Quant
Product details
- ISBN 9781119821328
- Weight: 612g
- Dimensions: 155 x 229mm
- Publication Date: 15 Nov 2021
- Publisher: John Wiley & Sons Inc
- Publication City/Country: US
- Product Form: Hardback
- Language: English
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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.
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.
