Monte Carlo Simulation in Stock Trading — Myth or Mirage?

In the world of trading, randomness is real, but it doesn’t play by the rules of mathematics.

Monte Carlo simulation has always carried an air of mathematical sophistication. It is a computational tool capable of simulating uncertainty, predicting outcomes, and quantifying risk. Many traders and analysts talk about it as if it were a crystal ball for financial markets. But is it really that effective? Today, we’ll take a deep dive into Monte Carlo simulation in the context of stock trading through a simple code example and a real-world case study to see whether it lives up to its reputation.

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The Illusion of Predictive Power

Let’s start with a simple image: a curve-fitting graph. Why begin there? Because it captures the essence of what’s wrong with much of the data science hype surrounding market prediction. Many online tutorials promise that machine learning methods like decision trees, random forests, neural networks can “predict” stock prices. The reality is far less glamorous.

In practice, most strategies that rely on such predictive models fail when tested in live markets. Some quantitative analysts estimate that the real success rate of machine learning-based trading strategies is no higher than 10%. The truth is that financial markets are not like clean, well-behaved datasets. They are messy, dynamic, and full of “unknown unknowns.”

Reference: https://xkcd.com/2048/

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What Exactly Is Monte Carlo Simulation?

The Monte Carlo method takes its name from the famous gambling district in Monaco. It’s a computational technique that uses random sampling to model uncertainty and study systems governed by probability.

In finance, Monte Carlo simulation is often used to estimate potential future prices of an asset by assuming a stochastic process, most commonly the Wiener process (or Brownian motion). By generating thousands of possible price paths, it aims to show the range of potential outcomes.

Conceptually, this sounds solid. But there’s a major problem: real-world stock prices don’t follow clean, continuous-time processes. Markets are discrete, affected by daily closes, overnight gaps, liquidity shifts, and sudden shocks. Even if we can simulate randomness mathematically, that doesn’t mean we can replicate how markets truly behave.

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Case Study: Monte Carlo Simulation on GE Stock

We’ll look at General Electric (GE) between January 2016 and January 2019, a period marked by one of the company’s most dramatic declines, with the stock plunging 57.9% in 2018.