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📄 Abstract
Abstract: This paper provides robust, new evidence on the causal drivers of market
troughs. We demonstrate that conclusions about these triggers are critically
sensitive to model specification, moving beyond restrictive linear models with
a flexible DML average partial effect causal machine learning framework. Our
robust estimates identify the volatility of options-implied risk appetite and
market liquidity as key causal drivers, relationships misrepresented or
obscured by simpler models. These findings provide high-frequency empirical
support for intermediary asset pricing theories. This causal analysis is
enabled by a high-performance nowcasting model that accurately identifies
capitulation events in real-time.