A comprehensive look at the tools and techniques used in quantitative equity management Some books attempt to extend portfolio theory, but the real issue today relates to the practical implementation of the theory introduced by Harry Markowitz and others who followed. A comprehensive look at the tools and techniques used in quantitative equity management Some books attempt to extend portfolio theory, but the real issue today relates to the practical implementation of the theory introduced by Harry Markowitz and others who followed. Preface. About the Authors. Chapter 1 Introduction. In Praise of Mathematical Finance. Studies of the Use of Quantitative Equity Management. Looking Ahead for Quantitative Equity Investing. Chapter 2 Financial Econometrics I: Linear Regressions. Historical Notes. Covariance and Correlation. Regressions, Linear Regressions, and Projections. Multivariate Regression. Quantile Regressions. Regression Diagnostic. Robust Estimation of Regressions. Classification and Regression Trees. Summary. Chapter 3 Financial Econometrics II: Time Series. Stochastic Processes. Time Series. Stable Vector Autoregressive Processes. Integrated and Cointegrated Variables. Estimation of Stable Vector Autoregressive (Var) Models. Estimating the Number of Lags. Autocorrelation and Distributional Properties of Residuals. Stationary Autoregressive Distributed Lag Models. Estimation of Nonstationary VAR models. Estimation with Canonical Correlations. Estimation with Principal Component Analysis. Estimation with the Eigenvalues of the Companion Matrix. Nonlinear Models in Finance. Causality. Summary. Chapter 4 Common Pitfalls in Financial Modeling. Theory and Engineering. Engineering and Theoretical Science. Engineering and Product Design in Finance. Learning, Theoretical, and Hybrid Approaches to Portfolio Management. Sample Biases. The Bias in Averages. Pitfalls in Choosing from Large Data Sets. Time Aggregation of Models and Pitfalls in the Selection of Data Frequency. Model Risk and its Mitigation. Summary. Chapter 5 Factor Models and Their Estimation. The Notion of Factors. Static Factor Models. Factor Analysis and Principal Components Analysis. Why Factor Models of Returns. Approximate Factor Models of Returns. Dynamic Factor Models. Summary. Chapter 6 Factor Based Trading Strategies I: Factor Construction and Analysis. Factor Based Trading. Developing Factor Based Trading Strategies. Risk to Trading Strategies. Desirable Properties of Factors. Sources for Factors. Building Factors from Company Characteristics. Working with Data. Analysis of Factor Data. Summary. Chapter 7 Factor Based Trading Strategies II: Cross Sectional Models and Trading Strategies. Cross Sectional Methods for Evaluation of Factor Premiums. Factor Models. Performance Evaluation of Factors. Model Construction Methodologies for a Factor Based Trading Strategy. Backtesting. Backtesting Our Factor Trading Strategy. Summary. Chapter 8 Portfolio Optimization: Basic Theory and Practice. Mean Variance Analysis: Overview. Classical Framework for Mean Variance Optimization. Mean variance Optimization with a Risk Free Asset. Portfolio Constraints Commonly Used in Practice. Estimating the Inputs Used in Mean Variance Optimization: Expected Return and Risk. Portfolio Optimization with Other Risk Measures. Summary. Chapter 9 Portfolio Optimization: Bayesian Techniques and the Black Litterman Model. Practical Problems Encountered in Mean Variance Optimization. Shrinkage Estimation. The Black Litterman Model. Summary. Chapter 10 Robust Portfolio Optimization. Robust Mean Variance Formulations. Using Robust Mean Variance Portfolio Optimization in Practice. Some Practical Remarks on Robust Portfolio Optimization Models. Summary. Chapter 11 Transaction Costs and Trade Execution. A Taxonomy of Transaction Costs. Liquidity and Transaction Costs. Market Impact Measurements and Empirical Findings. Forecasting and Modeling Market Impact. Incorporating Transaction Costs in Asset Allocation Models. Integrated Portfolio Management: Beyond Expected Return and Portfolio Risk. Summary. Chapter 12 Investment Management and Algorithmic Trading. Market Impact and the Order Book. Optimal Execution. Impact Models. Popular Algorithmic Trading Strategies. What Is Next? Some Comments about the High Frequency Arms Race. Summary. Appendix A Data Descriptions and Factor Definitions. The MSCI World Index. One Month LIBOR. The Compustat Point in Time, IBES Consensus Databases and Factor Definitions. Appendix B Summary of Well Known Factors and Their Underlying Economic Rationale. Appendix C Review of Eigenvalues and Eigenvectors. The SWEEP Operator. Index.