding the development of an international financial centre? We find support for decades of theorising, in the globalization literatures as well as in others, that some jurisdictions use autocracy (less polyarchy) to help grow out their financial centres. We look at the growth of these financial centres as the extent to which they attract more funds from abroad (cross-border bank liabilities). Polyarchy decreases as other international financial centres’ centrality in the global financial centre network expands. Polyarchy increases in most jurisdictions over time because some financial centres rely on increasingly polyartic governance as a way to foster financial innovation through increased participation by non-previously powerful sectors. Namely, the growth of an international financial centre’s centrality in global financial networks relies on tapping down on polyarchy. Yet, such polyarchy – when used by some very central jurisdictions to remain central – “spreads.” We model such a relationship between polyarchy and centrality in the global financial network, basically as a process of the process of globalization itself. These results could impact decisions ranging from Brexit to Hong Kong’s autonomy in its post-2047 period.

Interplay of crises: Unpacking intraday spillovers in oil and European equities in the shadow of the COVID-19 and the Ukraine-Russia war

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval to analyze the interplay between crude oil market returns and the Stoxx 600 index returns, including sectors such as auto, basic material, banks, chemicals, food and beverage, health, industrials, insurance, oil and gas, retail, real estate, technology (tech), telecommunication (telecom), and utilities. The sample period is January 3, 2022, to March 25, 2022. Our findings reveal a substantial degree of connectedness within this financial network, with specific sectors—auto, chemicals, food, industrials, insurance, real estate, retail, tech, and telecom—acting as net transmitters of shocks, while other sectors assume the role of net receivers. The Russia-Ukraine war is a significant driver of these interconnected dynamics. Spillovers are most prevalent in the medium-term horizon, with the time dimension affecting the role of sectors and oil. Furthermore, our research highlights the potential benefits of adding oil assets to portfolios, enhancing risk-adjusted performance. However, ongoing risk management remains crucial due to the dynamic hedge ratios. This study offers practical insights for investors and policy makers, aiding risk management and investment strategies in turbulent markets.

Transmission Risk of Stock Price Fluctuations Among Industries in Complex Financial Networks

Transmission Risk of Stock Price Fluctuations Among Industries in Complex Financial Networks

Post global financial crisis multiplex financial network structure longevity: Does it predict re-globalization? Empirical investigation for 234 countries

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable un-ally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits re-globalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its’ structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009–2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009–2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post- global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth.

Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach

There are complex risk correlations between financial sectors, and the risks generated by different financial sectors propagate, accrue, and cluster through the network of correlations, posing a threat to the entire financial system. This research constructs static and dynamic cross-sector risk spillover networks using VAR and generalized variance decomposition, and forecasts the evolution of risk spillover networks using recurrent neural network models such as RNN and LSTM. The results indicate that the risk spillover network will change rapidly in response to risk events, and that the total volatility spillover will increase during times of crisis, while banks and securities are the most important risk propagating and receiving sectors. The LSTM model can achieve more effective dynamic prediction of the multidimensional network, and the predicted network is essentially consistent with the actual network. According to the findings of the study, forecasting changes in the structure of financial sector networks may provide early warning of systemic financial risk and assist to a better understanding of the risk link between financial sectors.

The Carbon Emission Reduction Effect and Spatio-Temporal Heterogeneity of the Science and Technology Finance Network: The Combined Perspective of Complex Network Analysis and Econometric Models

With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China’s STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents’ affluence, energy consumption, industrial structure, and environmental pollution on CE.

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been developed to effectively capture the evolving patterns in dynamic graphs. While these models have demonstrated their superiority, being widely adopted in various important fields, their vulnerabilities against adversarial attacks remain largely unexplored. In this paper, we propose T-SPEAR, a simple and effective adversarial attack method for link prediction on continuous-time dynamic graphs, focusing on investigating the vulnerabilities of TGNNs. Specifically, before the training procedure of a victim model, which is a TGNN for link prediction, we inject edge perturbations to the data that are unnoticeable in terms of the four constraints we propose, and yet effective enough to cause malfunction of the victim model. Moreover, we propose a robust training approach T-SHIELD to mitigate the impact of adversarial attacks. By using edge filtering and enforcing temporal smoothness to node embeddings, we enhance the robustness of the victim model. Our experimental study shows that T-SPEAR significantly degrades the victim model's performance on link prediction tasks, and even more, our attacks are transferable to other TGNNs, which differ from the victim model assumed by the attacker. Moreover, we demonstrate that T-SHIELD effectively filters out adversarial edges and exhibits robustness against adversarial attacks, surpassing the link prediction performance of the naive TGNN by up to 11.2% under T-SPEAR. The code and datasets are available at https://github.com/wooner49/T-spear-shield

A Mathematica-Based Interface for the Exploration of Inter- and Intra-Regional Financial Flows

This work surveys the use of directed weighted graphs in conducting comparative static analyses. The paper discusses the implementation of a computer-aided process for building spreadsheet-based graph models for inter- and intra-regional financial flows. The graph-theoretic techniques are programmed to enable the interactive visualization and analysis of financial data using Wolfram technologies (i.e., Mathematica software v. 11.3 or later, Wolfram player v. 12 or later). The paper describes the workflow for several interactive visualizations applicable to financial networks. The author provides four programs, written in the Wolfram language, that customize input–output financial models by combining the Manipulate command with built-in Mathematica functions and functions of the IGraph package (IGraph/M). The study is a tutorial article for the generation of a suite of visual schemes that provide patterns, practices, and roadmaps of the financial markets across the globe.