Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing
Author: Robbins, Michael
ISBN: 9781264258444
RM202.00
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Shelf: Professional Books / Finance & Investment / Investments (General)
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Big data combined with machine learning and artificial intelligence provides amazing opportunities for institutional investors—and this comprehensive resource walks you through the process of leveraging them for all they are worth.
In a straightforward and unambiguous style, Quantitative Asset Management shows 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 Management reveals the entire investing process, from designing goals to planning, research, implementation, testing, and risk management. Inside, you'll find:
Written 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. 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.
In a straightforward and unambiguous style, Quantitative Asset Management shows 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 Management reveals 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 institutions
- Real-world investment processes as employed by the largest investment companies
- A toolkit for investing as a professional
- Clear explanations of how to use modern quantitative methods to analyze investing options
- An 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 Management explains 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.
