Application of Probability Analysis and Monte Carlo Simulations to Evaluate LUNR’s NASA Contract Investment Opportunities

Event-driven investments like LUNR’s NASA LTV contract bid are characterized by binary, high-impact outcomes (win/lose) with limited historical data, making traditional valuation metrics less effective. In this case, the trader applied probability analysis rooted in expected value (EV) to assess the investment’s statistical merit [0]. EV calculation requires estimating three critical variables: the probability of winning the contract (p), the potential percentage gain (g) if successful, and the potential percentage loss (l) if unsuccessful. For example, a 20% contract success probability, 500% option gain, and 80% loss would yield an EV of (0.2500%) - (0.880%) = 36%, indicating a positive statistical edge.

Monte Carlo simulations enhance this analysis by incorporating multiple uncertainties influencing the outcome, such as competition intensity, NASA’s evaluation weightings, contract scope adjustments, and market reaction variability. By running thousands of iterations with random values for each variable (based on estimated ranges), Monte Carlo generates a distribution of possible outcomes, quantifying the likelihood of different gain/loss levels [0]. This addresses the limitations of single-point EV estimates by accounting for real-world variability.

1. Edge quantification over outcome prediction
   : Unlike traditional investing, event-driven probability analysis prioritizes identifying investments with positive expected value over multiple trials, similar to poker strategy [0].
2. Monte Carlo mitigates uncertainty
   : For complex events like NASA contracts, where multiple variables interact, Monte Carlo provides a more robust risk assessment than static EV calculations by mapping outcome ranges and their probabilities.
3. Risk management is non-negotiable
   : Even with a positive EV, high volatility requires strict controls (e.g., position sizing, stop-loss orders) to avoid catastrophic losses from single unsuccessful outcomes.

• Mispriced options
  : If the market underestimates LUNR’s contract win probability, undervalued options present high-reward opportunities [0].
• Method scalability
  : The same framework applies to other event-driven opportunities (regulatory approvals, mergers, large contract bids).

• Estimation error
  : Inaccurate assessments of contract probability, price movements, or variable ranges can invalidate results.
• Market efficiency
  : Pricing corrections may reduce profitable entry windows once probability signals become public [0].
• Black swan events
  : Unmodeled developments (NASA delays, competitor breakthroughs) can disrupt expected outcomes.

Probability analysis and Monte Carlo simulations provide a structured framework for high-risk, event-driven investments like LUNR’s NASA contract bid. These methods quantify statistical edge rather than predict specific outcomes, aligning with binary contract win/lose dynamics. The trader’s 800% gain illustrates potential when supported by accurate estimation and risk management. However, investors must remain cautious of estimation errors, market inefficiencies, and unmodeled variables that can impact results.