Quantitative Portfolio Management - by Michael Isichenko (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"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"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Discover foundational and advanced techniques in quantitative equity trading from a veteran insider </b></p> <p>In <i>Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage</i>, 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. </p> <p>In this important book, you'll discover: </p> <ul> <li>Machine learning methods of forecasting stock returns in efficient financial markets </li> <li>How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods</li> <li>Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as "benign overfitting" in machine learning </li> <li>The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage </li> </ul> <p>Perfect for investment professionals, like quantitative traders and portfolio managers, <i>Quantitative Portfolio Management</i> 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. </p> <br /><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>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.</p> <p>In <i>Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, </i> 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. <p>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 <p>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. <p>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. <p>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. <p>Perfect for investment professionals, including quants and portfolio managers, <i>Quantitative Portfolio Management</i> 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.<p/><br></br><p><b> About the Author </b></p></br></br><p><b>MICHAEL ISICHENKO, PhD, </b> 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.
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