Quantitative Portfolio Management

Quantitative Portfolio Management

by Michael Isichenko
Released August 2021
Publisher(s): Wiley
ISBN: 9781119821328

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Book description

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. 

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. List of Figures
  5. Code Listings
  6. Preface
  7. About this Book
  8. Abstract
  9. Acknowledgments
  10. Introduction
  11. Chapter 1: Market Data
    1. 1.1 Tick and bar data
    2. 1.2 Corporate actions and adjustment factor
    3. 1.3 Linear vs log returns
  12. Chapter 2: Forecasting
    1. 2.1 Data for forecasts
    2. 2.2 Technical forecasts
    3. 2.3 Basic concepts of statistical learning
    4. 2.4 Machine learning
    5. 2.5 Dynamical modeling
    6. 2.6 Alternative reality
    7. 2.7 Timeliness-significance tradeoff
    8. 2.8 Grouping
    9. 2.9 Conditioning
    10. 2.10 Pairwise predictors
    11. 2.11 Forecast for securities from their linear combinations
    12. 2.12 Forecast research vs simulation
  13. Chapter 3: Forecast Combining
    1. 3.1 Correlation and diversification
    2. 3.2 Portfolio combining
    3. 3.3 Mean-variance combination of forecasts
    4. 3.4 Combining features vs combining forecasts
    5. 3.5 Dimensionality reduction
    6. 3.6 Synthetic security view
    7. 3.7 Collaborative filtering
    8. 3.8 Alpha pool management
  14. Chapter 4: Risk
    1. 4.1 Value at risk and expected shortfall
    2. 4.2 Factor models
    3. 4.3 Types of risk factors
    4. 4.4 Return and risk decomposition
    5. 4.5 Weighted PCA
    6. 4.6 PCA transformation
    7. 4.7 Crowding and liquidation
    8. 4.8 Liquidity risk and short squeeze
    9. 4.9 Forecast uncertainty and alpha risk
  15. Chapter 5: Trading Costs and Market Elasticity
    1. 5.1 Slippage
    2. 5.2 Impact
    3. 5.3 Cost of carry
    4. 5.4 Market-wide impact and elasticity
  16. Chapter 6: Portfolio Construction
    1. 6.1 Hedged allocation
    2. 6.2 Forecast from rule-based strategy
    3. 6.3 Single-period vs multi-period mean-variance utility
    4. 6.4 Single-name multi-period optimization
    5. 6.5 Multi-period portfolio optimization
    6. 6.6 Portfolio capacity
    7. 6.7 Portfolio optimization with forecast revision
    8. 6.8 Portfolio optimization with forecast uncertainty
    9. 6.9 Kelly criterion and optimal leverage
    10. 6.10 Intraday optimization and execution
  17. Chapter 7: Simulation
    1. 7.1 Simulation vs production
    2. 7.2 Simulation and overfitting
    3. 7.3 Research and simulation efficiency
    4. 7.4 Paper trading
    5. 7.5 Bugs
  18. Afterword: Economic and Social Aspects of Quant Trading
  19. Appendix
    1. A1 Secmaster mappings
    2. A2 Woodbury matrix identities
    3. A3 Toeplitz matrix
  20. Index
  21. Question Index
  22. Quotes Index
  23. Stories Index
  24. End User License Agreement

Product information

  • Title: Quantitative Portfolio Management
  • Author(s): Michael Isichenko
  • Release date: August 2021
  • Publisher(s): Wiley
  • ISBN: 9781119821328