Quantitative trading has become a multi-billion-dollar industry employing thousands of portfolio managers and quantitative analysts (quants) trained in mathematics, physics, and other “hard” sciences. Quants trade securities by quickly finding and exploiting mispricing in the market, creating liquidity, and maintaining the efficiency of financial markets.

In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, theoretical physicist and accomplished quantitative portfolio manager Dr. Michael Isichenko delivers a systematic review of the quant equity trading process, also known as statistical arbitrage.

Covering every major component of the quantitative trading process, the author discusses how to source financial data, learn future asset returns from historical data, generate and combine multiple forecasts, manage risk, build optimal portfolios mindful of risk preferences and trading costs, and execute trades

The book balances practical financial insights with mathematical ideas of statistical and machine learning, computational strategies, and examples gleaned from the author’s years of experience as a quant portfolio manager. You’ll also find a collection of insightful and perplexing questions asked at quant interviews.

Quantitative Portfolio Management includes discussions of complex topics that remain the subject of active research, like double descent of generalization error in regression and deep learning, forecast combination and its diversification limits, and market-wide elasticity.

Throughout, the book focuses on the application of machine learning and forecasting techniques to real-world portfolio optimization problems. It offers special closed-form solutions with impact and slippage costs and approximations for efficient algorithmic approaches.

Perfect for investment professionals, including quants and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of traders, data scientists, and students of finance, data science, and machine learning seeking a one-stop resource from a recognized expert in quantitative finance.

Praise for QUANTITATIVE PORTFOLIO MANAGEMENT

“This is a wonderful book: deep, original, witty, and provocative. It is a survey of the most important ideas and methods of modern quantitative investment that should enthrall both seasoned and junior quants. A must-read that will no doubt become a classic.”

―Jean-Philippe Bouchaud, Chairman and Chief Scientist, Capital Fund Management; member of the French Academy of Sciences

“In his lively and clever style, Isichenko shares from his decades of experience at some of the top quantitative trading shops. Even seasoned veterans will find unfamiliar ideas, as he includes many concepts and models nowhere else in print.”

―Colin Rust, Quantitative Portfolio Manager, Cubist Systematic Strategies

“I encouraged Michael Isichenko not to seek publication of this book, a comprehensive and accurate survey of market structure and data and mathematical and computational approaches and results for systematic trading. I am grateful that he enlarged and extended it beyond a first draft. I now hope that competitors have so much to absorb that they'll misapply much and not eliminate all remaining avenues to profit for my firm.”

―Aaron Sosnick, Founder, Analytics, Research & Trading Advisors

An in-depth and telling handbook for quant portfolio management from a leading industry expert

Quantitative Portfolio Management is a complete and up-to-date exploration of the quantitative analysis process. You’ll find information about sourcing financial data, alpha generation approaches, dealing with risk, portfolio construction, and trade execution.

The book covers both theoretical and algorithmic machine learning subjects in the context of competition-based market efficiency that imposes limits on complexity and performance of quantitative trading models. In addition to foundational subjects that form the basis of quantitative finance, you’ll also learn about lesser-known machine learning algorithms and rarely discussed topics, like forecast combining and multi-period portfolio optimization. The author expertly balances practical observations drawn from his years as a practicing portfolio manager with financial and mathematical insights in statistics and machine learning.