Standard Bayesians’ beliefs converge when they receive the same piece of new information. However, when agents initially disagree and have uncertainty about the precision of a signal, their disagreement might instead increase, despite receiving the same information. We demonstrate that this divergence of beliefs leads to a unimodal effect of the absolute surprise in the signal on trading volume. We show that this prediction is consistent with the empirical evidence using trading volume around earnings announcements of U.S. firms. We find evidence of elevated volume following moderate surprises and depressed volume following more extreme surprises, a pattern that is more pronounced when investors hold more distant prior beliefs and are more uncertain about earnings’ precision. The evidence is consistent with the model where investors disagree about stocks’ expected returns and do not know the precision of earnings as a signal about the firm’s value.

This study examines the determinants and implications of the volume of tax-related numbers reported in the financial statements. I document that the volume of tax numbers increases with tax reporting requirements and decreases with the complexity of the tax rate, implying that firms with greater proprietary costs decrease their numeric disclosures once they meet mandatory reporting requirements. With respect to implications, I document that firms reporting more tax numbers improve the transparency of the information environment, reducing the errors and dispersion of analysts’ implied effective tax rate forecasts. In contrast, greater emphasis on narrative tax disclosure does not reduce information frictions, highlighting an important trade-off between numeric detail and strategic narratives. Further investigation suggests that this relationship is driven by more tax numbers in the financial statement footnotes rather than in the face financial statements. These findings suggest firms’ tax information environment improves with a greater volume of numeric tax-related disclosures.

This paper combines traditional fundamentals, such as earnings and cash flows, with measures tailored for growth firms, such as earnings stability, growth stability and intensity of R&D, capital expenditure and advertising, to create an index – GSCORE. A long–short strategy based on GSCORE earns significant excess returns, though most of the returns come from the short side. Results are robust in partitions of size, analyst following and liquidity and persist after controlling for momentum, book-to-market, accruals and size. High GSCORE firms have greater market reaction and analyst forecast surprises with respect to future earnings announcements. Further, the results are inconsistent with a risk-based explanation as returns are positive in most years, and firms with lower risk earn higher returns. Finally, a contextual approach towards fundamental analysis works best, with traditional analysis appropriate for high BM stocks and growth oriented fundamental analysis appropriate for low BM stocks.