Generalized PCR Model for Stock Price Modelling
This group project develops a generalized principal component regression approach for stock price modelling and stock selection. The focus was on handling high-dimensional financial factors through PCA-based compression before prediction.
Highlights
- Implemented a generalized PCR pipeline in R.
- Worked with high-dimensional equity factor data sourced from Bloomberg.
- Compared PCA-based modelling with more standard regression baselines.
Methods
- Used PCA to reduce dimensionality in factor-ratio predictors.
- Applied the compressed factor representation in a regression framework for return estimation.
- Evaluated model performance against simpler benchmark approaches.
Findings
- The project provided a practical example of how dimensionality reduction can improve financial modelling workflows.
- It also highlighted the tradeoff between interpretability and predictive efficiency in factor-based models.