#Quant_Insights_Conference

از ارایه های روز دوم که اسلایدهاش خدمتتون تقدیم شد، این سخنرانی دیگری هست که انتخاب شد تا در کانال قرار بگیره:

📎 Alternatives to Deep Neural Networks for Function Approximations in Finance, Dr. Alexandre Antonov

We propose neo-classical alternatives to NNs for financial applications. Financial applications are often characterized by a limited amount of data to fit and require explainability (properties of the approximator should be understandable from parameters) and predictability (no local minimum uncertainty). Deep NNs, as a rule, do not satisfy one or more of these requirements. Our solution combines a linear regression against a well-defined set of basis functions derived from the spectral information about the approximated function with a low-dimensional solver. The solver easily extends to higher dimensions and has a controlled guess and bounds. Importantly, our regression basis is efficient for high-dimensional problems.

@FinPy