Pricing of Exchange-Traded Assets
Predicting the fair market price of a financial instrument
Current pricing methods are not robust to all market conditions
The traditional approach to this problem is to approximate the fair value with a fixed, simple price formula. This approach performs well on average, but yields poor value estimates in adverse market conditions.
We used Optimal Policy Trees to design a new pricing methodology that better leverages the singularities of every market situation, while maintaining interpretability.
Using the right price at the right moment
In collaboration with traders and market experts, we first generated a series of meaningful price formulae, each one being designed to perform well in a specific market scenario. At every timestep, we then monitored traditional market signals and observed which of the generated prices was closest to the true market price.
Leveraging all the available market indicators at each of those timesteps, we used an Optimal Policy Tree to learn which conditions are best suited for what price.
Illustrative Optimal Policy Tree prescribing what type of price to use
Easy to understand, easy to adopt
By the end of the project, our model was consistently outperforming the bank's previous approach to pricing by up to 2%, and its high level of transparency facilitated its adoption by the management team.
Why is the Interpretable AI solution unique?
A novel approach to policy learning
Optimal Policy Trees are the first scalable and optimal solution to this class of problem
Building on the knowledge of experts
At every stage of model development, the transparency of our model facilitates collaboration with domain experts to improve and audit the final results