We study the dynamics of information demand and supply in capital markets, focusing on how firms’ disclosures align with analysts’ information needs. Using a novel dataset from Visible Alpha, we analyze granular data from analysts’ forecasting models to understand the breadth of information they seek and how firms meet these demands through mandatory and voluntary disclosures. We document significant variation in the complexity of analysts’ models and the extent of firms’ disclosures, leading to some items in analysts’ models remaining undisclosed. This unmet information demand gives rise to a novel concept we term “uncertainty about the past” ( UP ). We investigate its implications for key capital market outcomes, including analyst forecast dispersion, market reactions to earnings announcements, and stock market liquidity. Our results demonstrate that UP plays a significant role in shaping the information environment, challenging the assumption that earnings announcements fully resolve uncertainty about past performance.

Financial analysts closely follow a firm’s operations and assess the risks that it faces. In this paper, we examine whether analysts incorporate ESG risks into their stock recommendations and target prices. Specifically, we use a unique firm-day level dataset on negative ESG risk incidents to proxy for unobservable risk assessments of analysts. We find that analyst outputs predict future ESG incidents, suggesting that analysts incorporate ESG risks into their models. Our results are robust to controlling for ESG incidents that firms experienced in the past, and are stronger in more transparent information environments, and in the presence of more guidance on ESG issues from the Sustainability Accounting Standards Board. Importantly, we find that analysts incorporate ESG risks through adjusting discount rates rather than cash flow estimates. Overall, our results highlight the ability of financial analysts to synthesize and integrate ESG risks into their research.