Quantitative portfolio management the art and science of statistical arbitrage

"Quantitative trading of financial securities is a multi-billion dollar business employing thousands of portfolio managers and quantitative analysts ("quants") trained in mathematics, physics, or other "hard" sciences. The quants trade stocks and other instruments creating l...

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Detalles Bibliográficos
Otros Autores:	Isichenko, Michael, author (author)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Hoboken, New Jersey : John Wiley & Sons, Incorporated [2021]
Colección:	Wiley finance series
Materias:	Portfolio management > Mathematical models.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009644301806719

Tabla de Contenidos:

Cover
Title Page
Copyright
Contents
List of Figures
Code Listings
Preface
About this Book
Abstract
Acknowledgments
Introduction
Chapter 1 Market Data
1.1 Tick and bar data
1.2 Corporate actions and adjustment factor
1.3 Linear vs log returns
Chapter 2 Forecasting
2.1 Data for forecasts
2.1.1 Point‐in‐time and lookahead
2.1.2 Security master and survival bias
2.1.3 Fundamental and accounting data
2.1.4 Analyst estimates
2.1.5 Supply chain and competition
2.1.6 M&amp
A and risk arbitrage
2.1.7 Event‐based predictors
2.1.8 Holdings and flows
2.1.9 News and social media
2.1.10 Macroeconomic data
2.1.11 Alternative data
2.1.12 Alpha capture
2.2 Technical forecasts
2.2.1 Mean reversion
2.2.2 Momentum
2.2.3 Trading volume
2.2.4 Statistical predictors
2.2.5 Data from other asset classes
2.3 Basic concepts of statistical learning
2.3.1 Mutual information and Shannon entropy
2.3.2 Likelihood and Bayesian inference
2.3.3 Mean square error and correlation
2.3.4 Weighted law of large numbers
2.3.5 Bias‐variance tradeoff
2.3.6 PAC learnability, VC dimension, and generalization error bounds
2.4 Machine learning
2.4.1 Types of machine learning
2.4.2 Overfitting
2.4.3 Ordinary and generalized least squares
2.4.4 Deep learning
2.4.5 Types of neural networks
2.4.6 Nonparametric methods
2.4.7 Hyperparameters
2.4.8 Cross‐validation
2.4.9 Convex regression
2.4.10 Curse of dimensionality, eigenvalue cleaning, and shrinkage
2.4.11 Smoothing and regularization
2.4.11.1 Smoothing spline.
2.4.11.2 Total variation denoising
2.4.11.3 Nadaraya-Watson kernel smoother
2.4.11.4 Local linear regression
2.4.11.5 Gaussian process
2.4.11.6 Ridge and kernel ridge regression.
2.4.11.7 Bandwidth and hypertuning
2.4.11.8 Lasso regression
2.4.11.9 Dropout
2.4.12 Generalization puzzle of deep and overparameterized learning
2.4.13 Online machine learning
2.4.14 Boosting
2.4.15 Twicing
2.4.16 Randomized learning
2.4.17 Latent structure
2.4.18 No free lunch and AutoML
2.4.19 Computer power and 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
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
3.5.1 PCA, PCR, CCA, ICA, LCA, and PLS
3.5.2 Clustering
3.5.3 Hierarchical combining
3.6 Synthetic security view
3.7 Collaborative filtering
3.8 Alpha pool management
3.8.1 Forecast development guidelines
3.8.1.1 Point-in-time data
3.8.1.2 Horizon and scaling
3.8.1.3 Type of target return
3.8.1.4 Performance metrics
3.8.1.5 Measure of forecast uncertainty
3.8.1.6 Correlation with existing forecasts
3.8.1.7 Raw feature library
3.8.1.8 Overfit handling
3.8.2 Pnl attribution
3.8.2.1 Marginal attribution
3.8.2.2 Regression-based attribution
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
Chapter 5 Trading Costs and Market Elasticity
5.1 Slippage
5.2 Impact
5.2.1 Empirical observations.
5.2.2 Linear impact model
5.2.3 Instantaneous impact cost model
5.2.4 Impact arbitrage
5.3 Cost of carry
5.4 Market‐wide impact and elasticity
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
6.4.1 Optimization with fast impact decay
6.4.2 Optimization with exponentially decaying impact
6.4.3 Optimization conditional on a future position
6.4.4 Position value and utility leak
6.4.5 Optimization with slippage
6.5 Multi‐period portfolio optimization
6.5.1 Unconstrained portfolio optimization with linear impact costs
6.5.2 Iterative handling of factor risk
6.5.3 Optimizing future EMA positions
6.5.4 Portfolio optimization using utility leak rate
6.5.5 Notes on portfolio optimization with slippage
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
6.10.1 Trade curve
6.10.2 Forecast‐timed execution
6.10.3 Algorithmic trading and HFT
6.10.4 HFT controversy
Chapter 7 Simulation
7.1 Simulation vs production
7.2 Simulation and overfitting
7.3 Research and simulation efficiency
7.4 Paper trading
7.5 Bugs
Afterword: Economic and Social Aspects of Quant Trading
Appendix
Index
Question Index
Quotes Index
Stories Index
EULA.

Quantitative portfolio management the art and science of statistical arbitrage

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