The reasons why your Professors were unaware of neural networks in forecasting stock prices is because, if you intend to model stock prices alone, it does not work, hence nothing has surpassed either ARIMA modelling or adaptive filtering, eg. Kalman filters. The reason why NN does not work is because they can be easily trained to perfectly fit the training data, but they have virtually no forward predictability on test data, as they by the nature of the method overfit a model.
However, NN can be very accurate in forecasting prices using factor methods, eg. linking ice cream stocks to such things as daily temperature, season, GDP, Disposable income, etc. But not on prices alone.
It is hard to see how you have got to do a Ph.D. when fundamentally do not understand that a differential equation gives either an unstable or stable solution( I am making an assumption here, I could be incorrect, you may be aware), given that the BS formula can be derived by a differential equation analogous to Einstein's heat diffusion equation, it is possible a prediction within a range is possible, it is in the realm of econophysics, which I will leave for others.