This study examines the role of information transparency in facilitating peer firms' investment in follow‐on innovation. We capture information transparency with both textual and numerical information disclosed in 10‐Ks. Using patent citations to proxy for investment in follow‐on innovation, we predict and find a positive association between transparency at the knowledge source and follow‐on innovation. We further show that the effect of information transparency varies with the degree of uncertainty around technological innovation. Thus, the evidence suggests that information transparency facilitates investment in follow‐on innovation by resolving uncertainty associated with investment in technological innovation. An analysis using the cited firms' going‐private decision as a negative shock to information transparency confirms the significant effect of a cited firm's disclosure on its decision to invest in follow‐on innovation. Our study contributes to the literature on the positive externalities of peer‐firm disclosures and highlights the important role of information transparency in shaping innovation investment decisions.

To determine the welfare implications of price changes in demand data, we introduce a revealed preference relation over prices. We show that the absence of cycles in this relation characterizes a consumer who trades off the utility of consumption against the disutility of expenditure. Our model can be applied whenever a consumer’s demand over a strict subset of all available goods is being analysed; it can also be extended to settings with discrete goods and non-linear prices. To illustrate its use, we apply our model to a single-agent data set and to a data set with repeated cross-sections. We develop a novel test of linear hypotheses on partially identified parameters to estimate the proportion of the population who are revealed better off due to a price change in the latter application. This new technique can be used for non-parametric counterfactual analysis more broadly.

This study examines the relation between CEO tournament incentives, proxied by the difference between CEO pay and the median pay of the senior executives of a given firm, and corporate debt contracting. We find negative relations between CEO pay gap and the cost of debt and default risk, and a positive relation between CEO pay gap and debt maturity. Further analysis indicates that the results are stronger for firms with near-retirement CEOs, which are more likely to run CEO tournaments. Our evidence suggests that creditors view tournament incentives favorably and are willing to provide better debt terms.

This research examines the relation between shareholder litigation and corporate social responsibility (CSR). Exploiting exogenous changes in shareholder litigation rights following the staggered adoption of universal demand laws by U.S. states and the Ninth Circuit Court of Appeals’ ruling on securities class action lawsuits, we show that weaker shareholder litigation rights lead to lower CSR scores. Moreover, the relation is stronger for firms facing higher litigation risk, and a decreased CSR score enhances firm value. Our evidence suggests that firms engage in CSR activities partly to reduce shareholder litigation risk ex ante and mitigate its consequences ex post.

We introduce a real-time measure of conditional biases to firms’ earnings forecasts. The measure is defined as the difference between analysts’ expectations and a statistically optimal unbiased machine-learning benchmark. Analysts’ conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide.

This paper examines how European banks adjusted their lending subsequent to the release of the countercyclical capital buffers (CCyB) during the COVID-19 pandemic. At its onset in 2020Q1, being exposed to a higher ex-ante countercyclical capital buffer led to a reduction in banks' lending. Yet the relief of the CCyBs removed this negative effect from 2020Q2 onwards. We find that the reduction in CCyBs led to a significant relative increase in the average bank's lending by about 5.6 percentage points of their total assets. This increase happened mainly in retail mortgage loans and was stronger for poorly-capitalized banks. These results imply that the release of the CCyBs was effective in promoting bank lending during the pandemic.

We classify asset pricing anomalies into those exacerbating mispricing (build-up anomalies) and those resolving it (resolution anomalies). We estimate the dynamics of price wedges for well-known anomaly portfolios and map them to firm-level mispricings. We find that several prominent anomalies like momentum and profitability further dislocate prices. Multi-factor models designed to eliminate one-month alphas still produce large price wedges. Our estimates yield a novel decomposition of Tobin’s q, revealing that q’s mispricing component has substantial explanatory power for firm investment. Overall, our results suggest that financial intermediaries chasing build-up anomalies negatively affect price efficiency and associated real capital allocation.

We hypothesize that the well-documented negativity bias, the psychological tendency to asymmetrically emphasize negative over positive aspects, can help explain several financial market phenomena: why most individuals hold strongly bearish views of both short- and long-term equity return distributions, why individuals exhibit heterogeneous beliefs, and the stock market participation puzzle. Using variation in the perceived risk of mortality from the swine flu pandemic as our primary proxy for an individual's negativity bias, we find strong support for our hypothesis even when controlling for alternative mechanisms including optimism, risk aversion, ambiguity aversion, and anxiety.

The narrative approach to macroeconomic identification uses qualitative sources, such as newspapers or government records, to provide information that can help establish causal relationships. This paper discusses the requirements for rigorous narrative analysis using fresh research on the impact of monetary policy as the focal application. We read the historical Minutes and Transcripts of Federal Reserve policymaking meetings to identify significant contractionary and expansionary changes in monetary policy not taken in response to current or prospective developments in real activity for the period 1946 to 2016. We find that such monetary shocks have large and significant effects on unemployment, output, and inflation in the expected directions. Analysis of available policy records suggests that a contractionary monetary shock likely occurred in 2022. Based on the empirical estimates of the effect of previous shocks, one would expect substantial negative impacts on real GDP and inflation in 2023 and 2024.