What Can Analysts Learn from Artificial Intelligence about Fundamental Analysis?

69 Pages Posted: 8 Jan 2021 Last revised: 20 Jan 2022

See all articles by Oliver Binz

Oliver Binz

INSEAD

Katherine Schipper

Duke University - Fuqua School of Business

Kevin Standridge

Duke University, Fuqua School of Business, Students

Date Written: January 20, 2022

Abstract

We use machine learning to estimate Nissim and Penman’s (2001) (NP) structural framework that decomposes profitability into four levels of increasingly disaggregated profitability drivers. Our analysis has two distinct features: first, we apply machine learning to accommodate the non-linearities that precluded NP from estimating their framework and second, we analyze the financial statement design choices in NP to provide insights for the teaching and practice of fundamental analysis. We find that out-of-sample profitability forecasts obtained by applying machine learning to NP’s framework are more accurate than those from benchmark models, and that investing strategies based on intrinsic values generated from our profitability forecasts yield risk-adjusted returns. With respect to insights for fundamental analysis, we find that focusing on operating activities, core items and five-year-horizon forecasts improves performance while using a long time series of past information impairs performance. We find mixed evidence of benefits from increasingly granular disaggregation of profitability.

Keywords: Financial Statement Analysis, Machine Learning, Earnings Forecasting

JEL Classification: C53, G10, M41

Suggested Citation

Binz, Oliver and Schipper, Katherine and Standridge, Kevin, What Can Analysts Learn from Artificial Intelligence about Fundamental Analysis? (January 20, 2022). Available at SSRN: https://ssrn.com/abstract=3745078 or http://dx.doi.org/10.2139/ssrn.3745078

Oliver Binz (Contact Author)

INSEAD ( email )

Boulevard de Constance
CEDEP No. 11
F-7705 Fontainebleau Cedex, 77305
France

Katherine Schipper

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States

Kevin Standridge

Duke University, Fuqua School of Business, Students ( email )

Durham, NC
United States

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