{"product_id":"quantitative-portfolio-management-9781119821328","title":"Quantitative Portfolio Management","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface 1\u003c\/p\u003e \u003cp\u003eIntroduction 3\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Market Data 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Tick and bar data 9\u003c\/p\u003e \u003cp\u003e1.2 Corporate actions and adjustment factor 10\u003c\/p\u003e \u003cp\u003e1.3 Linear vs log returns 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Forecasting 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Data for forecasts 14\u003c\/p\u003e \u003cp\u003e2.1.1 Point-in-time and lookahead 15\u003c\/p\u003e \u003cp\u003e2.1.2 Security master and survival bias 16\u003c\/p\u003e \u003cp\u003e2.1.3 Fundamental and accounting data 16\u003c\/p\u003e \u003cp\u003e2.1.4 Analyst estimates 17\u003c\/p\u003e \u003cp\u003e2.1.5 Supply chain and competition 18\u003c\/p\u003e \u003cp\u003e2.1.6 M\u0026amp;A and risk arbitrage 18\u003c\/p\u003e \u003cp\u003e2.1.7 Event-based predictors 18\u003c\/p\u003e \u003cp\u003e2.1.8 Holdings and flows 19\u003c\/p\u003e \u003cp\u003e2.1.9 News and social media 20\u003c\/p\u003e \u003cp\u003e2.1.10 Macroeconomic data 21\u003c\/p\u003e \u003cp\u003e2.1.11 Alternative data 21\u003c\/p\u003e \u003cp\u003e2.1.12 Alpha capture 21\u003c\/p\u003e \u003cp\u003e2.2 Technical forecasts 22\u003c\/p\u003e \u003cp\u003e2.2.1 Mean reversion 22\u003c\/p\u003e \u003cp\u003e2.2.2 Momentum 24\u003c\/p\u003e \u003cp\u003e2.2.3 Trading volume 24\u003c\/p\u003e \u003cp\u003e2.2.4 Statistical predictors 25\u003c\/p\u003e \u003cp\u003e2.2.5 Data from other asset classes 25\u003c\/p\u003e \u003cp\u003e2.3 Basic concepts of statistical learning 27\u003c\/p\u003e \u003cp\u003e2.3.1 Mutual information and Shannon entropy 28\u003c\/p\u003e \u003cp\u003e2.3.2 Likelihood and Bayesian inference 32\u003c\/p\u003e \u003cp\u003e2.3.3 Mean square error and correlation 33\u003c\/p\u003e \u003cp\u003e2.3.4 Bias-variance tradeoff 35\u003c\/p\u003e \u003cp\u003e2.3.5 PAC learnability, VC dimension, and generalization error bounds 36\u003c\/p\u003e \u003cp\u003e2.4 Machine learning 40\u003c\/p\u003e \u003cp\u003e2.4.1 Types of machine learning 41\u003c\/p\u003e \u003cp\u003e2.4.2 Overfitting 43\u003c\/p\u003e \u003cp\u003e2.4.3 Ordinary and generalized least squares 44\u003c\/p\u003e \u003cp\u003e2.4.4 Deep learning 46\u003c\/p\u003e \u003cp\u003e2.4.5 Types of neural networks 48\u003c\/p\u003e \u003cp\u003e2.4.6 Nonparametric methods 51\u003c\/p\u003e \u003cp\u003e2.4.7 Cross-validation 54\u003c\/p\u003e \u003cp\u003e2.4.8 Curse of dimensionality, eigenvalue cleaning, and shrinkage 56\u003c\/p\u003e \u003cp\u003e2.4.9 Smoothing and regularization 61\u003c\/p\u003e \u003cp\u003e2.4.9.1 Smoothing spline 62\u003c\/p\u003e \u003cp\u003e2.4.9.2 Total variation denoising 62\u003c\/p\u003e \u003cp\u003e2.4.9.3 Nadaraya-Watson kernel smoother 63\u003c\/p\u003e \u003cp\u003e2.4.9.4 Local linear regression 64\u003c\/p\u003e \u003cp\u003e2.4.9.5 Gaussian process 64\u003c\/p\u003e \u003cp\u003e2.4.9.6 Ridge and kernel ridge regression 67\u003c\/p\u003e \u003cp\u003e2.4.9.7 Bandwidth and hypertuning of kernel smoothers 68\u003c\/p\u003e \u003cp\u003e2.4.9.8 Lasso regression 69\u003c\/p\u003e \u003cp\u003e2.4.10 Generalization puzzle of deep and overparameterized learning 69\u003c\/p\u003e \u003cp\u003e2.4.11 Online machine learning 74\u003c\/p\u003e \u003cp\u003e2.4.12 Boosting 75\u003c\/p\u003e \u003cp\u003e2.4.13 Randomized learning 79\u003c\/p\u003e \u003cp\u003e2.4.14 Latent structure 80\u003c\/p\u003e \u003cp\u003e2.4.15 No free lunch and AutoML 81\u003c\/p\u003e \u003cp\u003e2.4.16 Computer power and machine learning 83\u003c\/p\u003e \u003cp\u003e2.5 Dynamical modeling 87\u003c\/p\u003e \u003cp\u003e2.6 Alternative reality 89\u003c\/p\u003e \u003cp\u003e2.7 Timeliness-significance tradeoff 90\u003c\/p\u003e \u003cp\u003e2.8 Grouping 91\u003c\/p\u003e \u003cp\u003e2.9 Conditioning 92\u003c\/p\u003e \u003cp\u003e2.10 Pairwise predictors 92\u003c\/p\u003e \u003cp\u003e2.11 Forecast for securities from their linear combinations 93\u003c\/p\u003e \u003cp\u003e2.12 Forecast research vs simulation 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Forecast Combining 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Correlation and diversification 98\u003c\/p\u003e \u003cp\u003e3.2 Portfolio combining 99\u003c\/p\u003e \u003cp\u003e3.3 Mean-variance combination of forecasts 102\u003c\/p\u003e \u003cp\u003e3.4 Combining features vs combining forecasts 103\u003c\/p\u003e \u003cp\u003e3.5 Dimensionality reduction 104\u003c\/p\u003e \u003cp\u003e3.5.1 PCA, PCR, CCA, ICA, LCA, and PLS 105\u003c\/p\u003e \u003cp\u003e3.5.2 Clustering 107\u003c\/p\u003e \u003cp\u003e3.5.3 Hierarchical combining 108\u003c\/p\u003e \u003cp\u003e3.6 Synthetic security view 108\u003c\/p\u003e \u003cp\u003e3.7 Collaborative filtering 109\u003c\/p\u003e \u003cp\u003e3.8 Alpha pool management 110\u003c\/p\u003e \u003cp\u003e3.8.1 Forecast development guidelines 111\u003c\/p\u003e \u003cp\u003e3.8.1.1 Point-in-time data 111\u003c\/p\u003e \u003cp\u003e3.8.1.2 Horizon and scaling 111\u003c\/p\u003e \u003cp\u003e3.8.1.3 Type of target return 112\u003c\/p\u003e \u003cp\u003e3.8.1.4 Performance metrics 112\u003c\/p\u003e \u003cp\u003e3.8.1.5 Measure of forecast uncertainty 112\u003c\/p\u003e \u003cp\u003e3.8.1.6 Correlation with existing forecasts 112\u003c\/p\u003e \u003cp\u003e3.8.1.7 Raw feature library 113\u003c\/p\u003e \u003cp\u003e3.8.1.8 Overfit handling 113\u003c\/p\u003e \u003cp\u003e3.8.2 Pnl attribution 114\u003c\/p\u003e \u003cp\u003e3.8.2.1 Marginal attribution 114\u003c\/p\u003e \u003cp\u003e3.8.2.2 Regression-based attribution 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Risk 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Value at risk and expected shortfall 117\u003c\/p\u003e \u003cp\u003e4.2 Factor models 119\u003c\/p\u003e \u003cp\u003e4.3 Types of risk factors 120\u003c\/p\u003e \u003cp\u003e4.4 Return and risk decomposition 121\u003c\/p\u003e \u003cp\u003e4.5 Weighted PCA 122\u003c\/p\u003e \u003cp\u003e4.6 PCA transformation 123\u003c\/p\u003e \u003cp\u003e4.7 Crowding and liquidation 124\u003c\/p\u003e \u003cp\u003e4.8 Liquidity risk and short squeeze 126\u003c\/p\u003e \u003cp\u003e4.9 Forecast uncertainty and alpha risk 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Trading Costs 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Slippage 130\u003c\/p\u003e \u003cp\u003e5.2 Impact 130\u003c\/p\u003e \u003cp\u003e5.2.1 Empirical observations 132\u003c\/p\u003e \u003cp\u003e5.2.2 Linear impact model 133\u003c\/p\u003e \u003cp\u003e5.2.3 Impact arbitrage 135\u003c\/p\u003e \u003cp\u003e5.3 Cost of carry 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Portfolio Construction 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Hedged allocation 137\u003c\/p\u003e \u003cp\u003e6.2 Single-period vs multi-period mean-variance utility 139\u003c\/p\u003e \u003cp\u003e6.3 Single-name multi-period optimization 140\u003c\/p\u003e \u003cp\u003e6.3.1 Optimization with fast impact decay 141\u003c\/p\u003e \u003cp\u003e6.3.2 Optimization with exponentially decaying impact 142\u003c\/p\u003e \u003cp\u003e6.3.3 Optimization conditional on a future position 143\u003c\/p\u003e \u003cp\u003e6.3.4 Position value and utility leak 145\u003c\/p\u003e \u003cp\u003e6.3.5 Optimization with slippage 146\u003c\/p\u003e \u003cp\u003e6.4 Multi-period portfolio optimization 148\u003c\/p\u003e \u003cp\u003e6.4.1 Unconstrained portfolio optimization with linear impact costs 149\u003c\/p\u003e \u003cp\u003e6.4.2 Iterative handling of factor risk 150\u003c\/p\u003e \u003cp\u003e6.4.3 Optimizing future EMA positions 151\u003c\/p\u003e \u003cp\u003e6.4.4 Portfolio optimization using utility leak rate 151\u003c\/p\u003e \u003cp\u003e6.4.5 Notes on portfolio optimization with slippage 152\u003c\/p\u003e \u003cp\u003e6.5 Portfolio capacity 152\u003c\/p\u003e \u003cp\u003e6.6 Portfolio optimization with forecast revision 153\u003c\/p\u003e \u003cp\u003e6.7 Portfolio optimization with forecast uncertainty 156\u003c\/p\u003e \u003cp\u003e6.8 Kelly criterion and optimal leverage 157\u003c\/p\u003e \u003cp\u003e6.9 Intraday optimization and execution 160\u003c\/p\u003e \u003cp\u003e6.9.1 Trade curve 160\u003c\/p\u003e \u003cp\u003e6.9.2 Forecast-timed execution 161\u003c\/p\u003e \u003cp\u003e6.9.3 Algorithmic trading and HFT 162\u003c\/p\u003e \u003cp\u003e6.9.4 HFT controversy 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Simulation 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Simulation vs production 170\u003c\/p\u003e \u003cp\u003e7.2 Simulation and overfitting 171\u003c\/p\u003e \u003cp\u003e7.3 Research and simulation efficiency 172\u003c\/p\u003e \u003cp\u003e7.4 Paper trading 173\u003c\/p\u003e \u003cp\u003e7.5 Bugs 173\u003c\/p\u003e \u003cp\u003eAfterword: Economic and Social Aspects of Quant Trading 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA1 Secmaster mappings 183\u003c\/p\u003e \u003cp\u003eA2 Woodbury matrix identities 184\u003c\/p\u003e \u003cp\u003eA3 Toeplitz matrix 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eQuestions index 195\u003c\/p\u003e \u003cp\u003eQuotes index 197\u003c\/p\u003e \u003cp\u003eStories index 199\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866418262359,"sku":"9781119821328","price":33.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119821328.jpg?v=1722278550","url":"https:\/\/bookcurl.com\/products\/quantitative-portfolio-management-9781119821328","provider":"Book 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