Using Historical Monte Carlo Samples to Estimate a Stock Price Target

Summary

The document asks whether a Monte Carlo style estimate can approximate the chance that a stock reaches a target price within a fixed horizon. The proposed procedure is to select historical starting points, measure each subsequent 30-day percentage gain or loss, and use the resulting distribution to estimate the likelihood that a purchase at a chosen price reaches a specified higher level.

No answer, test, or performance evidence is included, so the method is presented only as a question and should not be read as a validated forecasting approach. The central issue is whether randomly sampled historical returns are representative of future outcomes and whether a terminal 30-day return distribution answers the path-dependent question of hitting a price at any time during the period. The document provides no treatment of changing volatility, dependence between observations, market regimes, or uncertainty in the estimate.

Key ideas

• The proposed method samples historical returns over a fixed horizon to estimate the probability of a stock price gain.
• The target event asks whether the price reaches a level within the period, which is path-dependent.
• The document does not provide evidence that the sampling approach produces reliable probabilities.
• Historical return distributions may not represent future conditions, but this question is not resolved in the document.

Tags

# 33071


# Is using a Monte Carlo simulation sufficient for predicting probabilities that a stock will hit a certain price by a certain date?












Forgive my ignorance about my question. I understand a Monte Carlo simulation to basically be `n` times that the truth is checked in some historic data set. For stock prices, if I buy some symbol at say \$100 and I want to predict the probability that the price will reach \$105 within 30 days.

Can I use a MC simulation to look at historic prices and see what the percent gain / loss was over a 30 day period from random starting points and use that to build my probability curve?

Is that too naive or will that yield decent results?

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