Using firms’ online job postings, we identify economically related peer firms in the labor market. Firms’ labor peers are vastly different from their industry peers, where the overlap is about 20%. Returns of labor-linked firms strongly comove, suggesting common responses to labor market shocks on average. However, industry shocks can affect firms outside the industry through the labor network, leading to substitution effects between labor peers. Last, we show that investors do not promptly incorporate news about labor-linked firms, leading to predictable subsequent returns. A long-short strategy exploiting this delay generates an average annualized excess return of 9%.

Mergers and acquisitions (M&As) are an important way for non-Big 4 accounting firms to grow their businesses. Non-Big 4 firms also account for the vast majority of PCAOB inspections. Consistent with negative inspection reports signaling low quality at inspected firms, we find that non-Big 4 accounting firms conduct fewer M&A deals after they receive negative inspection reports. Additional analyses support our hypothesized signaling mechanism: (1) the chilling effect of inspection reports on M&A activity is stronger when the inspected firm’s business is focused on public company audits, (2) the effect is stronger when stakeholders at the target firm know less about the acquirer because the acquirer is located in a different city, and (3) clients at the target firm switch to new firms rather than move over to the acquirer if the acquirer received a negative inspection report prior to the M&A date.

We find that three factors—cryptocurrency market, size, and momentum—capture the cross‐sectional expected cryptocurrency returns. We consider a comprehensive list of price‐ and market‐related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long‐short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three‐factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.