Abstract. This paper investigates the usefulness of the real-time macroeconomic news-flow as a leading indicator of firm-level end-of-quarter realized earnings. Using recent advances in macroeconomics, I develop a nowcasting model for quarterly earnings and provide two main findings. First, I show that my model provides out-of-sample expectations that are as accurate as analysts’ forecasts. Second, macroeconomic news embedded in my nowcasts is not fully incorporated into investors’ earnings expectations and predicts future abnormal returns around earnings announcements. These findings have three main implications for capital markets research. First, real-time macroeconomic news can be used to update earnings expectations in real-time. Second, there are economic benefits of doing so, as evidenced by the magnitude of risk-adjusted returns around earnings announcements. Third, after three decades of almost nonexistent research on time-series models for quarterly earnings, the door is open again for fruitful research in this area.

Abstract. We propose and find that aggregate special items conveys more information about future real GDP growth than aggregate earnings before special items because the former contains advance news about future economic outcomes. A two-stage rational expectations test reveals that professional forecasters fully understand the information content of aggregate earnings before special items but underestimate that of aggregate special items when revising their GDP forecasts. Using vector autoregressions, we show that aggregate earnings before special items has predictive ability for GDP because, as suggested by previous literature, it acts as a proxy for corporate profits included in national income. In contrast, aggregate special items captures changes in the behavior of economic agents on a timely basis, which in turn have real effects on firms’ investment and hiring, as well as consumers’ wealth and spending. Consistent with news-driven business cycles, we find that aggregate special items produces synchronized movements across macroeconomic aggregates.

Abstract. We use a standard dynamic factor model to extract new factors based on the real-time flow of accounting data from the corporate financial reports. The extracted accounting factors exploit across-sector comovements in corporate value creation drivers and can be used together with other closely watched economic indicators. We show that our weekly updated accounting factors are incrementally relevant for nowcasting and forecasting major components of economic output in the BEA's National Income and Product Accounts. Overall, our paper pioneers a new approach to incorporating the continuous flow of accounting data within the context of dynamic factor models.