Forecasting earnings and returns : a review of recent advancements

Jeremiah Green, Wanjia Zhao

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting earnings and returns presents difficult challenges. In the context of these challenges, we discuss recent papers that confront the challenges and present promising advancements and paths for future research.

MoreLess

Year of publication:	2022
Authors:	Green, Jeremiah ; Zhao, Wanjia
Published in:	The Journal of finance and data science : JFDS. - Amsterdam [u.a.] : Elsevier, ISSN 2405-9188, ZDB-ID 2837532-4. - Vol. 8.2022, p. 120-137
Subject:	Earnings \| Forecasting \| Machine learning \| Measurement error \| Returns \| Uncertainty \| Prognoseverfahren \| Forecasting model \| Kapitaleinkommen \| Capital income \| Künstliche Intelligenz \| Artificial intelligence \| Gewinn \| Profit \| Prognose \| Forecast \| Statistischer Fehler \| Statistical error \| Finanzanalyse \| Financial analysis \| Gewinnprognose \| Earnings announcement

freely available

Full text |

More access options

doi.org

Check Google Scholar

In libraries world-wide (WorldCat)

In German libraries (KVK)

subito order

I need help

Type of publication:	Article
Type of publication (narrower categories):	Aufsatz in Zeitschrift ; Article in journal
Language:	English
Other identifiers:	10.1016/j.jfds.2022.04.004 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014433781