ML_in_Finance

Quantitative methods in finance

Portfolio Management

Portfolio optimization, efficient frontier construction and the capital market line

Supervised Learning: Logistic Regressions, KNN, SVM & Evaluation metrics for supervised ML models
Supervised Learning: Decision Trees, Bagging (Random Forest & Extra Trees), Boosting (AdaBoost & Gradient Boosting)
Unsupervised Learning: clustering (K-Means & DBSCAN), PCA & Evaluation metrics for unsupervised ML models
Textual Analysis of the captions of news on companies, the sentiment on the general public and the effect on company stock price

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
Analyzing Returns.ipynb		Analyzing Returns.ipynb
Auto Regression and Partial Autocorrelation.ipynb		Auto Regression and Partial Autocorrelation.ipynb
Clustering & Dimension reductions.ipynb		Clustering & Dimension reductions.ipynb
Loan default prediction using SVM.ipynb		Loan default prediction using SVM.ipynb
Logit, KNN, SVM.ipynb		Logit, KNN, SVM.ipynb
Modeling inflation and unemployment rates in the US.ipynb		Modeling inflation and unemployment rates in the US.ipynb
Multi-Asset Efficient frontier & Global Sharpe Ratio.ipynb		Multi-Asset Efficient frontier & Global Sharpe Ratio.ipynb
NLP.ipynb		NLP.ipynb
README.md		README.md
VaR Analysis and Efficient Frontier Analysis.ipynb		VaR Analysis and Efficient Frontier Analysis.ipynb