Earnings Forecasts and Revisions, Price Momentum, and Fundamental Data: Further Explorations of Financial Anomalies

John Guerard and Andrew Mark

Chapter 30 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 1151-1209 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Earnings forecasting data has been a consistent, and highly statistically significant, source of excess returns. This chapter discusses a composite model of earnings forecasts, revisions, and breadth, CTEF, a model of forecasted earnings acceleration, was developed in 1997 to identify mispriced stocks. Our most important result is that the forecasted earnings acceleration variable has produced statistically significant Active and Specific Returns in the Post-Global Financial Crisis Period. Simple earnings revisions and forecasted yields have not enhanced returns in the past 7–20 years, leading many financial observers to declare earnings research passé. We disagree! Moreover, earnings forecasting models complement fundamental data (earnings, book value, cash flow, sales, dividends, liquidity) and price momentum strategies in a composite model for stock selection. The composite model strategy excess returns are greater in international stocks than in US stocks. The models reported in Guerard and Mark (2003) are highly statistically significant in its post-publication time period, including booms, recessions, and highly volatile market conditions.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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