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Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps

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  • Ravil Akhtyamov

Abstract

A defined-benefit pension is only as safe as its sponsor, yet sponsor distress is under-instrumented relative to plan-side modelling. We present a transparent, reproducible early-warning pipeline built entirely on public data. A Sponsor Vulnerability Index (SVI), a gradient-boosted classifier over point-in-time SEC EDGAR balance-sheet fundamentals and two macro series, predicts a filings-based distress state one to two quarters ahead. On 36 U.S. sponsors in five sector groups (1,418 firm-quarters, 2015Q1-2025Q3) it reaches an out-of-fold AUC-ROC of 0.887 and PR-AUC of 0.711 (base rate 11.9%) under leave-one-firm-out cross-validation. A Monte Carlo VaR with a stylized transition-stress overlay shows that climate-type stress is negligible for a diversified plan but adds 20-43% to one-year 95% VaR when 22.5% of assets are held in sponsor securities. A dynamic self-investment cap maps the SVI to a limit that falls from 8% to below 1% in acute distress and recovers with the sponsor, which we illustrate on PG&E's 2019 bankruptcy and Boeing's persistent fragility. We report the method's failure modes alongside its results (book-equity labels that also fire on non-credit events such as spin-offs, positives concentrated in one firm, and stylized stress parameters) and release code, data and results under a concept DOI.

Suggested Citation

  • Ravil Akhtyamov, 2026. "Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps," Papers 2610.14120, arXiv.org.
    • Handle: RePEc:arx:papers:2610.14120
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    1. Stefano Battiston & Antoine Mandel & Irene Monasterolo & Franziska Schütze & Gabriele Visentin, 2017. "A climate stress-test of the financial system," Nature Climate Change, Nature, vol. 7(4), pages 283-288, April.
    2. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    3. Shlomo Benartzi, 2001. "Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock," Journal of Finance, American Finance Association, vol. 56(5), pages 1747-1764, October.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Bolton, Patrick & Kacperczyk, Marcin, 2021. "Do investors care about carbon risk?," Journal of Financial Economics, Elsevier, vol. 142(2), pages 517-549.
    6. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    7. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    8. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    9. Sharpe, William F., 1976. "Corporate pension funding policy," Journal of Financial Economics, Elsevier, vol. 3(3), pages 183-193, June.
    10. Joshua D. Rauh, 2009. "Risk Shifting versus Risk Management: Investment Policy in Corporate Pension Plans," The Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2487-2533, July.
    11. Martellini, Lionel & Milhau, Vincent, 2012. "Dynamic allocation decisions in the presence of funding ratio constraints," Journal of Pension Economics and Finance, Cambridge University Press, vol. 11(4), pages 549-580, October.
    12. Joshua D. Rauh, 2006. "Investment and Financing Constraints: Evidence from the Funding of Corporate Pension Plans," Journal of Finance, American Finance Association, vol. 61(1), pages 33-71, February.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
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