Can we use regular Loss Function in Finance Deep Learning? (Exploring the properties of different loss functions) I am currently working on replicating financial deep learning papers to see if they actually would work in real markets.
I have this question that bothers me.
It is okay to use regular lossFunction such as MSE, RMSE, MAE, MAPE,..., other absolute difference loss function in Finance?
It seems odd.
For example, let's compare two scenarios.
In my opinion, it makes the most sense to first consider which functional of the (unknown, and typically only implicitly considered) future distribution we want to elicit: the mean, the median, a particular quantile, or some more exotic functional. Only once we know that can we decide on a loss function, and we should choose a loss function that is optimized in expectation by the sought-after functional. I have laid out my arguments in Kolassa (2020, IJF), relying mainly on previous work of Tilmann Gneiting. This is also the approach I follow in my answer to What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?.
