..., a comprehensive empirical description that could be used to evaluate these predictions is lacking. By integrating recent advances in statistics and machine learning with findings in the accounting literature, we develop an empirical method to identify the relationships, which is consistent with the firm-specific...

...Michael S. Drake; James R. Moon, Jr.; James D. Warren ABSTRACT We employ a novel machine learning technique to classify analysts’ forecast revisions into five types based on how the revision weighs publicly available signals. We label these forecast types as quant, sundry, contrarian, herder...

..., Lawrence, KS, USA. Editor’s note: Accepted by Daniel J. Taylor, under the Senior Editorship of W. Robert Knechel. 26 07 2021 12 10 2023 17 10 2023 2024 earnings forecasting machine learning nearest neighbors Earnings forecasting is important...

...David A. Guenther; Kyle Peterson; Jake Searcy; Brian M. Williams ABSTRACT We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our...

...-term credit losses CECL prediction aggregate credit losses machine learning expected loss overhang Accounting for loan losses by financial institutions has been at the center of a longstanding policy debate because of its effect on banks' regulatory capital. Until recently, financial...

... comparability information-processing costs textual analysis machine learning industries analysts information transfers The value of accounting derives from accumulating and summarizing data about business entities in a standardized format that generates useful information for targeted users (e.g...