We test the hypothesis that low-visibility shocks to text-based network industry peers can explain industry momentum. We consider industry peer firms identified through 10-K product text and focus on economic peer links that do not share common Standard Industrial Classification (SIC) codes. Shocks to less visible peers generate economically large momentum profits and are stronger than own-firm momentum variables. More visible traditional SIC-based peers generate only small, short-lived momentum profits. Our findings are consistent with momentum profits arising partially from inattention to economic links of less visible industry peers.

We empirically establish that unemployed individuals maintain significant access to credit and that upon a layoff, the unconstrained borrow while the constrained default and delever. Motivated by these findings, we develop a theory of credit lines and labor income risk to analyze optimal transfers to the unemployed. Since credit lines offer fixed interest rates and limits, credit lines are unresponsive to layoffs and provide greater consumption insurance relative to when debt is repriced period by period. At US levels of credit lines, the government can optimally reduce transfers to the unemployed, whereas this is not true when debt is counterfactually repriced period by period.

We provide evidence using firm 10‐Ks that over the past 30 years, U.S. firms have expanded their scope of operations. Increases in scope were achieved largely without increasing traditional operating segments. Scope expansion significantly increases valuation and is realized primarily through acquisitions and investment in R&D, but not through capital expenditures. Traditional concentration ratios do not capture this expansion of scope. Our findings point to a new type of firm that increases scope through related expansion, which is highly valued by the market.

We examine the impact of lost intellectual property protection on innovation, competition, firm performance, and valuation. We consider firms whose ability to protect intellectual property (IP) using patents is weakened following a major Supreme Court decision. We use large language models (LLM) to identify firms’ patent portfolios’ exposure to this decision and find an unequal impact. Large firms gain and small firms lose. Large impacted firms benefit as their sales growth increases and their exposure to lawsuits decreases. Small impacted firms lose as they face increased competition, product-market encroachment, and lower profits and valuations. They increase R&D and nondisclosure agreements.

We examine the determinants of vertical acquisitions using product text linked to product vocabulary from input-output tables. We find that the innovation stage is important in understanding vertical integration. R&D-intensive firms are less likely to become targets of vertical acquisitions. In contrast, firms with patented innovation are more likely to sell to vertically related buyers. Firms’ R&D intensity is a more important deterrent to their vertical acquisitions when the provision of innovation incentives by potential acquirers is more difficult. The role of patents in fostering vertical acquisitions is more prevalent when potential buyers face a higher risk of holdup.

We analyze whether industry competition influences analyst coverage decisions and whether analysts benefit from covering product market competitors. We find that analysts are more likely to cover a firm when this firm competes with more firms already covered by the analyst. We also find that the intensity of competition among these competitors is additionally important to the coverage decision. Moreover, we find that analysts who cover product market competitors are more likely to obtain analyst star status. These results are consistent with the importance to analysts of industry competition and product market knowledge accumulated through covering product market competitors.

To what extent is a worker's human capital growth affected by the quality of his coworkers? To answer this question, we develop and estimate a model in which the productivity and the human capital growth of an individual depend on the average human capital of his coworkers. The measured production function is supermodular: The marginal product of a more knowledgeable individual is increasing in the human capital of his coworkers. The measured human capital accumulation function is convex: An individual's human capital growth is increasing in coworkers' human capital only when paired with more knowledgeable coworkers, but independent of coworkers' human capital when paired with less knowledgeable coworkers. Learning from coworkers accounts for two thirds of the stock of human capital accumulated on the job. Technological changes that increase production supermodularity lead to labor market segregation and, by reducing the opportunities for low human capital workers to learn from better coworkers, lead to a decline in aggregate human capital and output.