The Oxford Handbook Of Quantitative Asset Management

This book explores the current state of the art in quantitative investment management across seven key areas. Chapters by academics and practitioners working in leading investment management organizations bring together major theoretical and practical aspects of the field.

A book combining the rigour of academic finance with the pragmatism of hands-on finance.

A modern introduction to risk and portfolio management for advanced undergraduate and beginning graduate students who will become practitioners in the field of quantitative finance, including extensive live data and Python code as online supplements which allow the application of theory to real-world situations.

A definitive reference to the theory and practice of pricing across industries, environments, and methodologies. It covers all major areas of pricing including, pricing fundamentals, pricing tactics, and pricing management.

This book analyses investment management policies for institutional investors. It is composed of four parts. The first one analyses the various types of institutional investors, institutions which, with different objectives, professionally manage portfolios of financial and real assets on behalf of a wide variety of individuals. This part goes on with an in-depth analysis of the economic, technical and regulatory characteristics of the different types of investment funds and of other types of asset management products, which have a high rate of substitutability with investment funds and represent their natural competitors. The second part of the book identifies and investigates the stages of the investment portfolio management. Given the importance of strategic asset allocation in explaining the ex post performance of any type of investment portfolio, this part provides an in-depth analysis of asset allocation methods, illustrating the different theoretical and operational solutions available to institutional investors. The third part describes performance assessment, its breakdown and risk control, with an in-depth examination of performance evaluation techniques, returns-based style analysis approaches, and performance attribution models. Finally, the fourth part deals with the subject of diversification into alternative asset classes, identifying the common characteristics and their possible role within the framework of investment management policies. This part analyses hedge funds, private equity, real estate, commodities, and currency overlay techniques.

This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.

Sovereign Wealth Funds have become increasingly powerful and influential investors. Their increasing role, and unusual character as both political and market actors, raise a number of issues with regard to finance, politics, regulation, and international business. This handbook draws together the growing but fragmented research on SWFs.

Targeted towards institutional asset managers in general and chief investment officers, portfolio managers and risk managers in particular, this practical book serves as a comprehensive guide to quantitative portfolio optimization, asset allocation and risk management. Providing an accessible yet rigorous approach to investment management, it gradually introduces ever more advanced quantitative tools for these areas. Using extensive examples, this book guides the reader from basic return and risk analysis, all the way through to portfolio optimization and risk characterization, and finally on to fully fledged quantitative asset allocation and risk management. It employs such tools as enhanced modern portfolio theory using Monte Carlo simulation and advanced return distribution analysis, analysis of marginal contributions to absolute and active portfolio risk, Value-at-Risk and Extreme Value Theory. All this is performed within the same conceptual, theoretical and empirical framework, providing a self-contained, comprehensive reading experience with a strongly practical aim.