We examine the consequences of real earnings management from an innovation perspective and investigate the patent output of firms likely to be managing earnings through altering their R&D expenditures. We find that R&D cuts related to earnings management lead to fewer patents, less influential patent output, and lower innovative efficiency compared to other R&D cuts. Our results thus suggest that real earnings management may obstruct firms’ technological progress and highlight the potential costs of managerial manipulation of R&D expenditures to alter reported earnings.

We compare innovation strategies of public and private firms based on a large sample over the period 1997–2008. We find that public firms’ patents rely more on existing knowledge, are more exploitative, and are less likely in new technology classes, while private firms’ patents are broader in scope and more exploratory. We investigate whether these strategies are due to differences in firm information environments, CEO risk preferences, firm life cycles, corporate acquisition policies, or investment horizons between these two groups of firms. Our evidence suggests that the shorter investment horizon associated with public equity markets is a key explanatory factor.

We propose that innovative originality is a valuable organizational resource and that owing to limited investor attention and skepticism of complexity, greater innovative originality may be undervalued. We find that firms' innovative originality strongly predicts higher, more persistent, and less volatile profitability and higher abnormal stock returns, findings that are robust to extensive controls. The return predictive power of innovative originality is stronger for firms with higher valuation uncertainty, lower investor attention, and greater sensitivity of future profitability to innovative originality. This evidence suggests that innovative originality acts as a "competitive moat" and is undervalued by the market.

Using a series of advanced tests from White's (2000) “Reality Check” to correct for data-snooping bias, we assess the out-of-sample performance of various portfolio strategies relative to the naive 1/N rule. When we analyze 16 basic portfolio strategies, 126 learning strategies, and nearly 2,000 extended strategies, we find that some strategies outperform the 1/N rule in conventional tests that do not account for data-snooping bias. However, after we use the new tests that control for such bias, we find that none or very few of these strategies outperform the 1/N rule. Thus, our finding underscores the necessity to control for data-snooping bias when making asset allocation decisions.

In this paper, we empirically measure the impact of natural disasters on firm-level operating performance and examine if such impact can be mitigated by technology diversification. Using major natural disasters specified by Barrot and Sauvagnat (2015) and factory location data from the toxic release inventory (TRI) database, we first find that firms with factories located in states affected by natural disasters are much less profitable. Second, we find that firms with diversified technologies are significantly less subject to the impact of natural disasters, suggesting that technology diversity enhances firms’ sustainability.