To be announced

Applications of neural networks to finance

"Classical" approach of Finance is using models based on partial differential equations (Black-Scholes type equations) and stochastic processes (Ito difussions) for more than 4 decades. It is worth to remark that none of these models is an ultimate correct model of the markets. Contrary to the laws of physics, which stay unchanged forever, the models that govern financial markets tend to get obsolite relatively fast, and hence, their need of continous improvement. This project advances the idea that an ultimate model of financial markets might involve a sophisticated NN trainned on the current available market data.

Furthermore, the classical methods are not efficient when trying to finding alpha, or the signal in the market, on which the trader should bid to make a profit. It is our belief that the hidden signals, or irregularities in the market can be unveiled by using deep neural networks, including both CNNs and RNNs.

The project involves using NN to study the following problems:

Team Members:

Ovidiu Calin Paul Brown Aaron Bolton Cedric Bernard
Cristian Paul Bara Oana Ignat Hai Tran Bach Dan Capps