Asset management: tools and issues

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

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.