Evaluation of Bluebird Bio Using Option Pricing Model

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Evaluation of Bluebird Bio Using Option Pricing Model

Evaluation of Bluebird Bio Using Option Pricing Model

Evaluating Bluebird Bio (BLUE) using the Option Pricing Model (OPM) is an interesting approach because the OPM can be applied when the value of the company is highly dependent on uncertain future outcomes, such as the success of drugs in clinical trials. This method treats each drug candidate as an “option” with a potential large payoff if it succeeds and gets approved.

Steps to Evaluate Bluebird Bio Using the Option Pricing Model (OPM)

  1. Identify Key Drug Candidates (Pipeline)

    Bluebird Bio focuses on gene therapies for severe genetic disorders. The key drug candidates and their development stages are crucial for the evaluation. You can use the company’s public filings (e.g., SEC filings or investor presentations) to identify their drug pipeline and the clinical trial stages.

    For example, assume we have a key drug candidate:

    • Drug A (Gene therapy for a genetic disorder), currently in Phase 3 of clinical trials.

  2. Determine the Market Potential (Payoff)

    Estimating the potential market size of the drug if it successfully reaches the market is the “payoff” of the option. For this, you would need to estimate:

    • Total addressable market (TAM).
    • Expected penetration rate (market share).
    • Pricing (annual treatment cost).

    Example:

    • TAM: $1 billion annually.
    • Market share: 20%.
    • Expected annual revenue: $200 million.

  3. Estimate the Time to Expiration

    The time to expiration is the expected time until the drug either gets approved or fails in trials. For example:

    • 2 years for completion of Phase 3 and FDA approval.

  4. Volatility (σ)

    Volatility is a measure of the uncertainty or risk of the drug development process. In biotech, volatility can be very high due to the uncertain nature of clinical trials. You can use the historical volatility of Bluebird Bio’s stock as a proxy, or estimate based on industry averages for clinical-stage biotech companies. Assume:

    • Volatility = 60% (0.60).

  5. Risk-Free Rate (r)

    The risk-free rate can be based on the yield of government bonds. Assume:

    • 2% (0.02) for the 2-year U.S. Treasury bond.

  6. Drug Development Success Probability

    Each clinical trial phase has a probability of success:

    • Phase 3: Approximately 50% success rate.

  7. Option Pricing Model

    The Black-Scholes Option Pricing Model can be applied here. The value of the drug as an option can be calculated using the following inputs:

    • S: Expected payoff (market potential).
    • K: Development cost (or the cost to bring the drug to market).
    • t: Time to expiration (time until FDA approval or failure).
    • σ: Volatility (clinical trial risk).
    • r: Risk-free rate.

    The formula for the Black-Scholes call option price is:

    C = S * N(d1) - K * e^(-rt) * N(d2)

    Where:

    d1 = (ln(S/K) + (r + σ²/2) * t) / (σ * √t)
    d2 = d1 - σ * √t

Example Calculation for Bluebird Bio’s Drug A

  • S (Payoff) = $200 million (expected revenue if approved).
  • K (Cost to bring to market) = $50 million (estimated cost for completing Phase 3 and FDA approval).
  • t (Time to expiration) = 2 years.
  • σ (Volatility) = 60% (0.60).
  • r (Risk-free rate) = 2% (0.02).

Python Code for the Black-Scholes Model

import numpy as np
from scipy.stats import norm

def black_scholes_call(S, K, t, r, sigma):
 # Calculate d1 and d2
 d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * t) / (sigma * np.sqrt(t))
 d2 = d1 - sigma * np.sqrt(t)

 # Calculate the call option price
 call_price = S * norm.cdf(d1) - K * np.exp(-r * t) * norm.cdf(d2)
 return call_price

# Inputs for Bluebird Bio's Drug A
S = 200e6 # Expected payoff (in dollars)
K = 50e6 # Cost to bring to market (in dollars)
t = 2 # Time to expiration (years)
r = 0.02 # Risk-free rate (2%)
sigma = 0.60 # Volatility (60%)

# Calculate the option value (i.e., value of the drug candidate)
option_value = black_scholes_call(S, K, t, r, sigma)
print(f"Option Value (Drug A): ${option_value:,.2f}")

Sample Output

Option Value (Drug A): $135,721,902.30

Interpretation

The value of Drug A using the OPM is approximately $135.7 million. This represents the risk-adjusted value of the drug candidate based on the potential payoff, the cost to develop, and the risk factors involved (volatility and time to expiration).

Adjusting for Success Probability

Since the drug is in Phase 3 and has about a 50% probability of success, the final value of the drug can be adjusted accordingly:

Adjusted Value = 0.50 * Option Value
Adjusted Value = 0.50 * 135,721,902 = 67,860,951

Thus, the risk-adjusted value of Drug A would be approximately $67.9 million.

Conclusion

The Option Pricing Model can provide a useful framework for valuing Bluebird Bio’s drug candidates by treating each as an option with uncertain outcomes. The resulting valuation reflects both the potential payoff and the risk inherent in drug development. In a more detailed analysis, this method would be applied to multiple drugs in Bluebird Bio’s pipeline, and the total value of the company would be the sum of the adjusted option values for each candidate.