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Follow the Author
Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage Kindle Edition
Discover foundational and advanced techniques in quantitative equity trading from a veteran insider
In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades.
In this important book, you’ll discover:
- Machine learning methods of forecasting stock returns in efficient financial markets
- How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods
- Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning
- The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage
Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.
From the Back Cover
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.--This text refers to the hardcover edition.
About the Author
MICHAEL ISICHENKO, PhD, is a theoretical physicist and a quantitative portfolio manager who worked at Kurchatov Institute, University of Texas, University of California, SAC Capital Advisors, Société Générale, and Jefferies. He received his doctorate in physics and mathematics from the Moscow Institute of Physics and Technology and is an expert in plasma physics, nonlinear dynamics, and statistical and chaos theory.--This text refers to the hardcover edition.
- ASIN : B09FZVTM64
- Publisher : Wiley; 1st edition (Sept. 10 2021)
- Language : English
- File size : 13760 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Sticky notes : On Kindle Scribe
- Print length : 434 pages
- Best Sellers Rank: #707,803 in Kindle Store (See Top 100 in Kindle Store)
- #431 in Investment Portfolio Management
- #1,386 in Investment Analysis & Strategy
- #1,471 in Stock Market Investing
- Customer Reviews:
About the author
Top reviews from other countries
I would recommend this book to anyone with a good understanding of mathematics and a desire to see how it might be applied to finance.
I would be happy to pay more for a better quality, but the option isn't offered, and the above details were kept from the article description. All "hard covers" are not the same.
Ich habe nix in dem Buch gefunden, was man nicht mit 2 Minuten Internetsuche auch finden würde. Deswegen 3 Punkte, weil viel zu teuer.
In the other hand, if you're a newbie to machine learning, math or finance you'll be lost in a second. If you're enough qualified to understand you already know most of the book content. Maybe there is some tips to take from and some ideas to take from this book but that's clearly in between for a newbie and a pro. The author seems very good but I thought I'll learn more things I was expecting more real exemple and less basic theory..it takes me times to read but I didn't get a lot of value from this reading. But still the author seems very good and the book itself is a beautiful object.