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 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 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.
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.
This handbook draws on research from a range of academic disciplines to reflect on the implications for provisions of pension and retirement income of demographic ageing. it reviews the latest research, policy related tools, analytical methods and techniques and major theoretical frameworks.
The financial crisis that began in 2008 and its lingering aftermath have caused many intellectuals and politicians to question the virtues of capitalist systems. The 19 original essays in this handbook, written by leading scholars from Asia, North America, and Europe, analyze both the strengths and weaknesses of capitalist systems. The volume opens with essays on the historical and legal origins of capitalism. These are followed by chapters describing the nature, institutions, and advantages of capitalism: entrepreneurship, innovation, property rights, contracts, capital markets, and the modern corporation. The next set of chapters discusses the problems that can arise in capitalist systems including monopoly, principal agent problems, financial bubbles, excessive managerial compensation, and empire building through wealth-destroying mergers. Two subsequent essays examine in detail the properties of the "Asian model" of capitalism as exemplified by Japan and South Korea, and capitalist systems where ownership and control are largely separated as in the United States and United Kingdom. The handbook concludes with an essay on capitalism in the 21st century by Nobel Prize winner Edmund Phelps.