Paper: arXiv 2509.05922

Authors: Peilin Rao, Randall R. Rojas

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced causal machine learning (DML, APE) with high-dimensional data, justifying a high math score, while its detailed backtesting, extensive data sourcing, and implementation-focused pipeline indicate strong empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Identify Causal Drivers<br>of Market Troughs"] --> B["Data: High-Frequency Market Data<br>Options Implied Volatility<br>Liquidity Indicators"]
  
  B --> C{"Methodology: Causal ML"}
  C --> D["Computational Process 1<br>Flexible DML Framework"]
  C --> E["Computational Process 2<br>Nowcasting Capitulation Events"]
  
  D --> F["Key Findings: Robust Causal Estimates"]
  E --> F
  
  F --> G["Outcomes"]
  G --> H["1. Volatility of Options-Implied<br>Risk Appetite is a Key Driver"]
  G --> I["2. Market Liquidity is a Key Driver"]
  G --> J["3. Misrepresented by<br>Simpler Linear Models"]
  G --> K["4. Support for<br>Intermediary Asset Pricing Theories"]

Cited References (25)

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  2. Modeling and Forecasting Realized Volatility (2003) — link
  3. Value and Momentum Everywhere (2013) — link
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  7. Expected stock returns and variance risk premia (2009) — link
  8. Market Liquidity and Funding Liquidity (2009) — link
and 17 more
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  3. Double/debiased machine learning for treatment and structural parameters (2018) — link
  4. Long Story Short: Omitted Variable Bias in Causal Machine Learning (2022) — link
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  8. Taming the Factor Zoo: A Test of New Factors (2020) — link
  9. The Leverage Cycle (2010) — link
  10. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction (2008) — link
  11. Empirical Asset Pricing via Machine Learning (2020) — link
  12. Intermediary Asset Pricing (2013) — link
  13. Credit Cycles (1997) — link
  14. Consumption, Aggregate Wealth, and Expected Stock Returns (2001) — link
  15. A Unified Approach to Interpreting Model Predictions (2017)
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  17. The Investor Fear Gauge (2000) — link

0 of 25 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.