Forecasting with Alternative Data

Abstract

We consider the problem of forecasting fine-grained company financials, such as daily revenue, from two input types: noisy proxy signals a la alternative data (e.g. credit card transactions) and sparse ground-truth observations (e.g. quarterly earnings reports). We utilize a classical linear systems model to capture both the evolution of the hidden or latent state (e.g. daily revenue), as well as the proxy signal (e.g. credit cards transactions). The linear system model is particularly well suited here as data is extremely sparse (4 quarterly reports per year). In classical system identification, where the central theme is to learn parameters for such linear systems, unbiased and consistent estimation of parameters is not feasible: the likelihood is non-convex; and worse, the global optimum for maximum likelihood estimation is often non-unique.