The Implied Cost of Capital: A Deep Learning Approach

50 Pages Posted: 22 Jun 2020

See all articles by Xinyu Wang

Xinyu Wang

Michigan State University - Department of Finance

Date Written: May 27, 2020

Abstract

I exploit deep learning techniques trained on a set of common accounting items and constructed to mimic features of the human brain to predict future earnings. I show that this model offers incremental explanatory power in predicting future earnings and in estimating the associated implied cost of capital. My forecasting model exhibits less bias than human analyst forecasts and fits the data substantially better than linear regression models. In addition, the derived implied cost-of-capital estimates substantially outperform linear models in their ability to predict future returns. This study illustrates the power of machine learning techniques to improve the accuracy of accounting forecasting.

Keywords: Implied cost of capital; earnings forecasts; machine learning; deep learning; deep neural network; expected returns.

Suggested Citation

Wang, Xinyu, The Implied Cost of Capital: A Deep Learning Approach (May 27, 2020). Available at SSRN: https://ssrn.com/abstract=3612472 or http://dx.doi.org/10.2139/ssrn.3612472

Xinyu Wang (Contact Author)

Michigan State University - Department of Finance ( email )

315 Eppley Center
East Lansing, MI 48824-1122
United States
5175151097 (Phone)

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
44
Abstract Views
201
PlumX Metrics