Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression

AAAI 2015 pp. 2203-2209

doi:10.1609/AAAI.V29I1.9515 /aaai/2015/bitvai2015aaai-predicting/

Abstract

Peer-to-peer lending is a new highly liquid market for debt, which is rapidly growing in popularity. Here we consider modelling market rates, developing a non-linear Gaussian Process regression method which incorporates both structured data and unstructured text from the loan application. We show that the peer-to-peer market is predictable, and identify a small set of key factors with high predictive power. Our approach outperforms baseline methods for predicting market rates, and generates substantial profit in a trading simulation.

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Cite

Text

Bitvai and Cohn. "Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9515

Markdown

[Bitvai and Cohn. "Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/bitvai2015aaai-predicting/) doi:10.1609/AAAI.V29I1.9515

BibTeX

@inproceedings{bitvai2015aaai-predicting,
  title     = {{Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression}},
  author    = {Bitvai, Zsolt and Cohn, Trevor},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {2203-2209},
  doi       = {10.1609/AAAI.V29I1.9515},
  url       = {https://mlanthology.org/aaai/2015/bitvai2015aaai-predicting/}
}