Knowledge that Transforms

Using manually compiled cost of equity (COE) estimates disclosed in takeover regulatory filings, we provide novel evidence on how investment bankers estimate discount rates. COE estimates are related to several risk proxies, such as beta and size. Other firm characteristics are unrelated to COE estimates or provide relations contradicting academic evidence. We also explore the role of incentives. For example, banks use significantly higher COEs in management buyouts, which potentially underestimates target value, making the bid more attractive for target shareholder approval.

We examine whether location within a dense regional cluster of interconnected businesses affected firm performance during the Great Recession and the subsequent recovery. Firms in denser regional clusters experienced faster sales growth than their rivals in less dense clusters, especially firms operating in more competitive industries and those more able to reap agglomeration benefits. They also faced lower uncertainty, invested more in both physical capital and intangible capital, and maintained higher employment growth. Their greater resiliency and agility led to significant increases in their valuations. These results suggest that regional clusters provide competitive advantages during turbulent times.

One-third of deals in the venture capital (VC) market involve a founder and investor from the same university. Venture capitalists are more likely to invest in and place larger bets on startups with founders from their alma mater. These deals are also more likely to lead to IPOs postfunding. Tests using VC partner turnover confirm a direct link between education ties and funding likelihood. Taken together, our results suggest that university connections facilitate improved deal-making and outcomes, rather than diverting funds toward lower-quality startups.

Consistent with theories on the equilibrium matching between capital structure and employee job risk aversion, we find a robust, positive association between a firm’s leverage and its employees’ family labor income diversification. Higher-Leverage firms also recruit new employees with greater income diversification. For identification, we exploit two policy shocks that exogenously change employee income diversification and firm leverage, respectively. Individual employee-level tests further reveal that workers with differential risk attitudes adjust their job choices and household labor income portfolios in response to significant shifts in their employers’ leverage. Finally, human bankruptcy costs contribute to the general level of corporate risk-taking.

Order flow segmentation prevents direct interactions between U.S. retail and institutional investors. Using the imbalance in observable internalized retail trades, we show wholesalers use retail flow to provide liquidity to institutional investors, especially when liquidity is scarce. Our institutional liquidity cost ( $ ILC $ ) measures average absolute retail trade imbalances, positing that institutions holding stocks with greater such averages more often resort to the expensive wholesaler-provided liquidity. $ ILC $ is correlated with expected institutional price impacts. Unlike existing illiquidity measures, $ ILC $ has economically meaningful relations with institutional holding horizons and yields annualized liquidity premia of 2.7%–3.2% post-2010, even after excluding microcap stocks.

This paper documents that 56% of nonprofessional social media investment analysts (SMAs) are skilled and declare beliefs that generate positive abnormal returns (ABRs), while 44% produce negative ABRs. 13% of all SMAs are high-skill type and produce a 1-week 3-factor alpha of 61 bps, while the remaining 87% generate only 6 bps. The distinctive features of high-skill SMAs are primarily firm and industry specializations. Although SMAs tend to extrapolate and herd, their expectations are not systematically wrong. For higher-skilled SMAs compared to the less-skilled ones, extrapolation fades more quickly, and herding is lower, consistent with theory.

We document significant increases in the suspension of ongoing drug projects following the passage of the Food and Drug Administration Amendments Act of 2007 (FDAAA), which mandates that pharmaceutical companies publicly disclose detailed clinical study results. Our results suggest a causal interpretation through difference-in-differences analyses that exploit variations in pre-FDAAA information environments. We also show evidence that fewer new projects are initiated after the FDAAA. Drug developers’ learning from peer failures is the primary mechanism, further amplified by financial constraints. We also examine the consequences of enhanced information disclosure, including changes in firm investment efficiency, drug quality, and disease morbidity.

We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.

This study examines whether a simple measure of internet search intensity for publicly traded retail firms can enhance the capital market's firm‐level revenue expectations and provide insights into economy‐wide retail sales. At the firm level, the search index is predictive of analyst nowcast and forecast errors after controlling for past sales, deferred revenue, firm characteristics, and firm and time fixed effects. An implementable trading strategy generates abnormal returns of roughly 2% to 3% from the fiscal quarter end through the earnings announcement, well above transaction costs. We also find that approximately two‐thirds of the abnormal returns occur around earnings announcements, with an even greater fraction for firms with coarser information environments. At the macro level, we find that the permanent, seasonal, and transitory components of our search intensity index align with those of the Census Bureau's retail sales data and US real gross domestic product, suggesting our measure is a leading indicator of personal consumption expenditures, a key driver of aggregate output. The aggregated search index nowcasts aggregated publicly traded retail firm sales both within and out‐of‐sample after controlling for past sales.

We present a case illustrating the pitfalls of insufficient disclosure of commercial databases' coding policies. We replicate the finding in the literature that a nontrivial percentage of firms mandated to adopt IFRS ignore this obligation. Specifically, Pownall and Wieczynska (2018, Contemporary Accounting Research , 35 (2), 1029–1066) report more than 3,000 cases, or 10% of all mandated firms in the European Union. When using primary data sources (applicable local regulations and firms' annual reports), we find that noncompliance with IFRS adoption is nonexistent in the one‐to‐one replication using the same firm‐year observations. We attribute the prior misperception to the commercial database's insufficient disclosure of a misleading coding policy of the consolidation item. We also show that no other data provider correctly captures consolidation status, which determines whether firms must report under IFRS. In response to this gap, we showcase the application of bidirectional encoder representations from transformers (BERT) models for extracting the consolidation status and offer guidance for coding IFRS‐mandated firms. Our article underscores the need to exercise caution when using secondary data sources.