A methodology & validation note documenting a live Zyberno tool. This is not part of the Brina Gap framework research; it is supporting technical work.
The Zyberno Market Valuation Score: Construction, Backtest, and the Limits of Optimization
Abstract. We document the construction and historical behaviour of the Zyberno Market Valuation Score, a single 0–100 reading that blends four widely-followed macro gauges — the Buffett Indicator and Shiller PE (valuation), and the yield curve and credit spread (recession risk). Rebuilt monthly from 1986 to 2026 across four U.S. recessions, the score is strongly inversely related to subsequent 10-year S&P 500 returns (r = −0.78) but is not a reliable recession-timing tool — it was low before both the 1990 and 2008 downturns. We further show that optimizing the component weights overfits: the in-sample-optimal and least-squares-optimal weightings both fail out-of-sample, while a simple, robustness-validated weighting (20/40/30/10) generalises. Adding a market-sentiment signal adds no predictive power. We therefore present the score as a transparent long-run valuation gauge — useful for setting return expectations, not for market timing — and publish it live with full methodology.
1. Motivation
Individual valuation gauges are familiar but noisy, and each tells only part of the story. The Buffett Indicator and Shiller PE say how expensive equities are; the yield curve and credit spread say whether the financial system is flashing stress. Our aim is modest and explicit: combine them into one transparent number, then ask honestly what that number has — and has not — been able to do. In an environment where generic market commentary is increasingly automated, a gauge earns trust through reproducible construction and candid validation, not through a confident headline.
2. Components and Data
The score uses four monthly series. The common window begins in 1986, set by the shortest input (the Baa credit spread). Quarterly inputs are carried forward as-of (no look-ahead). Recession dates are the NBER business-cycle reference dates (FRED series USREC); forward returns use the S&P 500 monthly price.
| Gauge | Definition | Source | History |
|---|---|---|---|
| Buffett Indicator | Total market cap ÷ GDP | FRED NCBEILQ027S ÷ GDP | 1951– |
| Shiller PE (CAPE) | 10-yr cyclically-adjusted P/E | Shiller / multpl.com | 1871– |
| Yield curve | 10-yr minus 2-yr Treasury | FRED T10Y2Y | 1976– |
| Credit spread | Moody's Baa minus 10-yr Treasury | FRED BAA10Y | 1986– |
3. Construction
Each gauge is mapped to a 0–100 sub-score by where it sits in its historical range (0 = cheap or calm, 100 = extreme): the Buffett Indicator over 75–200%, the Shiller PE over 10–40, the yield curve over +1.5% to −1.0%, and the credit spread over 1.5–5.0%. These thresholds are set by economic judgment and held fixed — they are not fitted to outcomes. The composite is a weighted average, kept to a 60% valuation / 40% recession-risk split. The weights are the one element we examine empirically in §5.3.
4. Backtest methodology
We compute the score at each month-end from 1986 to 2026 (485 observations) and study three things: (i) its level in the run-up to each NBER recession; (ii) its correlation with subsequent S&P 500 price returns at 1-, 3-, 5- and 10-year horizons; and (iii) whether re-weighting the components improves prediction out-of-sample (train 1986–2005, test 2006–2026). Return windows overlap, so significance is overstated and we read magnitudes as indicative; returns are price-only.
5. Results
5.1 It is not a recession timer
The score gave a clear warning before the 2001 dot-com bust — but was low before both the 1990 recession and the 2008 global financial crisis, when valuations were only middling and credit was still calm.
| Recession (NBER peak) | Score 12m before | Score at onset | Peak (prior 24m) | Warned? |
|---|---|---|---|---|
| Jul 1990 | 31 | 17 | 31 | No |
| Mar 2001 | 78 | 46 | 80 | Yes |
| Dec 2007 | 49 | 29 | 50 | No |
| Feb 2020 | 57 | 48 | 59 | Partial (pandemic) |
The recession signal lives almost entirely in one component: the yield curve, which inverted roughly 19, 34 and 25 months before the 1990, 2001 and 2008 recessions (and not before the 2020 pandemic shock). We therefore show the yield curve as its own gauge and do not market the composite as a recession predictor.
5.2 It predicts long-run returns
What the score is good at is exactly what a valuation gauge should do — set expectations for the next decade.
| Horizon | corr(score, forward annualised return) |
|---|---|
| 1 year | −0.07 |
| 3 years | −0.37 |
| 5 years | −0.52 |
| 10 years | −0.78 |
By component, the 10-year signal is carried by valuation — Shiller (−0.88) and Buffett (−0.54) — with a modest contribution from the yield curve (−0.33) and essentially none from the credit spread (+0.09), which is a coincident stress gauge rather than a return predictor. Splitting history at the median score, the top half of readings preceded about +5.5%/yr over the next decade, versus +10.8%/yr from the bottom half — roughly half the return from an expensive starting point.
5.3 Optimizing the weights overfits
It is tempting to fit the weights for maximum predictive power. We did — exhaustively — and the result is a cautionary tale. The in-sample-optimal weighting collapses onto ~100% Shiller (r = −0.88 in-sample) but, fit on 1986–2005 and tested on 2006–2026, its out-of-sample correlation falls to +0.07. An unconstrained least-squares fit — the best possible linear combination over all weights and signs — scores a flattering +0.91 in-sample and then −0.28 out-of-sample, i.e. actively wrong on unseen data. The naïve balanced weighting generalised better (−0.41) than either "optimised" version.
5.4 Sentiment adds nothing
We tested whether a market-sentiment signal (a price-momentum and volatility proxy for a Fear & Greed-style index, since real Fear & Greed history spans only ~2 years) improves the score. Its correlation with forward returns ranged from −0.06 to −0.13, and the optimizer assigned it zero weight. Sentiment is a days-to-weeks timing signal, not a decade-ahead valuation signal, so it stays out of the composite and is shown separately on the live dashboard.
6. The score through history
7. Limitations
We state these plainly. (1) Forward-return windows overlap, so the correlations are autocorrelated and their statistical significance is overstated; we treat magnitudes as indicative. (2) The window contains only four recessions — low power for any recession claim. (3) Returns are price-only; total returns would be ~2%/yr higher across the board, leaving the relationship intact. (4) Sub-score thresholds use the full historical range, a mild look-ahead in levels (not in the weight tests, which are split out-of-sample). (5) The sentiment input is a proxy, not the live Fear & Greed index. (6) The analysis is U.S.-only and uses a single train/test split. We deliberately did not fit nonlinear, interaction or regime-switching terms: the linear out-of-sample failures above show such flexibility would overfit, not generalise.
8. Conclusion
The Zyberno Market Valuation Score is best understood as a transparent, long-run valuation gauge. Its honest job is expectation-setting: when it is high, the next decade has historically paid roughly half what it paid from low readings. It is not a market-timing or recession-prediction device, and we do not present it as one. At the time of writing the score sits near the top of its 40-year range, which the historical relationship associates with below-average forward returns — a sober reading, offered with its uncertainty attached. Future work will ask whether this macro regime conditions the stock-level signal of the Brina Gap framework.
References
- Buffett, W. & Loomis, C. (2001). Warren Buffett on the Stock Market. Fortune.
- Campbell, J. Y. & Shiller, R. J. (1988). Stock Prices, Earnings, and Expected Dividends. Journal of Finance, 43(3).
- Estrella, A. & Mishkin, F. S. (1998). Predicting U.S. Recessions: Financial Variables as Leading Indicators. Review of Economics and Statistics, 80(1).
- Gilchrist, S. & Zakrajšek, E. (2012). Credit Spreads and Business Cycle Fluctuations. American Economic Review, 102(4).
- Harvey, C. R., Liu, Y. & Zhu, H. (2016). …and the Cross-Section of Expected Returns. Review of Financial Studies, 29(1).
- McLean, R. D. & Pontiff, J. (2016). Does Academic Research Destroy Stock Return Predictability? Journal of Finance, 71(1).
- Shiller, R. J. (2015). Irrational Exuberance (3rd ed.). Princeton University Press.
- Data: Federal Reserve Economic Data (FRED), Robert Shiller's online dataset, and NBER U.S. business-cycle reference dates.