E-Books →Quantitative Asset Management Factor Investing and Machine Learning for Institutional Investing

In a straightforward and unambiguous style,Quantitative Asset Managementshows how to join factor investing and data science―machine learning applied to investing. Using instructive anecdotes and practical examples, including a companion website with working code, this innovative guide provides a toolkit for applying these modern tools to investing and includes such real-world details as currency controls, market impact, and taxes.
Quantitative Asset Managementreveals the entire investing process, from designing goals to planning, research, implementation, 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 computer code and additional resources
Written by a seasoned financial investor who uses technology as a tool―as opposed to a technologist who invests―Quantitative Asset Managementexplains the author's methods without oversimplification or confounding theory and math. It 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 provides 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.