Stock Trading Algorithms | Future Price vs Option Value
- Rolando Rivera
- May 26
- 3 min read
Conducting comprehensive financial analysis isn't always feasible with a small user interface. I believe it's essential to view multiple data points simultaneously to identify significant correlations.
For instance, I didn't fully understand the contrast between future price and option value until the latest Fintech Trades code updates. The distinction between these two variables became apparent by visualizing more data at the same time.
In this edition, we will examine the differences and correlations of these two Stock Trading Algorithms for Fintech Trades financial data analysis:
Option Value Algorithms
Refers to the price you would pay today for the right (but not the obligation) to buy a share at a future date — it's derived from complex models (e.g., Black-Scholes, LSM, Binomial Tree) that consider:
Current stock price
Strike price
Time to expiration
Volatility
Interest rates
Probability of being in-the-money
Example: A call option for a stock priced at $100 with a strike of $105 might cost $3 today. That $3 is the option value, not the predicted stock price.
📌 Key Point: Option value reflects risk-adjusted potential — it’s a derivative of the underlying asset and accounts for uncertainty, not a direct prediction of the stock’s future price.
Future Price of a Stock Trading Algorithms
This refers to the estimated or projected price of a stock at some point in the future. It’s based on:
Expected return
Market volatility
Time horizon
External economic factors
Example:
Geometric Brownian Motion (GBM) simulates that a stock currently priced at $100 will likely be around $110 in one year, based on certain risk-free rates and volatility.
📌 Key Point: Future stock price is about where the actual share price might end up. It can be used for investment projections, price targets, or valuation models.
Summary of Differences
Concept | Future Stock Price | Option Value (Premium) |
What is it? | Estimate of stock's future value | Cost to buy right to trade at strike price |
Based on | Return, volatility, time | Stock price, strike, time, volatility, rates |
Used for | Forecasting, valuation | Trading derivatives, hedging |
Example Output | $110 in 1 year | $3 call option premium |
Related Model | Geometric Brownian Motion | Black-Scholes, LSM, Binomial |
Common Attributes
While Geometric Brownian Motion (GBM) and Least Squares Monte Carlo (LSM) are used for different purposes in financial modeling, they share several foundational characteristics. Here's a concise breakdown:
Characteristic | Description |
1. Monte Carlo Simulation | Both often rely on Monte Carlo methods to simulate many possible paths for stock prices over time. |
2. Time-Discrete Modeling | Both divide time into small intervals (e.g., days or months) and simulate price evolution step-by-step. |
3. Incorporate Volatility (σ) | Both account for market volatility as a key driver of uncertainty in stock price movement. |
4. Use of Randomness | Both use random number generation (usually standard normal distributions) to model price paths. |
5. Risk-Neutral Framework | Both are typically implemented under a risk-neutral measure, especially for pricing derivatives. |
6. Dependence on Stochastic Processes | GBM is a stochastic process itself, and LSM builds on simulated stochastic paths to estimate values. |
7. Price Path Simulation | Both generate simulated future prices of an asset, which are then used in different ways (e.g., forecasting or pricing). |
🧠 In Essence:
GBM gives you a model of price evolution.
LSM uses simulated paths (often via GBM) and combines them with regression techniques to decide optimal exercise points for American options.
Fintech Trades Scoring Update
Geometric Brownian Motion is effectively utilized in our scoring and selection process. New metrics and assessment updates for Monte Carlo Simulation results are on the way. Stay tuned for announcements.
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