Earnings Quality on the Street

• In Event: Accruals and Earnings Quality

Abstract

To understand how practitioners operationalize poor earnings quality, we obtain a proprietary dataset of 1,029 reports on aggressive reporting practices over 2003-2015 for 348 unique firms published by a research firm (RF) that sells such data to institutional clients. From these reports, we identify 141 measures of poor earnings quality under five major categories: (i) sales quality; (ii) margin quality; (iii) cash flow quality; (iv) corporate governance quality; and (v) others. As a first-cut to short-list stocks for detailed fundamental analysis, the RF appears to screen for larger, growing firms with higher accruals and lower barriers to arbitrage. The average abnormal return on the day (250 days) after the stock is first flagged by the RF is -1.25% (-10.5%) and such return is incremental to the return attributable to mispricing of accruals. Modified Jones and Dechow-Dichev models of abnormal accruals do not appear to capture RF identified signals well suggesting that such models are too coarse to pick up nuanced fundamental analysis conducted by the RF. In out of sample analyses, we find that (i) the RF signals are associated with future restatements, AAERs and GAAP related lawsuits after controlling for M-Score, total accruals and abnormal accruals; and (ii) the RF signals can be used to improve academic models of earnings quality, as illustrated for a sample of retail stocks. To the Street, our paper suggests that fundamental analysis, beyond just the magnitude of accruals, can predict future stock returns. To academics, our research suggests that granular, context-specific analysis of public data can supplement and improve the workhorse models used to identify poor earnings quality.

Authors

• Urooj Khan, Columbia University

• Venkat Peddireddy, Columbia University

• Shivaram Rajgopal, Columbia University Business School