Quantitative Portfolio Management

Synopsis

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. 

Summary

Chapter 1: Introduction

* Defines quantitative portfolio management (QPM) as the application of mathematical and statistical techniques to investment decision-making.
* Discusses the benefits and challenges of QPM, such as improved efficiency and reduced human bias.
* Example: Using historical data to analyze the relationship between different asset classes and create a diversified portfolio.

Chapter 2: Mean-Variance Optimization

* Introduces Markowitz's mean-variance optimization (MVO) framework, a popular QPM approach that seeks to maximize expected return (mean) while minimizing risk (variance).
* Explains the construction of efficient frontiers and discusses the optimal portfolio based on risk tolerance.
* Example: Optimizing a portfolio of stocks and bonds to achieve a desired risk-return trade-off.

Chapter 3: Factor Models

* Describes factor models that attempt to explain the systematic risk of assets by capturing macroeconomic or fundamental factors.
* Discusses the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model.
* Example: Estimating betas for different stocks based on factors like market capitalization and book-to-market ratio.

Chapter 4: Performance Evaluation

* Introduces performance metrics commonly used in QPM, such as Sharpe ratio, Jensen's Alpha, and information ratio.
* Explains the importance of risk-adjusted performance evaluation.
* Example: Calculating the Sharpe ratio of a managed portfolio compared to a benchmark.

Chapter 5: Risk Management

* Discusses various risk management techniques, including portfolio diversification, value-at-risk (VaR), and stress testing.
* Explains the role of stress scenarios in assessing portfolio robustness.
* Example: Conducting a stress test to evaluate the impact of a global recession on a diversified portfolio.

Chapter 6: Machine Learning in QPM

* Explores the use of machine learning techniques in QPM, such as decision trees, support vector machines, and neural networks.
* Discusses the challenges and opportunities of incorporating machine learning into investment models.
* Example: Using a machine learning algorithm to predict future stock returns based on historical data.

Chapter 7: Applications of QPM

* Provides real-world examples of QPM applications, including asset allocation, portfolio optimization, and risk management.
* Explains how QPM techniques can enhance investment decisions.
* Example: Implementing a QPM-based strategy to manage a pension fund.

Chapter 8: Frontier Research in QPM

* Discusses emerging trends and future directions in QPM research, such as multi-factor models, big data analytics, and artificial intelligence.
* Explores the challenges and opportunities facing QPM in the evolving investment landscape.
* Example: Investigating the potential of quantum computing to accelerate QPM algorithms.