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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