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

Whether you’re managing institutional portfolios or private wealth, Quantitative Asset Management will open your eyes to a new, more successful way of investing—one that harnesses the power of big data and artificial intelligence.

This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools. Written from the perspective of a seasoned financial investor making use of technology, it details proven investing methods, striking a rare balance between providing important technical information without burdening you with overly complex investing theory. Quantitative Asset Management is organized into four thematic sections:

• Part I reveals invaluable lessons for planning and governance of investment decision-making.
• Part 2 discusses quantitative financial modeling, covering important topics like overfitting, mitigating unrealistic assumptions, managing substitutions, enhancing minority classes, and missing data imputation.
• Part 3 shows how to develop a strategy into an investment product, including the alpha models, risk models, implementation, backtesting, and cost optimization.
• Part 4 explains how to measure performance, learn from mistakes, manage risk, and survive financial tragedies.

MATLAB is introduced and used to solve numerous examples in the book. The Financial Toolbox and Statistics and Machine Learning Toolbox are also introduced. In addition, a supplemental set of MATLAB code files is available for download.

With Quantitative Asset Management, you have everything you need to build your awareness of other markets, ask the right questions and answer them effectively, and drive steady profits even through times of great uncertainty.