Conference Agenda

We document new asset pricing stylized facts implied by zero days-to-expiration (0DTE) options, which now comprise half of total S\&P 500 option volume, and contrast them to those of longer-maturity contracts. A distinctive feature of the 0DTE market is that investors require more compensation for positive market returns than for negative returns. This is reflected in a high variance risk premium, which is mainly driven by compensation for upside risk and negatively predicts market returns. Moreover, the majority of 0DTEs violates price bounds associated with risk-averse investors. A trading strategy exploiting these violations is highly profitable up to 2022, but dissipates after the daily availability of 0DTEs, consistent with growing integration with the underlying market in recent years.

We construct a high-frequency measure of earnings expectations using financial prediction markets. Unlike analyst forecasts, which are updated infrequently and prone to agency conflicts, prediction market prices reflect real-time, stake-backed beliefs. Each contract pays one dollar if realized earnings exceed the analyst consensus; we derive the conditions under which these prices represent subjective probabilities and introduce a methodology to convert them into implied expectations about earnings. Relative to analyst forecasts, we find that market-implied expectations are (i) more accurate; (ii) incrementally informative for earnings announcement returns; (iii) significantly less biased, though they exhibit short-term overreaction in contrast to the underreaction typical of analysts; and (iv) lead in price discovery. We expect this measure to become increasingly informative as the market matures, liquidity deepens, and financial prediction markets become part of mainstream finance.

How should investors value firms without return histories? In practice, investors typically proxy the cost of equity in discounted cash flow valuations using peer-based betas. Using IPOs as a natural laboratory, I show that disclosed business risks provide an informative basis for beta estimation. I introduce Aggregated Cluster Embeddings (ACE), a context-aware framework that encodes disclosed business risks into economically meaningful numerical representations. Using these representations as inputs to machine learning models, I obtain beta estimates that are up to 31 percent more accurate than peer-based benchmarks. Despite this improvement, investors do not appear to fully incorporate the information contained in disclosed business risks at issuance. Portfolios formed on predictions derived from ACE representations exhibit cross-sectional return predictability. The resulting long–short portfolio earns a significant six-factor alpha of 101 basis points per month during the first year after the IPO. These abnormal returns vanish as return histories accumulate, consistent with markets gradually learning firms’ true systematic risk exposures.