Revisiting Boehmer et al. (2021): Recent Period, Alternative Method, Different Conclusions

ArXiv ID: 2403.17095 “View on arXiv”

Authors: Unknown

Abstract

We reassess Boehmer et al. (2021, BJZZ)’s seminal work on the predictive power of retail order imbalance (ROI) for future stock returns. First, we replicate their 2010-2015 analysis in the more recent 2016-2021 period. We find that the ROI’s predictive power weakens significantly. Specifically, past ROI can no longer predict weekly returns on large-cap stocks, and the long-short strategy based on past ROI is no longer profitable. Second, we analyze the effect of using the alternative quote midpoint (QMP) method to identify and sign retail trades on their main conclusions. While the results based on the QMP method align with BJZZ’s findings in 2010-2015, the two methods provide different conclusions in 2016-2021. Our study shows that BJZZ’s original findings are sensitive to the sample period and the approach to identify ROIs.

Keywords: Retail Order Imbalance, Return Prediction, Replication Study, Market Microstructure, Statistical Significance, Equities

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on empirical replication and robustness checks using real financial data, with moderate statistical analysis but no advanced mathematical derivations. It is highly data-driven with clear backtesting elements (e.g., long-short strategy profitability assessment), making it practical for implementation.

  flowchart TD
    A["Research Goal<br>Replicate & Extend Boehmer et al.<br>2021: ROI Predictive Power"] --> B
    
    subgraph B ["Methodology & Data"]
        direction LR
        B1["Dataset 1: 2010-2015<br>(Original Period)"] --> B2["Dataset 2: 2016-2021<br>(Recent Period)"]
        B1 --> B3["Method A: Original Method"]
        B1 --> B4["Method B: QMP Method"]
    end

    B --> C{"Computational Analysis"}

    C --> D["Regression Analysis<br>ROI vs. Future Returns"]
    C --> E["Backtesting Strategy<br>Long-Short on ROI quintiles"]

    D --> F{"Outcomes"}
    E --> F

    F --> G["2010-2015: Significant<br>Predictive Power"]
    F --> H["2016-2021: Weakens Significantly<br>Large Caps: Insignificant<br>Profitability: Neutralized"]