Cuchulainn wrote:Someone wrote this
- reinforcement learning will not replace option pricing models. If you say Black-Scholes because you believe that is what traders and quants actually use, then you are an idiot and have not understood how option pricing works since... 1987. machine learning will have its uses but it will not replace the existing structure of how quants work. Quants work in Q-world - risk neutral measure world - where the drift is given to you (risk free rate, collateral rate, whatever, etc) - and you calibrate vol to ensure E[f(S_T)] matches the market. Machine learning is used in P-world - physical measure world - drift and vol are unknown - and you must estimate them using statistical methods. using machine learning methods in Q-world is stupid, it is not needed.
ML appears to me more like simulating intelligence.
Once the network can beat some humans in some task (for example playing go or chess) its considered it has achieved its goal. But does this mean it is performing well? It appears intelligent because in a very special domain it can outperform humans. But maybe humans do not play chess and go very well.
And in these domains (Go and chess) there is no Q measure. The best move is unknown in general. Google (deepmind) has now published Go and chess programs based on selflearning with no prior human knowledge. So where does this NN calibrate to (or is trained on)? Where is the Q-measure?
You can btw find an abstract of the paper on learning from scratch here:
Learning from scratch