What to read after Quantitative Equity Portfolio Management, 2nd edition?

The classic guide that taught a generation of institutional investors how to construct and manage high-yield quant portfolios—now updated for the new generation

Quantitative Equity Portfolio Management is a comprehensive guide to the entire process of constructing and managing a high-yield quantitative equity portfolio. This detailed handbook begins with the basic principles of quantitative active management and then clearly outlines how to build an equity portfolio using those powerful concepts.

This edition of the go-to guide for quant investing has been updated with critical new data, information, and insights, including:

• All table and graph data updated to 2020
• The secret ingredients to building smart beta ETFs and mutual funds
• A new list of behavioral biases that lead to investment anomalies
• Entirely new factor definitions and test of their outperformance with real stock return data
• New labs using real data written in R, MATLAB, and STATA with new techniques to optimize professional portfolios
• New methods to deal with outlier data
• The author’s new research on transaction cost problems
• Detailed uses of ESG data to create socially responsible portfolios
• Downloadable monthly factor returns from the authors

Quantitative Equity Portfolio Management delivers a complete, easy-to-apply methodology for creating an equity portfolio that maximizes returns and minimizes risks. It covers every step of the process, including basic models, stock screening and ranking, fundamental and economic factor modelling, forecasting factor premiums and exposures, building market neutral portfolios, tax management, performance measurement and attribution, and backtesting.

An essential reference for professional money managers and students taking advanced investment courses, Quantitative Equity Portfolio Management offers a full array of methods for effectively developing high-performance equity portfolios that deliver lucrative returns for clients.