Graph Theory & Network Analysis in Quantitative Finance A Practical Guide to Systemic Risk, Market Structures, and

Financial markets are complex networks of assets, institutions, and investors, where hidden connections drive risk, volatility, and market behavior. Graph theory and network analysis provide cutting-edge techniques to analyze these relationships, uncover market inefficiencies, and enhance trading strategies.
This comprehensive guide bridges the gap between mathematics, data science, and finance, equipping you with practical tools to model systemic risk, market topology, and capital flows using graph-based techniques.
What You'll Learn:
Graph Theory Fundamentals for Finance - Nodes, edges, adjacency matrices, and shortest path algorithms
Market Structure & Network Dynamics - Analyzing trade networks, liquidity flows, and asset correlations
Systemic Risk & Financial Contagion - Detecting too-big-to-fail institutions and contagion pathways in crises
Centrality Measures & Market Influence - Using degree, betweenness, and eigenvector centrality for market impact analysis
Algorithmic Trading & Graph-Based Strategies - Applying network models to predict price movements & uncover arbitrage opportunities
Machine Learning & Network Science in Finance - Leveraging graph neural networks (GNNs) for risk modeling and fraud detection
Python Implementation & Case Studies - Hands-on examples with NetworkX, Pandas, and PyGraph
Who This Book is For:
Traders & Hedge Fund Analysts - Gain an edge by analyzing market structures and capital flows
Risk Managers & Financial Regulators - Detect financial contagion and assess systemic risk
Quantitative Finance & Data Science Students - Master graph-based modeling for real-world financial applications
With clear explanations, real-world case studies, and Python implementations, this book transforms graph theory and network analysis into powerful tools for financial decision-making.

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