Data Science for Finance
The module “Data Science for Finance” is the largest among the four modules of the ARPM Body of Knowledge.
This module covers the statistical tools needed to model and estimate the joint dynamics of the markets. Unlike related approaches in computer science or engineering, we root our coverage of data science into the pillars of quantitative statistics for finance (the “P” in ARPM). In particular:
– we introduce all machine learning/artificial intelligence models as generalizations of linear factor models, omnipresent (and mis-used) across finance
– we connect the estimation/calibration of all machine learning/artificial intelligence models with classical and Bayesian econometrics
– we address backtesting and model/estimation risk in the context of decision theory
– we translate machine learning/artificial intelligence inference into market view processing: distributional stress-testing for risk management and portfolio/business construction for portfolio management.