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Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation

Peilin Rao, Randall R. Rojas

arXiv 7 Sep 2025 · Finance — Statistical Finance

arXiv:2509.05922 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

25
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appendix boundary found by appendix_command · 74% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Cinelli and Hazlett (2020) Making Sense of Sensitivity: Extending Omitted Variable Bias0.7946350%
2Bry and Boschan (1971)0.73732100%
3He and Krishnamurthy (2013) Intermediary Asset Pricing0.73732100%
4Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey and Robins (2018) Double/debiased machine learning for treatment and structural parameters0.64422100%
5Lundberg and Lee (2017) A Unified Approach to Interpreting Model Predictions0.64422100%
6Andersen, Bollerslev, Diebold and Labys (2003) Modeling and Forecasting Realized Volatility0.58531100%
7Asness, Moskowitz and Pedersen (2013) Value and Momentum Everywhere0.51121100%
8Bakshi, Kapadia and Madan (2003) Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Option Contracts0.51121100%
9Bernanke, Gertler and Gilchrist (1999) The Financial Accelerator in a Quantitative Business Cycle Framework0.51121100%
10Gu, Kelly and Xiu (2020) Empirical Asset Pricing via Machine Learning0.51121100%

Showing the top 10 of 25 scored citations.