Machine Learning for Factor Investing

1.
호랑이가 담배먹던 시절, 자료는 돈이었습니다. 새로운 지식을 얻을 수 있는 소중한 기회이고 비지니스를 새롭게 만들 수 있는 토대이기도 하였습니다. 인터넷이 등장하기 이전입니다. 인터넷이 등장했지만 상황이 크게 바뀌지 않았습니다. 지금은 잊혀진 듯 하지만 요즘말로 집단지성이라고 할 수 있는 usenet을 통해 문제를 해겨라고 자료를 찾곤 했습니다. 시간이 흐르면서 인터넷을 통해 얻을 수 있는 자료의 폭과 깊이 달라집니다. 폭은 끝이 없고 깊이는 내가 도달할 수 있는 수준까지입니다. 이제는 자료탓을 할 수 없습니다. 개인의 부지런함이나 능력이 변수일 뿐입니다. 하루가 다르게 등장하는 기술과 자료를 보면서 무력감을 느끼곤 합니다. 그래도 흐름이나 알자는 취지로 열심히 쫓아가봅니다.

오늘 자료를 팩터투자와 기계학습을 주제로 한 책입니다. 그렇다고 전문이 없는 책소개는 아니고 전문을 HTML로 읽을 수 있습니다.

Machine Learning for Factor Investing

This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics. The content is threefold. First, we try to simply explain the ideas behind most mainstream machine learning algorithms that are used in equity asset allocation. Second, we mention a wide range of academic references for the readers who wish to push a little further. Finally, we provide hands-on R code samples that show how to apply the concepts and tools on a realistic dataset which we share to encourage reproducibility.

저자중 한명인 Tony Guida 은 Big Data and Machine Learning in Quantitative Investment에 공저자로 참여하였습니다. 목차중 흥미로운 개념이 나옵니다. 1장이 Do Algorithms Dream About Artificial Alphas?인데 Artificial Alpha라는 표현입니다. 아래를 읽어보시면 Artificial Alpha에 대한 설명은 없습니다. 기계학습이 가져올 무언가의 기대감을 Artificial Alpha로 이름붙인 듯 합니다.

Download (PDF, 474KB)

2.
아래의 제목을 선택하시면 관련한 내용을 읽을 수 있습니다.

• Preface
  • What this book is not about
  • The targeted audience
  • How this book is structured
  • Companion website
  • Why R?
  • Coding instructions
  • Acknowledgments
  • Future developments
• I Introduction
• 1 Notations and data
  • 1.1 Notations
  • 1.2 Dataset
• 2 Introduction
  • 2.1 Context
  • 2.2 Portfolio construction: the workflow
  • 2.3 Machine learning is no magic wand
• 3 Factor investing and asset pricing anomalies
  • 3.1 Introduction
  • 3.2 Detecting anomalies
    • 3.2.1 Challenges
    • 3.2.2 Simple portfolio sorts
    • 3.2.3 Factors
    • 3.2.4 Fama-Macbeth regressions
    • 3.2.5 Factor competition
    • 3.2.6 Advanced techniques
  • 3.3 Factors or characteristics?
  • 3.4 Hot topics: momentum, timing and ESG
    • 3.4.1 Factor momentum
    • 3.4.2 Factor timing
    • 3.4.3 The green factors
  • 3.5 The links with machine learning
    • 3.5.1 A short list of recent references
    • 3.5.2 Explicit connections with asset pricing models
  • 3.6 Coding exercises
• 4 Data preprocessing
  • 4.1 Know your data
  • 4.2 Missing data
  • 4.3 Outlier detection
  • 4.4 Feature engineering
    • 4.4.1 Feature selection
    • 4.4.2 Scaling the predictors
  • 4.5 Labelling
    • 4.5.1 Simple labels
    • 4.5.2 Categorical labels
    • 4.5.3 The triple barrier method
    • 4.5.4 Filtering the sample
    • 4.5.5 Return horizons
  • 4.6 Handling persistence
  • 4.7 Extensions
    • 4.7.1 Transforming features
    • 4.7.2 Macro-economic variables
    • 4.7.3 Active learning
  • 4.8 Additional code and results
    • 4.8.1 Impact of rescaling: graphical representation
    • 4.8.2 Impact of rescaling: toy example
  • 4.9 Coding exercises
• II Common supervised algorithms
• 5 Penalized regressions and sparse hedging for minimum variance portfolios
  • 5.1 Penalized regressions
    • 5.1.1 Simple regressions
    • 5.1.2 Forms of penalizations
    • 5.1.3 Illustrations
  • 5.2 Sparse hedging for minimum variance portfolios
    • 5.2.1 Presentation and derivations
    • 5.2.2 Example
  • 5.3 Predictive regressions
    • 5.3.1 Literature review and principle
    • 5.3.2 Code and results
  • 5.4 Coding exercise
• 6 Tree-based methods
  • 6.1 Simple trees
    • 6.1.1 Principle
    • 6.1.2 Further details on classification
    • 6.1.3 Pruning criteria
    • 6.1.4 Code and interpretation
  • 6.2 Random forests
    • 6.2.1 Principle
    • 6.2.2 Code and results
  • 6.3 Boosted trees: Adaboost
    • 6.3.1 Methodology
    • 6.3.2 Illustration
  • 6.4 Boosted trees: extreme gradient boosting
    • 6.4.1 Managing loss
    • 6.4.2 Penalization
    • 6.4.3 Aggregation
    • 6.4.4 Tree structure
    • 6.4.5 Extensions
    • 6.4.6 Code and results
    • 6.4.7 Instance weighting
  • 6.5 Discussion
  • 6.6 Coding exercises
• 7 Neural networks
  • 7.1 The original perceptron
  • 7.2 Multilayer perceptron
    • 7.2.1 Introduction and notations
    • 7.2.2 Universal approximation
    • 7.2.3 Learning via back-propagation
    • 7.2.4 Further details on classification
  • 7.3 How deep we should go and other practical issues
    • 7.3.1 Architectural choices
    • 7.3.2 Frequency of weight updates and learning duration
    • 7.3.3 Penalizations and dropout
  • 7.4 Code samples and comments for vanilla MLP
    • 7.4.1 Regression example
    • 7.4.2 Classification example
    • 7.4.3 Custom losses
  • 7.5 Recurrent networks
    • 7.5.1 Presentation
    • 7.5.2 Code and results
  • 7.6 Other common architectures
    • 7.6.1 Generative adversarial networks
    • 7.6.2 Autoencoders
    • 7.6.3 A word on convolutional networks
    • 7.6.4 Advanced architectures
  • 7.7 Coding exercise
• 8 Support vector machines
  • 8.1 SVM for classification
  • 8.2 SVM for regression
  • 8.3 Practice
  • 8.4 Coding exercises
• 9 Bayesian methods
  • 9.1 The Bayesian framework
  • 9.2 Bayesian sampling
    • 9.2.1 Gibbs sampling
    • 9.2.2 Metropolis-Hastings sampling
  • 9.3 Bayesian linear regression
  • 9.4 Naive Bayes classifier
  • 9.5 Bayesian additive trees
    • 9.5.1 General formulation
    • 9.5.2 Priors
    • 9.5.3 Sampling and predictions
    • 9.5.4 Code
• III From predictions to portfolios
• 10 Validating and tuning
  • 10.1 Learning metrics
    • 10.1.1 Regression analysis
    • 10.1.2 Classification analysis
  • 10.2 Validation
    • 10.2.1 The variance-bias tradeoff: theory
    • 10.2.2 The variance-bias tradeoff: illustration
    • 10.2.3 The risk of overfitting: principle
    • 10.2.4 The risk of overfitting: some solutions
  • 10.3 The search for good hyperparameters
    • 10.3.1 Methods
    • 10.3.2 Example: grid search
    • 10.3.3 Example: Bayesian optimization
  • 10.4 Short discussion on validation in backtests
• 11 Ensemble models
  • 11.1 Linear ensembles
    • 11.1.1 Principles
    • 11.1.2 Example
  • 11.2 Stacked ensembles
    • 11.2.1 Two-stage training
    • 11.2.2 Code and results
  • 11.3 Extensions
    • 11.3.1 Exogenous variables
    • 11.3.2 Shrinking inter-model correlations
  • 11.4 Exercise
• 12 Portfolio backtesting
  • 12.1 Setting the protocol
  • 12.2 Turning signals into portfolio weights
  • 12.3 Performance metrics
    • 12.3.1 Discussion
    • 12.3.2 Pure performance and risk indicators
    • 12.3.3 Factor-based evaluation
    • 12.3.4 Risk-adjusted measures
    • 12.3.5 Transaction costs and turnover
  • 12.4 Common errors and issues
    • 12.4.1 Forward looking data
    • 12.4.2 Backtest overfitting
    • 12.4.3 Simple safeguards
  • 12.5 Implication of non-stationarity: forecasting is hard
    • 12.5.1 General comments
    • 12.5.2 The no free lunch theorem
  • 12.6 First example: a complete backtest
  • 12.7 Second example: backtest overfitting
  • 12.8 Coding exercises
• IV Further important topics
• 13 Interpretability
  • 13.1 Global interpretations
    • 13.1.1 Simple models as surrogates
    • 13.1.2 Variable importance (tree-based)
    • 13.1.3 Variable importance (agnostic)
    • 13.1.4 Partial dependence plot
  • 13.2 Local interpretations
    • 13.2.1 LIME
    • 13.2.2 Shapley values
    • 13.2.3 Breakdown
• 14 Two key concepts: causality and non-stationarity
  • 14.1 Causality
    • 14.1.1 Granger causality
    • 14.1.2 Causal additive models
    • 14.1.3 Structural time series models
  • 14.2 Dealing with changing environments
    • 14.2.1 Non-stationarity: yet another illustration
    • 14.2.2 Online learning
    • 14.2.3 Homogeneous transfer learning
• 15 Unsupervised learning
  • 15.1 The problem with correlated predictors
  • 15.2 Principal component analysis and autoencoders
    • 15.2.1 A bit of algebra
    • 15.2.2 PCA
    • 15.2.3 Autoencoders
    • 15.2.4 Application
  • 15.3 Clustering via k-means
  • 15.4 Nearest neighbors
  • 15.5 Coding exercise
• 16 Reinforcement learning
  • 16.1 Theoretical layout
    • 16.1.1 General framework
    • 16.1.2 Q-learning
    • 16.1.3 SARSA
  • 16.2 The curse of dimensionality
  • 16.3 Policy gradient
    • 16.3.1 Principle
    • 16.3.2 Extensions
  • 16.4 Simple examples
    • 16.4.1 Q-learning with simulations
    • 16.4.2 Q-learning with market data
  • 16.5 Concluding remarks
  • 16.6 Exercises
• V Appendix
• 17 Data description
• 18 Solutions to exercises
  • 18.1 Chapter 4
  • 18.2 Chapter 5
  • 18.3 Chapter 6
  • 18.4 Chapter 7
  • 18.5 Chapter 8: the autoencoder model
  • 18.6 Chapter 9
  • 18.7 Chapter 12: ensemble neural network
  • 18.8 Chapter 13
    • 18.8.1 EW portfolios with the tidyverse
    • 18.8.2 Advanced weighting function
    • 18.8.3 Functional programming in the backtest
  • 18.9 Chapter 16
  • 18.10 Chapter 17