Alternative Data and NLP in Quant Strategies › Key Parameters
Michael BrenndoerferJanuary 4, 20261 min read
Quantitative FinanceAll chapters Part 13 of 15Machine Learning and Alternative Data
Alternative Data and NLP in Quant Strategies › Key ParametersOne section · Sections in this chapter6 / 18 ↓
Sections in this chapter18
Reading tools Definitions & inline links
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Article links
Make inline references clickable
Key parameters for word embeddings.
- embedding_dim: Dimensionality of the embedding vectors, typically 50 to 300. Higher dimensions can encode more relationships but require more data and computation.
- window_size: Context window for training embeddings. Determines how far apart words can be while still influencing each other's representations.
- min_count: Minimum frequency for a word to receive an embedding. Filters out rare words to reduce vocabulary size.
- pretrained_model: Choice of pretrained embeddings (Word2Vec, GloVe, BERT, FinBERT). Domain-specific models like FinBERT perform better on financial text.