Quantitative Asset Management : Factor Investing and Machine Learning for Institutional Investing

Whether you are managing institutional portfolios or private wealth, augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growth

In a straightforward and unambiguous fashion, Quantitative Asset Management shows how to take join factor investing and data sciencemachine learning and applied to big data. Using instructive anecdotes and practical examples, including quiz questions and a companion website with working code, this groundbreaking guide provides a toolkit to apply these modern tools to investing and includes such real-world details as currency controls, market impact, and taxes. It walks readers through the entire investing process, from designing goals to planning, research, implementation, and testing, and risk management.

Written by a seasoned financial investor who uses technology as a toolas opposed to a technologist who investsQuantitative Asset Management explains the authors methods without oversimplification or confounding theory and math. Quantitative Asset Management demonstrates how leading institutions use Python and MATLAB to build alpha and risk engines, including optimal multi-factor models, contextual nonlinear models, multi-period portfolio implementation, and much more to manage multibillion-dollar portfolios.

Big data combined with machine learning provide amazing opportunities for institutional investors. This unmatched resource will get you up and running with a powerful new asset allocation strategy that benefits your clients, your organization, and your career.