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Publishing R: Strategic Irrationality

Strategic Irrationality

Game Theory and Behavioral Finance for Quantitative Traders

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

The Surprising Truth About How Money Works

Money: The Inside Story

Neue ökonomische Grundrisse

The second edition of this instructive and thought-provoking guide to the theory and practice of corporate financial decision making has been completely revised and up-dated. In particular, the many and varied changes to financial and capital markets have been worked into the application-orientated chapters together with additional diagrams and tables. The book's chapters on theoretical models have been enhanced with further examples, each ending with a practical exercise the solution to which will be made available online.
 

 Different conditions apply to libraries for this title; please contact the sales department.

Unternehmensfinanzierung und Kapitalmarkt

So you need a...Will

The book Finance in Non-Financial Problems applies the concept of finance to non-financial problems including politics, the market for ideas, social networks, free trade, and the law. The principal objective is to use techniques such as portfolio theory, diversification, options, and market efficiency in areas where they have not previously been used. Finance in Non-Financial Problems is then an interdisciplinary resource applying the techniques of uncertainty to new disciplines, providing a new way to look at old problems, presenting problems in the social sciences with a new perspective, and allowing readers to consider political, legal, and social issues in a new way.

Finance In Non-financial Problems

NLP, Sentiment, and Beyond

Financial Econometrics with Alternative Data

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.

Strategic Irrationality

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