Cross-posted and expanded from LinkedIn Pulse.
Markets are the ultimate validators of structural changes. From late May through mid-October 2025, I tested a thesis on the weaponization of rare earths (REMX)—the material backbone of defense, semiconductors, and electrification.
The hypothesis was simple: China’s tightening export controls and Western re-shoring mandates would reprice resource-sovereignty risk much faster than the market makers’ backward-looking models expected.
To express that view, I built a ladder of call options, buying cheap convexity in an under-theorized geopolitical theme. Through several disciplined rolls, early profits financed subsequent positions, compounding into a 2,904% (29×) return on the initial capital at risk. What began as a sub-$150 experiment became a self-funding proof of concept for structural forecasting.
| Date | Option Contract | Money Out (Buy) | Money In (Sell) | Cash Balance |
|---|---|---|---|---|
| — | — | — | — | $137.36 |
| 5/30/2025 | REMX250620C37 | -$137.36 | — | $0.00 |
| 6/5/2025 | REMX250620C37 | — | $494.64 | $494.64 |
| 6/5/2025 | REMX250718C41 | -$157.36 | — | $337.28 |
| 6/5/2025 | REMX250815C44 | -$45.68 | — | $291.60 |
| 6/18/2025 | REMX250718C41 | — | $148.64 | $440.24 |
| 6/18/2025 | REMX250815C44 | — | $69.32 | $509.56 |
| 6/18/2025 | REMX251121C46 | -$211.36 | — | $298.20 |
| 6/18/2025 | REMX260220C47 | -$167.68 | — | $130.52 |
| 7/10/2025 | REMX251121C46 | — | $596.65 | $727.17 |
| 7/10/2025 | REMX260220C47 | — | $296.32 | $1,023.49 |
| 7/10/2025 | REMX260220C55 | -$477.03 | — | $546.46 |
| 7/21/2025 | REMX260220C55 | — | $1,065.97 | $1,612.43 |
| 7/21/2025 | REMX260220C65 | -$482.03 | — | $1,130.40 |
| 8/19/2025 | REMX260220C65 | — | $797.98 | $1,928.38 |
| 8/19/2025 | REMX260220C75 | -$397.02 | — | $1,531.36 |
| 10/2/2025 | REMX260220C75 | — | $1,146.99 | $2,678.35 |
| 10/2/2025 | REMX260515C90 | -$791.01 | — | $1,887.34 |
| 10/13/2025 | REMX260515C90 | — | $2,238.99 | $4,126.33 |
The Diagnostic: Why the Counterparty Was Wrong
The trade’s success was diagnostic. It confirmed a recurring pattern: when volatility stems from a policy regime change, correlation-driven models systematically lag reality.
Market-maker algorithms, calibrated for statistical noise, cannot price strategic intent. They hedge as if governments behave like Gaussians. As a result, they consistently sell volatility too cheaply—just as legacy institutions optimize for equilibrium in systems already undergoing phase transition.
This is the same pathology seen in data analytics and business forecasting: a dependence on historical (co)variance to model fundamentally shifting market conditions.
From Forecasting to Execution
When a thesis converts into an asymmetric payoff, it demonstrates more than market timing. It validates a causal framework for how power, technology, and risk interact. It also proves that structural modeling is not merely theoretical but can be operationalized for effect.
The same causal logic informs strategic advisory and adaptation:
- Diagnose Regime Shifts Early. Detect technological, organizational, and financial phase transitions before they are visible in lagging data.
- Quantify the Mispricing. Measure the delta between the consensus model (backward-looking, correlation-based) and the emerging structural reality.
- Exploit Convexity. Design capital, policy, and operational strategies that convert uncertainty into asymmetry, where disciplined exposure to the right tail compounds advantage rather than risk.
Many firms face the same asymmetry: slow recognition of structural changes creates exploitable inefficiency. However, unlike traders, executives cannot hedge. They must adapt quickly—or risk being repriced by the real world.