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