Browsing by Author "Shaw, Kenneth W."

Now showing 1 - 3 of 3
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    Aggregate Financial Misreporting and the Predictability of U.S. Recessions
    (SSRN, 2021) Beneish, Messod D.; Farber, David B.; Glendening, Matthew; Shaw, Kenneth W.; Kelley School of Business - Indianapolis
    We rely on the theoretical prediction that financial misreporting peaks before economic busts to examine whether aggregate ex ante measures of the likelihood of financial misreporting improve the predictability of U.S. recessions. We consider six measures of misreporting and show that the Beneish M-Score significantly improves out-of-sample recession prediction at longer forecasting horizons. Specifically, relative to leading models based on yield spreads and market returns, M-Score increases the average probability of a recession across forecast horizons of six-, seven-, and eight-quarters-ahead by 56 percent, 79 percent, and 92 percent, respectively. These findings are robust to alternative definitions of interest rate spreads, and to controlling for the federal funds rate, investor sentiment, and aggregate earnings growth. We show that the performance of M-Score likely arises because firms with high M-Scores tend to experience negative future performance. Overall, this study provides novel evidence that accounting information can be useful to forecasters and regulators interested in assessing the likelihood of U.S. recessions a few quarters ahead.
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    Aggregate Financial Misreporting and the Predictability of U.S. Recessions and GDP Growth
    (American Accounting Association, 2023-09-01) Beneish, Messod D.; Farber, David B.; Glendening, Matthew; Shaw, Kenneth W.; Kelley School of Business
    This study examines the incremental predictive power of aggregate measures of financial misreporting for recession and real gross domestic product (GDP) growth. We draw on prior research suggesting that misreporting has real economic effects because it represents misinformation on which firms base their investment, hiring, and production decisions. We find that aggregate M-Score incrementally predicts recessions at forecast horizons of five to eight quarters ahead. We also find that aggregate M-Score is significantly associated with lower future growth in real GDP, real investment, consumption, and industrial production. Additionally, our result that aggregate M-Score predicts lower real investment one to four quarters ahead partially accounts for why misreporting predicts recessions five to eight quarters ahead. Our findings are weaker when we use aggregate F-Score as a proxy for misreporting. Overall, this study provides novel evidence that aggregate misreporting measures can aid forecasters and regulators in predicting recessions and real GDP growth.
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    Stock Market Rewards for Earnings that Beat Analyst Earnings Forecasts when the Economy is Unforecastable
    (SSRN, 2021) Baik, Bok; Duong, Hong Kim; Farber, David B.; Shaw, Kenneth W.; Kelley School of Business - Indianapolis
    This study examines whether and why the stock market assigns an incremental premium to the act of beating analyst earnings forecasts when the economy is unforecastable. Our study uses a novel measure of macroeconomic (macro) uncertainty from Jurado et al. (2015) that captures periods during which the real economy is not forecastable and a regression model that controls for the forecast error throughout the quarter. Results show that during high macro uncertainty periods, the market assigns a greater premium to earnings that beat analyst earnings forecasts compared to the premium assigned to these earnings during low macro uncertainty periods. We also report a lower likelihood of managing earnings to beat analyst earnings forecasts during high macro uncertainty periods, suggesting higher accounting information quality. We further show that the incremental premium in high macro uncertainty periods is mainly concentrated within the group of firms that have both low liquidity risk and high accounting information quality. Evidence from our study should be relevant to those interested in understanding the usefulness of earnings during periods of extreme macro uncertainty and forces that determine accounting information quality.