1. Discussion of “Optial Investment in Online Loans” by Nina Troha, Kay Giesecke and Abhishek Sheshadri
Balgobin Y., Telecom ParisTech
10th Financial Risks International Forum
March 27, 2017

2. Summary of paper

3. Summary of paper
Goal: using machine learning techniques to construct optimal loan- based portfolios.

4. Summary of paper
Goal: using machine learning techniques to construct optimal loan- based portfolios.
Data: Lending Club loans.

5. The approach
First step: classification of loan outcomes using a random forest algorithm.

6. The approach
First step: classification of loan outcomes using a random forest algorithm.
Second step: estimation of cash flows using a beta regression.

7. First step – Loan outcomes
Use of a random forest algorithm, compared with:
FICO score logistic regression model
Basic logistic regression model: addition of information available about loan and borrowers features (loan purpose, annual income, etc.)
Extended logistic regression model

8. First step – Loan outcomes
Use of a random forest algorithm, compared with:
FICO score logistic regression model
Basic logistic regression model: addition of information available about loan and borrowers features (loan purpose, annual income, etc.)
Extended logistic regression model
Advantage of the random forest: more efficient to distinguish risky projects with high returns and risky projects likely to default.

9. First step – Loan outcomes
Use of a random forest algorithm, compared with:
FICO score logistic regression model
Basic logistic regression model: addition of information available about loan and borrowers features (loan purpose, annual income, etc.)
Extended logistic regression model
Advantage of the random forest: more efficient to distinguish risky projects with high returns and risky projects likely to default.
 Best performance for the random forest algorithm.

10. First step – Loan outcomes
Use of a random forest algorithm, compared with:
FICO score logistic regression model
Basic logistic regression model: addition of information available about loan and borrowers features (loan purpose, annual income, etc.)
Extended logistic regression model
Advantage of the random forest: more efficient to distinguish risky projects with high returns and risky projects likely to default.
Best performance for the random forest algorithm.
Comparison with generalized classification and regression trees (CART) modelling ?

11. Second step – Loan returns
Use of beta regression, compared with:
FICO score regression
Basic regression
Extended regression
Lasso regression
Ridge regression
Advantage of the beta regression: no assumption of constant standard deviation for returns.

12. Second step – Loan returns
Use of beta regression, compared with:
FICO score regression
Basic regression
Extended regression
Lasso regression
Ridge regression
Advantage of the beta regression: no assumption of constant standard deviation for returns.
 The beta regression produces the best results.

13. Portfolio selection
Predictions from the two-step approach used to perform a CVaR optimization.

14. Portfolio selection
Predictions from the two-step approach used to perform a CVaR optimization.
Comparison with rule-based investment strategies.

15. Portfolio selection
Predictions from the two-step approach used to perform a CVaR optimization.
Comparison with rule-based investment strategies.
 The two-step approach outperforms all of these strategies.

16. Portfolio selection
Predictions from the two-step approach used to perform a CVaR optimization.
Comparison with rule-based investment strategies.
 The two-step approach outperforms all of these strategies.
 Comparison with similar portfolio optimization ?