With note-trading between users at the core of our Liquid Match model, we want to ensure both sides of a transaction are treated fairly, and we accomplish this with our fair pricing strategy.
When automating the trade of a note between users through the secondary market, simply pricing the note at par (equal to the outstanding balance) will result in the buyer getting a lower yield than the seller.
This is partly due to the negative impact of Lending Club’s 1% investor fee. Learn more about the effect Lending Club’s fee structure has on YTM.
Our strategy is to price the note so that the buyer gets the same Yield to Maturity (the return on a note if held to maturity and received all payments according to payment schedule) as she could have gotten if she purchased a similar note on the primary market.
We refer to this process as “making the buyer whole,” and the price as a “fair price”.
Calculating a fair price is two-step process:
1. Determine the YTM of a newly-issued note on the primary market with the same term*, sub-grade, and interest rate as the note in transaction. This accounts for any interest rate change since the note in transaction was issued.
*Notes with 37-60 remaining payments are considered having a 5-year term, and with 1-36 remaining payments are considered having a 3-year term.
Solve for YTM in the following equation using Newton’s method: Loan Amount = (payment-fee)/(1+YTM/12)+(payment-fee)/((1+YTM/12)2 ) + ... + (payment-fee)/((1+YTM/12)term )
2. Plug the YTM obtained from step 1 into the following bond pricing formula to calculate the fair price any month (t).
P(t) = (payment-fee)/(1+YTM/12)+(payment-fee)/((1+YTM/12)2 ) + ... + (payment-fee)/((1+YTM/12)t )
Liquid Match is a win-win
What better way to foster a more active secondary market than to automate the trade of notes between users in a seamless way that mutually benefits both sides of the equation?
As we continuously invest user funds, our Liquid Match model automatically matches Liquid P2P buyers and sellers FIRST to both boost returns and accelerate liquidity. It’s a true win-win.
In addition to making the buyer whole with our fair pricing strategy, our machine-learning algorithms select loans that are less likely to default to keep buyers invested in high quality notes with a seasoned payment history and shorter time to maturity.
On the other side of a successful Liquid Match transaction, sellers are able to accelerate the liquidation of their notes.
Read more about our how our patent-pending model.