Long gone are the times when investors could make decisions based on intuition. Modern asset management draws on a wide-range of fields beyond financial theory: economics, financial accounting, econometrics/statistics, management science, operations research (optimization and Monte Carlo simulation), and more recently, data science (Big Data, machine learning, and artificial intelligence). The challenge in writing an institutional asset management book is that when tools from these different fields are applied in an investment strategy or an analytical framework for valuing securities, it is assumed that the reader is familiar with the fundamentals of these fields. Attempting to explain strategies and analytical concepts while also providing a primer on the tools from other fields is not the most effective way of describing the asset management process. Moreover, while an increasing number of investment models have been proposed in the asset management literature, there are challenges and issues in implementing these models. This book provides a description of the tools used in asset management as well as a more in-depth explanation of specialized topics and issues covered in the companion book, Fundamentals of Institutional Asset Management. The topics covered include the asset management business and its challenges, the basics of financial accounting, securitization technology, analytical tools (financial econometrics, Monte Carlo simulation, optimization models, and machine learning), alternative risk measures for asset allocation, securities finance, implementing quantitative research, quantitative equity strategies, transaction costs, multifactor models applied to equity and bond portfolio management, and backtesting methodologies. This pedagogic approach exposes the reader to the set of interdisciplinary tools that modern asset managers require in order to extract profits from data and processes.
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Sample Chapter(s)
Preface
Chapter 1: Asset Management Companies
Contents:
- Asset Management Companies
- Fundamentals of Financial Statements
- Securitization and the Creation of Residential Mortgage-Related Securities
- Financial Econometrics Tools for Asset Management
- Monte Carlo Applications to Asset Management
- Optimization Models for Asset Management
- Machine Learning and Its Applications to Asset Management
- Risk Measures and Asset Allocation Problems
- Securities Lending and Its Alternatives in the Equity Market
- Repurchase Agreements for Financing Positions and Shorting in the Bond Market
- Implementable Quantitative Research
- Quantitative Equity Strategies
- Challenges in Implementing Equity Factor Investing Strategies
- Transaction and Trading Costs
- Managing a Common Stock Portfolio with a Multifactor Risk Model Using Fundamental Factor
- Managing a Bond Portfolio Using a Multifactor Risk Model
- Backtesting Investment Strategies
- Monte Carlo Backtesting Method
Readership: Suitable for professional portfolio managers, security analysts as well as students focusing on investment management courses.
Frank J Fabozzi is an editor of The Journal of Portfolio Management and co-editor/co-founder of The Journal of Financial Data Science. He is Professor of Finance at EDHEC Business School. Over the past 35 years, he has held professorial positions at MIT, Yale, Princeton, and New York University. He is a trustee of the BlackRock closed-end fund complex. He has authored more than 120 books and more than 200 articles in peer-reviewed journals. He is the CFA Institute's 2007 recipient of the C Stewart Sheppard Award and the CFA Institute's 2015 recipient of the James R Vertin Award. He was inducted into the Fixed Income Analysts Society Hall of Fame in November 2002. He earned his designations of Chartered Financial Analyst (CFA) and Certified Public Accountant (CPA). He received his BA and MA in economics in 1970 from The City College of New York where he was elected to Phi Beta Kappa, and acquired his PhD in economics in 1972 from the City University of New York. In 1994 he was awarded an Honorary Doctorate of Humane Letters from Nova Southwestern University.
Francesco A Fabozzi is a doctoral student in data science at the Stevens Institute of Technology. He is the managing editor of The Journal of Financial Data Science. He has worked as a research associate at NYU's Courant Institute in the Department of Mathematical Finance. He is on the Curriculum Board of the Financial Data Professionals Institute (FDP Institute). He interned at AQR in the firm's machine learning group. Francesco assisted in the authoring of Global Financial Markets. He earned a BA in economics in 2018 from Princeton University and an MS in financial analytics in 2019 from the Stevens Institute of Technology.
Marcos López de Prado is Professor of Practice at Cornell University and CIO at True Positive Technologies. Over the past 20 years, Marcos has pursued three simultaneous careers, as an investment manager, a professor, and a researcher at a leading US National laboratory. This combination of backgrounds has allowed him to manage multibillion-dollar funds, produce numerous innovations that have resulted in 13 patents (11 of which were sold to various firms), publish three textbooks used in leading university programs, and a large number of peer-reviewed publications in academic journals. Marcos has helped modernize finance, by popularizing the use of machine learning and supercomputing, and by developing statistical tests that identify false investment strategies (false positives). Marcos is a co-founding editor of The Journal of Financial Data Science. Since 2017, Marcos has ranked as the most-read author in economics (SSRN). In 2019, Marcos was named the "Quant of the Year" by The Journal of Portfolio Management, and the US Congress invited him to offer testimony on AI policy, and the impact of automation in the financial sector.
Stoyan V Stoyanov is the Equity Research Director at a prominent financial services firm. Prior to this role, he was a research professor of finance in the Center for Finance and the College of Business at Stony Brook University. He has held professorial positions at EDHEC Business School and EDHEC Risk Institute–Asia. He has published over 40 articles in leading academic and practitioner-oriented scientific journals, contributed to many professional handbooks, and co-authored several books on probability and stochastics, financial risk assessment and portfolio optimization. He earned his BA in economics and MS in applied probability and statistics from Sofia University in 2002 and a PhD in mathematical finance in 2005 from Karlsruhe Institute of Technology.
