/ML-in-Fixed-Income-Factor-Investing

Final project for "Machine Learning" course at Software University; completed on 27 November 2020

Primary LanguageJupyter Notebook

Machine Learning in Fixed Income Factor Investing

Applications of Machine Learning in Fixed Income factor investing - finding the proper model and explanatory variables for deriving the Value factor.

Completed on 27th of November as final project for "Machine Learning" course at Software University (Sep - Nov 2020)

  • course 3 of 4 of "Artificial Intelligence" specialization

The project consists of the following chapters:

  1. Factor Investing in Fixed Income Credit - Background
  2. Project aim and specifications - academic research, defining the Value factor
  3. Investment Universe - Selection Process
  4. Pre-processing Data
  5. Explanatory Data Analysis
  6. Data Transformations
  7. Data Split
  8. Models Training & Selection
    • Linear Regression
    • Polynomial Regression
    • Decision Trees & Random Forests
  9. Models Fine-tuning & Improvements
    • Polynomial Regression
      • K-fold Cross Validation
      • Lasso Regularization
      • Principal Component Analysis (PCA)
    • Random Forests
      • Grid Search with K-fold Cross Validation
      • Gradient Boosting Regressor
  10. Final Model Evaluation on the Test Set
  11. Conclusions & Further Research

"Machine Learning" course at Software University covers the following topics (4-hour live Lectures + Labs):

  1. Introduction to Machine Learning
  2. Linear and Logistic Regression
  3. Model Training and Improvement
  4. Tree and Ensemble Methods
  5. Support Vector Machines
  6. Clustering
  7. Dimensionality Reduction
  8. Introduction to Neural Networks
  9. Exam Preparation: End-to-end Project
  10. Course Summary
  • Time Series Analysis - additional
  • Model Deployment - additional

Repository with all live exercises & homework done for the course: https://github.com/pmikov/Machine-Learning-SoftUni---Labs