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 growthIn a straightforward and unambiguous fashion, Quantitative Asset Management shows how to take join factor investing and data science-machine 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. Inside, you'll find:Cutting edge methods married to the actual strategies used by the most sophisticated institutionsReal-world investment processes as employed by the largest investment companiesA toolkit for investing as a professionalClear explanations of how to use modern quantitative methods to analyze investing optionsAn accompanying online site with coding and appsWritten by a seasoned financial investor who uses technology as a tool-as opposed to a technologist who invests-Quantitative Asset Management explains the author's 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.