In search of lost edges: a case study on reconstructing financial networks

We apply a novel conceptual framework that integrates the global production network (GPN) and global financial network (GFN) approaches combined with the notions of transaction costs, risk, and uncertainty to shed new light on commodities markets, using wheat futures markets as a case study. We critically assess three major trends characterizing the evolution of wheat futures trading since 2000: horizontal and vertical integration, the establishment of a new EU price benchmark, and heightened volatility. The focus of this article is the infrastructural backbone of the market, which includes trading houses, warehouses, derivatives exchanges, central counterparties, and investment banks. Conceptually, it is argued that the wheat global production and financial network (GPFN) can be conceived as a means of taming transaction costs, risk, and uncertainty. These concepts are crucial for understanding the network’s configuration and evolution, explaining increasing concentration in a limited number of key nodes and the existence of interlinkages not only within but also across the wheat GPN and GFN. Empirically, it is shown that Paris has become a key financial center for trading wheat futures flanking (if not supplanting) Chicago—a notable development given the typically slow pace of change in GPFNs.

This paper presents a new framework for player valuation in European football, by fusing principles from financial mathematics and network theory. The valuation model leverages a “passing matrix” to encapsulate player interactions on the field, utilizing centrality measures to quantify individual influence. Unlike traditional approaches, such as regressing on past performance-salary data, this model focuses on in-game performance as a player’s contributions evolve over time. Consequently, our model provides a dynamic and individualized framework for ascertaining a player’s fair market value. The methodology is empirically validated through a case study in European football, employing real-world match and financial data. This cross-disciplinary mechanism for player valuation adapts the effect of connecting pay with performance, first seen in Scully ((1974). Pay and performance in major league baseball. Am. Econ. Rev. 64: 915–930), to include in-game contributions as well as expected present valuation of stochastic variables.

With the availability of the Internet, financial social networks such as StockTwits and SeekingAlpha are emerging to provide opportunities for investors around the world to gather and share their experiences and opinions on the stock market. When these social networks become more and more popular, millions of users join and post huge amount of posts every single day to discuss all kinds of topics related to the market, e.g., the news and events about companies, when to buy and sell shares, which stocks to buy. If a user has a question related his/her investment, he/she needs to browse all the posts returned by searching the stock symbols, however due to the high volume of the posts he/she will drown in the information and hardly to find the answers he/she wants to know. In this paper, we propose to apply a language model with dynamic pseudo relevance feedback to obtain relevant posts in StockTwits, so that people can quickly and easily grasp the ongoing events of their interested financial news. Experiments and a case study on the StockTwits dataset demostrate the effectiveness of the dynamic pseudo relevance feedback.

The years 1875–1889 saw changes caused by improved transportation, communication and the long-term declining trend in the value of silver that led to the rise of the Hongkong and Shanghai Banking Corporation (HSBC). The HSBC’s success could be attributed mainly to its adoption of the “on an even keel” policy. The London Office of the bank played an essential role in implementing this policy. In this chapter, I used chiefly the correspondence of David McLean, the manager of the London Office from 1875 to 1889, in examining the actualities of business activities in the London Office of HSBC. This chapter divides into four sections. Section 1 analyzes the finance trade business. Section 2 examines the relationship of HSBC and its correspondent banks. Section 3 considers the deposit-taking activities while Sect. 4 deals with cooperative relationship between British international banks. This research revealed that HSBC strengthened its financial networks in London in responding to changes in international payment system as well as the growth of its trade finance business. Moreover, it showed that the dynamics of competition and cooperation between international banks were significant in shaping the international payment system between Asia and the West and vice versa. By the middle of the 1880s, HSBC proved an unwavering position in international capital movement between East Asia and Britain.

Survivability is the ability of a system to continue operating despite the presence of abnormal events such as accidental failures and malicious intrusions. Ensuring system survivability has increased in importance as critical infrastructures have become heavily dependent on computers. Examples of these infrastructures are utility, transportation, communication, and financial networks. Complicating the analysis of these networked systems is their inter-dependencies: a failure in one may trigger a failure in another. In this talk I present a two-phase method for performing survivability analysis of networked systems. First, we inject failure and intrusion events into a system model, use model checking to verify it for fault- and service-related properties, and visually display the model’s effects with respect to a given property as a scenario graph. Then, we annotate the graphs with symbolic or numeric probabilities to enable reliability analysis using standard Markov Decision Process policy iteration algorithms. We use similar modeling and analysis techniques to do latency and cost-benefit analyses of these networked systems. We model dependencies among events using Bayesian Networks. We applied our two-phase method to two large cases studies from the financial industry and are currently applying it to a case study on intrusion detection systems.

Ecological considerations in constructing marine infrastructure: The Falmouth cruise terminal development, Jamaica

We develop a topology data analysis-based method to detect early signs for critical transitions in financial data. From the time-series of multiple stock prices, we build time-dependent correlation networks, which exhibit topological structures. We compute the persistent homology associated to these structures in order to track the changes in topology when approaching a critical transition. As a case study, we investigate a portfolio of stocks during a period prior to the US financial crisis of 2007–2008, and show the presence of early signs of the critical transition.

This dissertation examines the economic opportunities that free women of color could derive from slaveholding, their motivations, and their impact on New Orleans’ antebellum society and economy. Another aim is to find out the role and impact of free women of color from Saint Domingue (later Haiti), whose arrival in New Orleans doubled the number of free women of color in the city. Finally, the analysis of relationships between free women of color and their slaves and with the diverse population of New Orleans plays an important part in this study. Notarial deeds (sales and purchases of slaves, mortgages of slaves, property inventories, powers of attorney, and wills), court records (lawsuits, Supreme Court records, and criminal records), and other public records (federal, state, county and city document, city directories, census data, and church sacramental registers) provide invaluable sources for this study. I use two major research strategies: (1) a statistical analysis of slave ownership among free women of color and (2) case studies. Such methodology allows me to consider slave ownership among these women in an exhaustive manner, including important parameters such as gender, race, and ethnicity. For free women of color, slaves were definitely a source of personal and commercial speculation, which was inherent in the relationship between master and slave. Free women of color did not and could not deny their slaves’ humanity, yet this knowledge, which gleams through the records on certain occasions, did not inhibit them from engaging in the exploitation and trading of slaves of all ages, which, in turn allowed them to acquire significant amounts of property. The data suggests that these aspirations were shared among the large community of free women of color in the urban center of New Orleans. There, they found a sense of community, tied together by a shared heritage, friendship, kinship, religion, education, and above all economic opportunities, creating thriving social and financial networks among themselves and with others throughout the city.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

Survivability is the ability of a system to continue operating despite the presence of abnormal events such as accidental failures and malicious intrusions. Ensuring system survivability has increased in importance as critical infrastructures have become heavily dependent on computers. Examples of these infrastructures are utility, transportation, communication, and financial networks. Complicating the analysis of these networked systems is their inter-dependencies: a failure in one may trigger a failure in another. In this talk I present a two-phase method for performing survivability analysis of networked systems. First, we inject failure and intrusion events into a system model, use model checking to verify it for fault- and service-related properties, and visually display the model’s effects with respect to a given property as a scenario graph. Then, we annotate the graphs with symbolic or numeric probabilities to enable reliability analysis using standard Markov Decision Process policy iteration algorithms. We use similar modeling and analysis techniques to do latency and cost-benefit analyses of these networked systems. We model dependencies among events using Bayesian Networks. We applied our two-phase method to two large cases studies from the financial industry and are currently applying it to a case study on intrusion detection systems.

Women, Literature, and Finance in Victorian Britain: Cultures of Investment

The rapid growth of artificial intelligence (AI) in decentralized systems such as healthcare, financial networks, and autonomous transportation has underscored the critical need for interpretability, fairness, and verifiable trust in decision-making. Traditional federated learning frameworks, while addressing data privacy and scalability, often suffer from bias propagation, opaque model behaviors, and limited mechanisms for ensuring accountability. This article introduces Chain-of-Trust AI, a novel paradigm that integrates zero-knowledge proofs (ZKPs), federated reinforcement learning (FRL), and generative learning models to create an interpretable, bias-free, and verifiable decision-making framework for complex distributed environments. The proposed framework leverages FRL to enable adaptive coordination across heterogeneous agents while maintaining local data sovereignty. Generative learning models, such as variational autoencoders, provide transparent causal representations that support bias detection and enhance interpretability of reinforcement-driven policies. ZKPs are embedded as cryptographic guarantees to verify model updates and decision outcomes without exposing sensitive information, thus ensuring compliance, trust, and transparency across decentralized networks. Methodologically, the framework is evaluated through MATLAB-based multi-agent simulations, benchmarking performance in terms of interpretability, fairness indices, convergence stability, and verification overhead. Theoretical analyses confirm convergence under heterogeneous reward structures, cryptographic soundness of proofs, and bias reduction capabilities through generative regularization. Case studies in decentralized healthcare diagnostics, financial fraud detection, and autonomous vehicular coordination highlight the practical scalability and robustness of Chain-of-Trust AI. By uniting reinforcement learning, generative interpretability, and zero-knowledge verification, this work pioneers a secure, auditable, and ethically aligned AI architecture for decentralized complex systems, advancing both technical rigor and governance in distributed intelligence.

Financial networks based on Granger causality: A case study

This chapter forms the backbone of our case studies in Chapters 5 and 6. Here, we will describe how exchange-traded funds and notes are constructed. Significantly, dealer rebalancing for these products can increase risk within the financial network. When an ETF or ETN stops functioning relative to the benchmark, dealers in the product can turn into Dominant Agents, distorting the distribution of returns. We will also investigate levered products, showing that outcomes can be disappointing in choppy markets.

Financial instability often results from positive feedback loops intrinsic to the operation of the financial system. The challenging task of identifying, modeling, and analyzing the causes and effects of such feedback loops requires a proper systems engineering perspective lacking in the remedies proposed in recent literature. We propose that signed directed graphs (SDG), a modeling methodology extensively used in process systems engineering, is a useful framework to address this challenge. The SDG framework is able to represent and reveal information missed by more traditional network models of financial system. This framework adds crucial information to a network model about the direction of influence and control between nodes, providing a tool for analyzing the potential hazards and instabilities in the system. This paper also discusses how the SDG framework can facilitate the automation of the identification and monitoring of potential vulnerabilities, illustrated with an example of a bank/dealer case study.