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Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage by [Michael Isichenko]

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Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage Kindle Edition

4.0 4.0 out of 5 stars 36 ratings

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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.

From the Inside Flap

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.

--This text refers to the hardcover edition.

Product details

  • 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
  • Customer Reviews:
    4.0 4.0 out of 5 stars 36 ratings

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4.0 out of 5 stars
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36 global ratings

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4.0 out of 5 stars Excellent Introduction
Reviewed in the United Kingdom 🇬🇧 on November 4, 2022
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3.0 out of 5 stars Dust cover and black and white print
Reviewed in the United Kingdom 🇬🇧 on November 17, 2021
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3.0 out of 5 stars Leichte Lekture
Reviewed in Germany 🇩🇪 on December 1, 2022
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Pierre FAY
2.0 out of 5 stars Don't really know what to said... Mix filling about this book
Reviewed in France 🇫🇷 on December 22, 2021
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3.0 out of 5 stars Interesting but does not go very deep
Reviewed in France 🇫🇷 on June 18, 2022
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