The Oxford Handbook of Quantitative Asset Management PDF Download

Author: Bernd Scherer
Publisher: Oxford University Press
ISBN: 0199553432
Category : Business & Economics
Languages : en
Pages : 530

View

Book Description
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.

Author: Kenneth Winston
Publisher: Cambridge University Press
ISBN: 1009209043
Category : Business & Economics
Languages : en
Pages : 647

View

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

Author: Kenneth J. Winston
Publisher: Cambridge University Press
ISBN: 1009209086
Category : Business & Economics
Languages : en
Pages : 647

View

Book Description
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.

Author: Özalp Özer
Publisher: Oxford University Press
ISBN: 0199543178
Category : Business & Economics
Languages : en
Pages : 977

View

Book Description
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.

Author: Ignazio Basile
Publisher: Springer
ISBN: 3319327968
Category : Business & Economics
Languages : en
Pages : 468

View

Book Description
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.

Author: Henry Schellhorn
Publisher: SIAM
ISBN: 1611977908
Category : Computers
Languages : en
Pages : 267

View

Book Description
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.

Author: Douglas Cumming
Publisher: Oxford University Press
ISBN: 0198754809
Category : Business & Economics
Languages : en
Pages : 737

View

Book Description
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.

Author: Michael Isichenko
Publisher: John Wiley & Sons
ISBN: 1119821320
Category : Business & Economics
Languages : en
Pages : 310

View

Book Description
Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.

Author: M. Rasmussen
Publisher: Springer
ISBN: 0230512852
Category : Business & Economics
Languages : en
Pages : 453

View

Book Description
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.

Author: Christian Dunis
Publisher: Springer Science & Business Media
ISBN: 1461543894
Category : Business & Economics
Languages : en
Pages : 345

View

Book Description
Advances in Quantitative Asset Management contains selected articles which, for the most part, were presented at the `Forecasting Financial Markets' Conference. `Forecasting Financial Markets' is an international conference on quantitative finance which is held in London in May every year. Since its inception in 1994, the conference has grown in scope and stature to become a key international meeting point for those interested in quantitative finance, with the participation of prestigious academic and research institutions from all over the world, including major central banks and quantitative fund managers. The editor has chosen to concentrate on advances in quantitative asset management and, accordingly, the papers in this book are organized around two major themes: advances in asset allocation and portfolio management, and modelling risk, return and correlation.