Named entity recognition (NER) aims to identify tokens that represent objects of interest, such as people, countries, or companies. It can be further developed into a knowledge graph that captures semantic and hierarchical relationships among such entities. It is a critical ingredient for applications that, for example, aim to predict the impact of news events or sentiment.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Machine Learning for Trading
- How to read this book
- The rise of ML in the investment industry
- Design and execution of a trading strategy
- ML and algorithmic trading strategies
- Summary
- Market and Fundamental Data
- Alternative Data for Finance
- Alpha Factor Research
- Strategy Evaluation
- How to build and test a portfolio with zipline
- How to measure performance with pyfolio
- How to avoid the pitfalls of backtesting
- How to manage portfolio risk and return
- Summary
- The Machine Learning Process
- Learning from data
- The machine learning workflow
- Basic walkthrough – k-nearest neighbors
- Frame the problem – goals and metrics
- Collecting and preparing the data
- Explore, extract, and engineer features
- Selecting an ML algorithm
- Design and tune the model
- How to use cross-validation for model selection
- Parameter tuning with scikit-learn
- Challenges with cross-validation in finance
- Summary
- Linear Models
- Linear regression for inference and prediction
- The multiple linear regression model
- How to build a linear factor model
- Shrinkage methods: regularization for linear regression
- How to use linear regression to predict returns
- Linear classification
- Summary
- Time Series Models
- Analytical tools for diagnostics and feature extraction
- Univariate time series models
- Multivariate time series models
- Summary
- Bayesian Machine Learning
- How Bayesian machine learning works
- Probabilistic programming with PyMC3
- Summary
- Decision Trees and Random Forests
- Gradient Boosting Machines
- Unsupervised Learning
- Working with Text Data
- How to extract features from text data
- From text to tokens – the NLP pipeline
- From tokens to numbers – the document-term matrix
- Text classification and sentiment analysis
- Summary
- Topic Modeling
- Learning latent topics: goals and approaches
- Latent semantic indexing
- Probabilistic latent semantic analysis
- Latent Dirichlet allocation
- Summary
- Word Embeddings
- How word embeddings encode semantics
- Word vectors from SEC filings using gensim
- Sentiment analysis with Doc2vec
- Bonus – Word2vec for translation
- Summary
- Next Steps
- Key takeaways and lessons learned
- ML for trading in practice
- Conclusion
- Other Books You May Enjoy
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