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Catalogue complet

Post H: Python Quant Edge

Python Quant Edge

Architecting, Backtesting, and Deploying High-Performance Automated Trading Systems

The Wiley Finance Series

A Practical Guide with Examples Worked in Python and R

Machine Learning and Artificial Intelligence for Credit Risk Analytics

So you only have $100 to...Invest

The OUSD Revolution

How Stablecoins Will Change Global Finance

This popular textbook offers a broad and accessible introduction to the building blocks of modern finance: financial markets, institutions and instruments. Theory is blended with real-world data, cases and numerical worked examples to offer a broad and accessible introduction to the building blocks of modern finance in the 5th edition of Finance and Financial Markets. The book features assessment and self-test resources to help support students and lecturers, as well as extensive discussion of regulatory developments, case studies involving FinTech and Brexit and fully updated data. An updated companion website is also available which offers lecturer slides and multiple choice questions. This new edition has updated data and cases throughout, ensuring that it is as up-to-date as possible in this fast-moving area. New material has been introduced relating to environmental, social and governance (ESG) factors, emerging markets and sustainable finance. The opening five chapters provide an introduction to the world of finance and cover the basic theory relating to the operation of financial institutions and the domestic and offshore (Eurocurrency) money markets. Chapters 6 to 10 deal with both the theory and practice relating to capital markets. The theory covers areas such as portfolio diversification, the capital asset pricing model, the Gordon growth model and the efficient market hypothesis, while the practice looks at the domestic and international bond markets and equity markets. After an examination of the foreign exchange market and forward markets, the final chapters of this comprehensive textbook cover derivative instruments, financial innovations and, as a closing reflection, financial regulation. Finance and Financial Markets is ideally suited to students at all levels who take economics, business and finance courses, as well as for those who want to understand the workings of the modern financial world.

Finance and Financial Markets

ELEMENTS OF STOCHASTIC FINANCE

Navigating Growth, Regulation, Innovation, and Competitive Advantage

Strategic Risk and Opportunity in Mid-Tier Banks

So you need a...Will

Wiley Finance

The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you'll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You'll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly. This book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You'll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts. <ul> <li>Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques</li> <li>Offers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning</li> <li>Demonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website</li> <li>Introduces the most important software frameworks for applying deep learning within finance</li></ul> This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.

Deep Learning in Quantitative Finance

A Practical Guide to Stochastic Calculus and Quantitative Modeling

Derivative Pricing

Preya Bhattacharya offers the first full-length assessment of the impact of microfinance on post-conflict women's empowerment in the Balkans. In so doing, she provides vital new insights into what women's microfinance contributes to macroeconomic development in the former Yugoslavian countries of Macedonia, Croatia, Slovenia, Montenegro, Serbia, Bosnia-Herzegovina, and Kosovo. At the same time, she develops a uniquely clear, concise approach to Qualitative Comparative Analysis (QCA), which she uses to make sense of an extensive, exhaustive raw set of available data and research. This methodology not only offers a way of using both micro and macroeconomic data to revisit Amartya Sen's theory of development as freedom, but also provides a framework for other researchers to assess the impacts of microfinance on women's development throughout the world, and particularly in the Global South. For its original focus, its clear and streamlined methodological approach, and its global implications, this book provides a go-to source for researchers, students, and practitioners of international development, gender studies, and economics and finance.

A Qualitative Comparative Analysis

Microfinance and Post-conflict Women's Empowerment in the Balkans

The True Story No Crypto Guru Will Ever Tell You

Burned by the Market

Structural Causal Economics with Python

Reactive PublishingEconomic systems are shaped by complex cause-and-effect relationships that traditional correlation-based models often fail to capture. Structural Causal Economics with Python introduces a rigorous, modern framework for understanding economic mechanisms using structural causal models, graph-based reasoning, and counterfactual simulation.This book bridges the gap between academic causal inference theory and real-world economic modeling. Readers learn how to move beyond prediction into explanation, policy testing, and scenario design using practical Python implementations. The focus is on building models that reflect how economic systems actually behave under intervention, shock, and policy change.Inside, you will learn how to: - Build and interpret causal graphs for economic systems - Design structural models that capture real economic mechanisms - Perform counterfactual analysis to evaluate alternative policy scenarios - Simulate policy interventions and measure downstream effects - Implement causal modeling workflows using modern Python tools - Connect causal inference methods to macroeconomic and microeconomic applicationsRather than treating economic data as purely statistical signals, this book teaches you how to model the underlying structure that generates economic outcomes. The result is more robust forecasting, clearer policy insight, and deeper strategic understanding.Written for quantitative economists, policy analysts, data scientists, and advanced finance professionals, this book assumes familiarity with Python and core economic concepts. It is designed as both a practical implementation guide and a conceptual reference for structural causal economic modeling.If you want economics models that explain why outcomes happen, not just what happens next, this book provides the tools and framework to build them.

Models for Economic Policy

Python Quant Edge

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