Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage

Quantitative trading of financial securities is a multi-billion dollar business employing thousands of portfolio managers and quantitative analysts (quants) trained in mathematics, physics, or other hard sciences. The quants trade stocks and other instruments creating liquidity for investors and competing, as best they can, at finding and exploiting any mispricings. The result is highly efficient financial markets not immune to occasional events of crowding, bubbling, and liquidation panic. This book covers all the major parts of the quantitative trading process starting with sourcing financial data, learning future asset returns from historical data, generating and combining multiple forecasts, dealing with risk, building optimal portfolio of stocks subject to risk preferences and trading costs, and executing trades. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual events and thoughts from the trenches, as observed by a quantitative portfolio manager, and even actual questions asked at countless quant interviews. The intended audience includes practicing quants who will encounter things both familiar and novel (such lesser known ML algorithms or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), and the general public interested in quantitative and algorithmic trading from a broad scientific, and occasionally ironic, standpoint.