Product market advertising, while containing little new information, triggers spikes in investor attention. Using weekly advertising data, we find that sell‐side analysts issue optimistic earnings forecasts in response to heavier advertising in the prior week. This effect is not driven by confounding earnings or product news. It is more pronounced for experienced analysts and analysts affiliated with brokerages that rely solely on trading revenues. The optimistic forecast bias intensifies the impact of advertising on investor trades of the underlying stock during the following week, especially on retail buying. Overall, analysts appear to issue optimistic forecasts to exploit retail investor attention spikes induced by advertising.

Using data on the registration of clinical trials and the disclosure of trial results, we examine how firms respond to peer disclosures. We find that firms are less likely to disclose their own trial results if the results of a larger number of closely related trials are disclosed by their peers. This relation is stronger if the firms face higher competition (as measured by the number of competing trials). It is weaker if the firms are further along in their research than the peers (as measured by the trials’ phase) and if the peers’ disclosures convey more negative news (as measured by the firms’ stock price reaction). We also find that firms are more likely to abandon ongoing trials if a larger number of peers disclose the results of closely related trials. Additional tests suggest that this real effects channel does not drive the impact on the firms’ disclosure decisions. Data Availability: Data are available from the public sources cited in the text.

We propose a novel 4/2 rough and smooth stochastic volatility model by combining the rough Heston (rough 1/2) and smooth 3/2 models through a convex specification. This parsimonious two-factor model admits semi-closed-form pricing formulas for equity and volatility index (VIX) derivatives, while capturing key stylized facts documented in empirical studies. The model flexibly generates elasticity of variance estimates consistent with empirical findings from equity markets and produces realistic variance distributions. Although the rough 1/2 component carries a small weight, our numerical experiments confirm a degree of roughness comparable with that obtained with the rough Heston model. Empirical analysis using S&P 500 and VIX option data shows that the model outperforms benchmark specifications both in- and out-of-sample. We further provide insights into how rough volatility modeling influences the estimation of risk-neutral return moments and variance risk premia.