Financial institutions and markets are highly interconnected, but only recently has literature begun to emerge that maps these interconnections and assesses their impact on financial risks and returns. The Journal of Network Theory in Finance is an interdisciplinary journal publishing academically rigorous and practitioner-focused research on the application of network theory in finance and related fields. The journal brings together research carried out in disparate areas within academia and other research institutions by policymakers and industry practitioners.
The Journal of Network Theory in Finance publishes data-driven or theoretical work in areas including, but not limited to:
- Empirical network analysis that enables better understanding of financial flows, trade flows, input-output tables, financial exposures or market interdependencies
- Modeling and simulation techniques for measuring interdependent financial risks
- New metrics and techniques for identifying central, vulnerable or systemically important institutions and markets in financial networks
- Network modeling of time-series data for financial risk management, asset allocation and portfolio management
- Social network analysis (SNA) in finance, such as using social network data for making credit and investment decisions
- Applied network visualization techniques that improve the communication of financial risks and rewards
- Analysis of counterparties and their risk exposure from interconnectivity with the financial system and regulatory strategies for improving financial stability
Abstracting and indexing: Clarivate Analytics Emerging Sources Citation Index; EconLit; EconBiz; and Cabell’s Directory
Structural changes in the interbank market across the financial crisis from multiple core–periphery analysis
In this work, the authors employ the KM–ER algorithm to characterize the internal organization of eMID.
This paper quantifies the interrelations induced among financial institutions by common asset holdings.
This paper surveys the use of networks and network-based methods to study economy- related questions.
This paper contributes to the financial networks literature by providing evidence that well-connected bankers on the boards of directors of nonfinancial firms reduce information asymmetry between credit markets and firms.
This paper defines an algorithm for measuring sentiment-based network risk, to understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies.
In this paper, the authors study the topological and structural properties of the bank–sector credit network of Spain over the period 1997–2007.
This paper presents an evaluation of how risk interdependence affects the risk management process.
This paper extensively compares mutual-information-based networks with correlation-based networks on a stand-alone basis and in the framework of active investment strategies.
This paper proposes a framework to identify the structure of a financial network and its evolution over time, and presents an application to an interbank market with complete actual data.
In this paper, the author builds dynamic networks based on correlation and transfer entropy, using both the log returns and the volatilities of 97 stock market indexes from various parts of the world between 2000 and 2016
In this paper the authors study insolvency cascades in an interbank system, in which banks are permitted to insure their loans with credit default swaps sold by other banks.
The authors present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the worldwide financial network.
In this paper, the author provides an empirical analysis of the network characteristics of two interrelated interbank money markets and their effect on overall market conditions.
Through financial network analysis, this paper ascertains the existence of important causal behavior between certain financial assets, as inferred from eight different causality methods.
In this paper, the authors use information theory quantifiers to analyze the graphs generated by the VG method as applied to the return rate time series of stock markets from different countries.
In this paper, the authors use a topic-modeling approach to quantify the changing attentions of a major news outlet, the Financial Times, to issues of interest.
This paper deals with statistical measures based on high frequency data from stock markets, and in particular looks at how these measures changed according to time, with a focus on before and after the crisis of 2008.