Accounting misstatements are often detected with substantial delays, leading to “look-ahead bias” in model predictions if the detection lag is not considered. Moreover, the misstatement data-generating process is evolving due to regulatory regime shifts, further complicating the evaluation of model predictions. We design an approach that accounts for detection lags and continuously updates models to adapt to the changing data-generating process. By comparing with the conventional approach that ignores detection lags, we show that the look-ahead bias can substantially inflate prediction performance. We also demonstrate that although leaving a temporal gap between training and test samples can mitigate the look-ahead bias, it sacrifices the model’s predictive power by disconnecting the dynamic data-generating process between training and test periods. We further implement a trading strategy to evaluate the practical utility of the continuously updating approach. Our study presents a new conceptual lens for understanding and evaluating misstatement prediction models. Data Availability: Data are available from the public sources identified in the study.

We develop an optimal tax-timing model that takes into account asymmetric long-term and short-term tax rates for positive capital gains and limited tax deductibility of capital losses. In contrast to the existing literature, this model can help explain why many investors not only defer short-term capital losses to long term but also defer large long-term capital gains and losses. Because the benefit of tax deductibility of capital losses increases with the short-term tax rates, effective tax rates can decrease as short-term capital gains tax rates increase.

We document that public firms participate more than private firms as buyers and sellers of assets in merger waves and their participation is affected more by credit spreads and aggregate market valuation. Public firm acquisitions realize higher gains in productivity, particularly for on‐the‐wave acquisitions and when the acquirer's stock is liquid and highly valued. Our results are not driven solely by public firms' better access to capital. Using productivity data from early in the firm's life, we find that better private firms subsequently select to become public. Initial size and productivity predict asset purchases and sales 10 and more years later.

Despite the importance of the personal income tax around the world, little is known about its impact on innovation. We construct a large database of inventors who patented at publicly listed companies during the 2008–2016 period and exploit the revised Personal Income Tax (PIT) Law of 2011 in China as a quasi-natural experiment to establish the causal effect of the personal income tax on corporate innovation. Using a difference-in-differences identification strategy, we show that a lower personal income tax rate has a significantly positive impact on patent quantity and quality. Further, the revised PIT Law raises the efficiency of R&D activities, induces more explorative innovation, and improves the success rate of patent applications, providing consistent evidence for the intentional effort channel. Moreover, this positive innovation effect is more pronounced in firms with an R&D team that is more sensitive to the salary incentive system, greater innovation dependence, better governance, and firms located in regions with better innovation environments. Taken together, our findings shed light on how inventors and firms respond to decreasing personal income tax rates and confirm that the net return to innovation can be vital to the innovation capacity of firms.