Financial Networks Research Articles

Analysis of Financial Data Risk and Network Information Security by Blockchain Technology and Edge Computing

To solve the problem of computer network security, this study creatively establishes a new information security (IS) strategy recommendation system and a cooperative intrusion detection system (CIDS) based on blockchain (BC) and edge computing (EC) technologies. The innovation lies in the proposal of the real-time CIDS method. First, the related technologies and theories of the Bayesian network, deep learning (DL), and neural network (NN) are analyzed. Second, a real-time IS strategy recommendation system is constructed using NNs and expert systems, and CIDS is built on BC and DL networks. Finally, experiments compare recurrent NN and long-short term memory (LSTM). The research results reveal that the accuracy of LSTM can reach about 93%. The comparison between the single intrusion detection system (SIDS) and CIDS denotes that CIDS is better than SIDS in detection accuracy, and the specific performance is 97.54% > 91%. The proposed model based on business continuity management can help enterprises better protect financial data and establish a complete protection system of network information security (NIS). In this study, a CIDS with good performance is obtained, which lays a foundation for the further development of the NIS.

The Analysis of Financial Network Transaction Risk Control Based on Blockchain and Edge Computing Technology

The Analysis of Financial Network Transaction Risk Control Based on Blockchain and Edge Computing Technology

Thermodynamical Modeling Stability of Financial Network Based on Their Structure on Fractal and Rule Driven Spin Lattice

Thermodynamical Modeling Stability of Financial Network Based on Their Structure on Fractal and Rule Driven Spin Lattice

The Role of DevOps in Financial Network Security

The Role of DevOps in Financial Network Security

Can green credit policies improve the digital transformation of heavily polluting enterprises: A quasi-natural experiment based on difference-in-differences.

The digital transformation of the manufacturing industry is closely linked to green credit policies, which jointly promote the development of the manufacturing industry towards a more environmentally friendly, efficient and sustainable development. Based on the research sample of China's manufacturing A-share listed companies from 2008 to 2022, this paper uses the difference-in- differences (DID) method to analyze the impact of green credit policies on the digital transformation of heavily polluting enterprises. The results show that green credit policies significantly inhibit the digital transformation of heavily polluting enterprises. In terms of the adjustment mechanism, the R&D investment of enterprises and the financial background of senior executives have weakened the inhibitory effect of green credit policies on the digital transformation of heavily polluting enterprises. When the R&D investment is low, the inhibitory effect of the policy is more significant, but with the increase of R&D investment, the inhibitory effect of the policy gradually weakens, indicating that there is a substitution relationship between the two. Enterprises with senior financial expertise have a deeper understanding of financial feasibility and benefit analysis, and are more receptive to the high-risk investment of digital transformation, while their financial network resources can help broaden financing channels, reduce financing constraints, and further reduce the financial difficulty of digital transformation. In addition, the green credit policy has a stronger inhibitory effect on the digital transformation of non-state-owned enterprises and enterprises that do not hold bank shares. The conclusions of this paper are expected to provide some policy implications for the subsequent green credit policies in promoting the digital transformation of the manufacturing industry.

Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies.

The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences.

Central Limit Theorems for Price-Mediated Contagion in Stochastic Financial Networks

Central Limit Theorems for Price-Mediated Contagion in Stochastic Financial Networks

Limit Theorems for Default Contagion and Systemic Risk

We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network subjected to an exogenous macroeconomic shock. We show that under certain regularity assumptions, the default cascade model can be transformed into a death process problem represented by a balls-and-bins model. We state various limit theorems regarding the final size of default cascades. Under appropriate assumptions on the degree and threshold distributions, we prove that the final sizes of default cascades have asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions, which enable us to provide systemic risk measures in relation to the structure and heterogeneity of the financial network. Lastly, we demonstrate how these results can be utilized by a social planner to optimally target interventions during a financial crisis given a budget constraint and under partial information of the financial network.

Bayesian inference of financial networks

Network data arises naturally in a wide variety of applications in different fields. In this article we discuss in detail the statistical modeling of financial networks. The structure of such networks red has not been studied thoroughly in the past, mainly due to limited accessible data. We explore the structure of a real trading network corresponding to transactions within the natural gas future market over a four-year period. The detection of meaningful communities of actors within networks is particularly relevant to understand the topology of a complex system like this. We explore the usage of stochastic block models in conjunction with a nonparametric Bayesian approach in order to identify clusters of traders in a flexible modeling framework. Our findings strongly indicate that the proposed models are highly reliable at detecting community structures.

Corporate Utilization of Bank Credit Financing Advantages under Supply Chain Finance and Industrial Policy Paths

Abstract Bank credit financing is an effective method to promote the development of enterprises, and this paper discusses the use of enterprises in the credit financing advantage supported by supply chain financial networks and industrial policy in the new era. In the paper, the strategic choice of enterprises using bank credit financing is analyzed, the Nash equilibrium point of the bank and the enterprise is modeled by calculus, and the game evolution model of enterprise use of credit financing advantage is constructed to explore the equilibrium distribution of bank credit financing. On this basis, the regression model of bank credit financing and enterprise reputation is established, and it is combined with relevant data for empirical analysis. The results show that under the initial conditions, there are two evolutionary stable strategies of (0,0,0) and (1,1,1) in the enterprise bank credit financing game system, which verifies the analysis for the stability of equilibrium point under the dual mechanism of supply chain finance and industrial policy. The regression coefficient of bank credit financing of enterprises with greater corporate reputation is 1.26, and when the explanatory variable is the indicator of short-term lending and long-term investment, the coefficient of long-term borrowing ratio is -0.003, with a significance level of 1%, and the increase of long-term borrowing ratio can significantly reduce the behavior of short-term lending and long-term investment of enterprises.

Places left behind? Declining inner suburbs in the Toronto Census Metropolitan Area, 1980–2015

This paper empirically tests for causal relationships between occupational polarization, shifting housing market preferences, and inner-suburban decline in the City of Toronto prior to the Covid-19 pandemic. The analysis used variables from the 1981 and 2016 Censuses to conduct a cluster analysis that illustrates the potential relationships between occupational shifts, shifting housing market preferences, and inner-suburban decline. I then conducted an OLS regression analysis to determine causal relationships among the three processes. The results indicated that occupational polarization is the stronger determinant of inner-suburban decline in comparison to shifting housing market preferences. Furthermore, I determined that neighbourhoods with higher proportions of professionals are declining relative to neighbourhoods with higher proportions of workers in the finance, insurance, and real estate occupations. Socio-spatial restructuring and decline in the inner suburbs are increasingly characterized by an emergent form of occupational polarization that entails the bifurcation of occupations between those neighbourhoods that are more connected to the circulation and (re)production of glocal financial networks vs. those that are not.

Dynamics of Contagion Risk Among World Markets in Times of Crises: A Financial Network Perspective

Dynamics of Contagion Risk Among World Markets in Times of Crises: A Financial Network Perspective

Contingent convertible bonds in financial networks

We study the role of contingent convertible bonds (CoCos) in a complex network of interconnected banks. By studying the system’s phase transitions, we reveal that the structure of the interbank network is of fundamental importance for the effectiveness of CoCos as a financial stability enhancing mechanism. Our results show that, under some network structures, the presence of CoCos can increase (and not reduce) financial fragility, because of the occurring of unneeded triggers and consequential suboptimal conversions that damage CoCos investors. We also demonstrate that, in the presence of a moderate financial shock, lightly interconnected financial networks are more robust than highly interconnected networks. This makes them a potentially optimal choice for both CoCos issuers and buyers.

Heterogeneity and dynamics in network models

SummaryWe propose an empirical spatial modeling framework that allows for both heterogeneity and dynamics in economic network connections. We establish the model's stationarity and ergodicity properties and show that the model's implied filter is invertible. While highly flexible, the model is straightforward to estimate by maximum likelihood. We apply the model to three datasets for Eurozone sovereign credit risk over the period Dec‐2009 to Dec‐2022. Accounting for both heterogeneity and time‐variation turns out to be empirically important both in‐sample and out‐of‐sample. The new model uncovers intuitive patterns that would go unnoticed in either homogeneous and/or static spatial financial network models.

Strategy Analysis of Financial Neural Network Model in Bond Investment Prediction

In the diversified and complex environment of financial markets, effective investment strategy prediction has become particularly crucial. Especially in the bond market, due to its close connection with macroeconomic factors, the accuracy of predictions is of great significance for investment returns and risk management. This article delves into the application and strategic analysis of financial neural network models in bond investment prediction. We first reviewed the basic architecture of financial neural network models and emphasized their advantages in capturing nonlinear market dynamics. Then, we describe in detail how to use this model to predict the bond market, including the impact of interest rate changes, credit risk and macroeconomic factors. Furthermore, we propose a bond investment strategy framework based on neural network prediction. This framework considers various factors such as market liquidity, bond duration, and credit rating to optimize the returns and risks of investment portfolios. With the further development of deep learning and neural network technology in the financial field, its application potential in bond market prediction and strategy formulation will be more widely recognized and utilized.

Optimal primary dealer selection in financial networks

This paper establishes a financial network model with bank heterogeneity to analyze the optimal choice of primary dealers. We conclude that expansionary monetary policy should utilize small banks as primary dealers while contractionary monetary policy should utilize large banks as primary dealers.

A Stochastic Model for Cascading Failures in Financial Networks

We study cascading failures in time-varying and uncertain financial networks. First we develop a stochastic dynamic model for the mean and covariance matrix of the market value of each company in the network. Second, we provide a steady-state analysis, we characterize equilibrium points and we provide conditions on the asymptotic stability of such points. For the covariance matrix the dynamics have the form of a Lyapunov equation and simulating such dynamics can be viewed as a numerical method to compute the steady-state solution. As a general insight, the dynamics reveal the probability of failure of each company during the transient and at steady-state. Finally, we perform a robust analysis to obtain bounding sets for the mean market value in absence of information on the covariance.

The Influence of Fintech on Network Security and Regulatory Countermeasures

In recent years, with the continuous development and innovation of fintech, it also has a series of impacts on network security. This paper uses the literature review method to study the influence of fintech on financial industry network security and regulatory countermeasures. In the aspect of impact analysis, this paper first discusses the data security and algorithm risks brought by the extensive application of artificial intelligence. Secondly, the use of big data technology may lead to the risk of data leakage and abuse. Finally, lagging regulatory policies may increase security risks in the financial sector. In response to these impacts, this paper proposes four regulatory policies, such as strengthening data security and privacy protection, strengthening management efforts, balancing the relationship between innovation and risk, and introducing relevant policies. The study believes that effective regulatory measures can deal with the security challenges in the development of fintech, and ensure the stability of the financial system and the rights and interests of users.

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines. Anomaly detection, which aims to identify rare observations, is among the most vital tasks in the world, and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam. The detection task is typically solved by identifying outlying data points in the feature space and inherently overlooks the relational information in real-world data. Graphs have been prevalently used to represent the structural information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a database/set of graphs. However, conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data. For the advent of deep learning, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We compile open-sourced implementations, public datasets, and commonly-used evaluation metrics to provide affluent resources for future studies. More importantly, we highlight twelve extensive future research directions according to our survey results covering unsolved and emerging research problems and real-world applications. With this survey, our goal is to create a "one-stop-shop" that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.

Research on the 2008 Financial Crisis Regarding Financial Network

The rapid withdrawal in the financial market due to the collapse of Silicon Valley Banks in 2023 brought reflections on the 2008 financial crisis. Past research has focused on modeling different types of financial networks that help explain bankruptcys contagion process. This paper discusses how these networks help to explain the 2008 financial crisis, followed by shedding light on the global impacts (European, Latin American, and Asian markets) of the crisis in terms of networks. The last section further discusses potential policies that could be used to prevent or predict severe contagion depending on different network structures, including adjusting the level of portfolio diversification for international investment, for example. This paper sheds light on the importance of financial networks regarding their contagion channel and wide application in the real economy.