Quantitative portfolio management : the art and science of statistical arbitrage / Michael Isichenko.

Format:

Book

Author/Creator:

Isichenko, Michael, author.

Language:

English

Subjects (All):

Portfolio management--Mathematical models.

Portfolio management.

Arbitrage.

Physical Description:

xxxi, 261 pages : illustrations ; 24 cm

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2021]

Summary:

"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 liquidity for investors and competing, as best they can, at finding and exploiting any mispricings. The result is highly efficient financial markets not immune to occasional events of crowding, bubbling, and liquidation panic. This book covers all the major parts of the quantitative trading process starting with sourcing financial data, learning future asset returns from historical data, generating and combining multiple forecasts, dealing with risk, building optimal portfolio of stocks subject to risk preferences and trading costs, and executing trades. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual events and thoughts "from the trenches", as observed by a quantitative portfolio manager, and even actual questions asked at countless quant interviews. The intended audience includes practicing quants who will encounter things both familiar and novel (such lesser known ML algorithms or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), and the general public interested in quantitative and algorithmic trading from a broad scientific, and occasionally ironic, standpoint"-- Provided by publisher.

Contents:

Machine generated contents note: ch. 1 Market Data

1.1. Tick and bar data

1.2. Corporate actions and adjustment factor

1.3. Linear vs log returns

ch. 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&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

ch. 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

ch. 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

ch. 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

ch. 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

ch. 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.

Notes:

Includes bibliographical references and indexes.

Other Format:

Online version: Isichenko, Michael. Quantitative portfolio management

ISBN:

9781119821328

1119821320

OCLC:

1237395968