Complex Financial Networks Research Articles

Optimizing network performance in large financial enterprises using BGP and VRF-lite

This paper reviews network performance optimization in large financial enterprises using Border Gateway Protocol (BGP) and Virtual Routing and Forwarding Lite (VRF-Lite). It explores the theoretical framework of these technologies, highlighting their role in enhancing network efficiency, security, scalability, and cost-effectiveness. The challenges of complex financial networks, including scalability issues, security concerns, and regulatory compliance, are examined. The integration of BGP and VRF-Lite is presented as a robust solution for addressing these challenges. Implications for financial enterprises and future research directions are discussed, emphasizing the importance of adopting advanced network optimization strategies to maintain high performance and reliability in a rapidly evolving digital landscape.

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.

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

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

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.

Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks.

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.

Identification and Ranking of High-Risk Nodes in Complex Financial Networks

Identification and Ranking of High-Risk Nodes in Complex Financial Networks

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.

Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks

The methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly different dynamic behaviors. To observe the spatiotemporal patterns of risk propagation in complex financial networks, we combined a dynamic model with empirical networks. Our analysis revealed that hub nodes play a dominant role in risk propagation across the network and respond rapidly, thus exhibiting a degree-driven effect. The influence of key dynamic parameters, i.e., infection rate and recovery rate, was also investigated. Furthermore, the impacts of two typical characteristics of complex financial systems—the existence of community structures and frequent large fluctuations—on the spatiotemporal patterns of risk propagation were explored. About 30% of the total risk propagation flow of each community can be explained by the top 10% nodes. Thus, we can control the risk propagation flow of each community by controlling a few influential nodes in the community and, in turn, control the whole network. In extreme market states, hub nodes become more dominant, indicating better risk control.

Review: Maadi: The Making and Unmaking of a Cairo Suburb 1878–1962

Review: <i>Maadi: The Making and Unmaking of a Cairo Suburb 1878–1962</i>

Reconstructing a complex financial network using compressed sensing based on low-frequency time series data

Financial time series data are often used to construct financial complex networks for studying price volatility transmission, risk diffusion and asset portfolio and so on. High frequency Network can provide more effective information for exploring network structure and more accurate research on network evolution rules. The motivation of this paper is to construct high frequency networks using low frequency data when high frequency data is unavailable, with improvement of compressed sensing method. Results show that the network reconstructed by compressed sensing is closer to the high frequency network. In conclusion, compressed sensing can be applied to solve financial practice problem.

Contagion and tail risk in complex financial networks

New contagion measures based on theories of copula, heavy-tailed distributions and networks are introduced. The measures are applied to study international stock markets contagion during the Global Financial Crisis 2008. Having declined post-crisis, the contagion risk remains above its pre-crisis level for both advanced and emerging economies. A sub-network analysis of contagion shows that the shock propagated mainly from core to periphery during the crisis. We propose an instrumental variable regression approach to deal with a potential endogeneity problem in the analysis of the contagion measures as determinants of tail risk. Endogeneity might arise as both contagion measures and tail indices are themselves estimated. The obtained results are statistically significant and suggest that more contagion-central countries tend to be less prone to tail risk.

Law of interest rate changes in financial markets based on the differential equation model of liquidity

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Law of interest rate changes in financial markets based on the differential equation model of liquidity

Abstract The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Forecasting price movements of global financial indexes using complex quantitative financial networks

As predicting trends in the financial market becomes more important, and artificial intelligence technology advances, there is active research on predicting stock movements by analyzing historical prices. However, few attempts have been made to utilize complex financial networks to predict price movements. Most studies focus only on the target financial index, and only a few studies examine both the target financial index and influential financial indexes. To fill the research gap, we propose a novel deep learning algorithm based on quantitative complex financial networks to forecast the price movement of global financial indexes using technical analysis. In other words, we propose a method that analyses the causal relationship between financial indexes in a cleaned correlation network, which considers the causal impact in this quantitatively constructed network. We use the random matrix theory to construct the financial network first, and we use transfer entropy to find directional impact within the network. Based on a daily historical dataset for global indexes and out-of-sample tests, the results show that the proposed method outperforms past state-of-the-art algorithms. Our findings reveal that identifying and using proper financial networks are important in predicting problems. Our study suggests that it is important to develop deep learning algorithms and to consider the financial network based on complex system theory when solving prediction problems in the financial market.

Fire Sales, Default Cascades and Complex Financial Networks

We present a general tractable framework for understanding the joint impact of fire sales and default cascades on systemic risk in complex financial networks. Our limit theorems quantify how price mediated contagion across institutions with common asset holding could worsen cascades of insolvencies in a heterogeneous financial network, during a financial crisis. For given prices of illiquid assets, we show that, under some regularity assumptions, the default cascade model could be transferred to a death process problem represented by balls-and-bins model. We model the price impact by a given inverse demand function. We state various limit theorems regarding the total sold shares and the equilibrium price of illiquid assets in a stylized fire sales model. In particular, we show that the equilibrium prices of illiquid assets has asymptotically Gaussian fluctuations. Our numerical studies investigate the effect of heterogeneity in network structure and price impact function on the final size of default cascade and fire sales loss.

A study of systemic risk of global stock markets under COVID-19 based on complex financial networks

In this paper, Granger causalities of stock markets of 20 different countries are estimated from Aug 2019 to Mar 2020. Also, the complex network for global stock markets is established based on the data. Financial risks are identified by comparing the various characteristics of the topology of a complex financial network and the centrality of the stabilization and fluctuation periods. The results demonstrate that COVID-19 leads to close relation of financial connections between various countries, the impact spreads in a shorter distance, and the crisis transmission is faster. Overall, financial crises can be identified using network topological structure and centrality analysis based on network connectivity measurements. The Granger complex network can be employed for measuring and warning the systemic risk.

Sensitivity and Computational Complexity in Financial Networks

Determining the causes of instability and contagion in financial networks is necessary to inform policy and avoid future financial collapse. In the American Economic Review, Elliott, Golub and Jackson proposed a simple model for capturing the dynamics of complex financial networks. In Elliott, Golub and Jackson’s model, the institutions in the network are connected by linear dependencies (cross-holdings) and if any institution’s value drops below a critical threshold, its value suffers an additional failure cost. This work shows that even in this simple model there are fundamental barriers to understanding the risks that are inherent in a network. First, if institutions are not required to maintain a minimum amount of self-holdings, any change in investments by a single institution can have an arbitrarily magnified influence on the net worth of the institutions in the system. This implies that if institutions have small selfholdings, then estimating the market value of an institution requires almost perfect information about every cross-holding in the system. Second, even if a regulator has complete information about all cross-holdings in the system, it may be computationally intractable to estimate the number of failures that could be caused by a small shock to the system.

Transfer Entropy between Communities in Complex Financial Networks

In this paper, we analyze information flows between communities of financial markets, represented as complex networks. Each community, typically corresponding to a business sector, represents a significant part of the financial market and the detection of interactions between communities is crucial in the analysis of risk spreading in the financial markets. We show that the transfer entropy provides a coherent description of information flows in and between communities, also capturing non-linear interactions. Particularly, we focus on information transfer of rare events—typically large drops which can spread in the network. These events can be analyzed by Rényi transfer entropy, which enables to accentuate particular types of events. We analyze transfer entropies between communities of the five largest financial markets and compare the information flows with the correlation network of each market. From the transfer entropy picture, we can also identify the non-linear interactions, which are typical in the case of extreme events. The strongest flows can be typically observed between specific types of business sectors—financial sectors is the most significant example.

The Rise of Merchants and Traders in Eighteenth-century Rajasthan, India: An Exploratory Study

The collapse of the Mughal Empire in Rajasthan during the first half of the eighteenth century initiated important reconfigurations in its polity, society and economy. Emergence of regional political order and a new notion of commercialisation widened the sphere of engagements of merchants and traders. This article traces the structural changes that ensued, focusing specifically on (a) the emergence of the non-peasant sector in agriculture, (b) the rise of a cross-caste mercantile class and (c) change in commercial relationships under the new governance between the principalities, traders, artisans and the merchants. The research is based on insights from rich archival primary sources from the Rajasthan State Archives in Bikaner, focusing primarily on careful and extensive examination of the Bahis. With the objective of enriching the current understanding of how trade and commerce played a pivotal role in serving as engines of social change and economic growth, this study finds ample evidence of thriving trade, growing commercialisation and capitalist development, characterised by complex financial networks and an intricate system of credit in highly speculative commodity trade markets. Evident economic prosperity in most parts of Rajasthan helps add to the existing literature debunking the idea of eighteenth century being a ‘dark age’.