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Assumptions & Caveats

  • Backtesting results are presented on out-of-sample data from 2007-2012 vintages.
  • Results for other vintages are available. Please contact us to discuss further.
  • We only report results where our algos were able to select at least 500 loans (± small tolerance) for a portfolio given the constraints we overlay.
  • The basic investment strategy is benchmarked to baseline IRRs. In other words, it is a 'relative value' strategy.
  • An advanced version of the strategy could be run that could lead to further 'alpha' generation. It will also serve as a more real-time indicator of underwriting drift.
  • We are not aware of the existence of any efficient mechanism to sell a baseline portfolio as a hedge against our artificial intelligence-based portfolio. This ties our fate, to a certain degree, to the overall sector's performance.
  • We are also unsure of the volume and efficiency of trading peer-to-peer loans on secondary exchanges. Effectively, the strategy is a 'hold to maturity' strategy. Offsetting this, the durations of the portfolios tend to be on the shorter end of the spectrum given high coupons, front-loaded default cycles, full amortization and maximum maturity of five years.
  • The interest rate risk in the portfolio can be further reduced by deploying hedges in the form of US Treasuries and/or interest rate swaps.
  • The returns presented here are gross, unlevered returns. Depending on market conditions, the portfolio could be levered up using warehouse financing. Once enough loan volume has been accumulated, the ABS market may allow for more permanent leverage by way of securitized term financing.
  • This list will most likely grow as we are either reminded of or think about more assumptions and caveats.