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Estimating an Up Probability from a Binomial Stock Price Average

Article Quant Q&A · Author: Harsh Sharma

Summary

The document explains how to infer the probability of an up move in a one-period binomial stock model from the average terminal price across repeated trials. With initial price $100, possible terminal prices of $105 and $97, and an observed average of $102, the expected-value equation weights each outcome by its probability. Solving that equation gives an up probability of 5/8. The accompanying explanation connects the probability to the relative frequency of up outcomes in a large set of Bernoulli trials.

A second answer discusses a different interpretation in which the stock undergoes a million successive moves, rather than a million independent one-period trials. That distinction matters: repeated multi-period compounding is not the same experiment as averaging one-period outcomes. The document offers no empirical validation or broader pricing framework, and its two interpretations should not be conflated when applying the calculation.

Key ideas

  • In a one-period binomial model, the expected terminal price is the probability-weighted average of the two possible prices.
  • The stated average terminal price implies an up-move probability of 5/8 under the one-trial interpretation.
  • Relative frequencies from many independent trials approximate the event probability.
  • A million independent one-period trials differ from one path containing a million successive price moves.

Tags

Full text
# Stock pricing using Binomial model


# Stock pricing using Binomial model












A stock is prices at $ \$100$ and follows a one-period binomial process with an up move that equals 1.05 and a down move that equals 0.97. If one million Bernoulli trials are performed and the average terminal stock price is $ \$102$, the probability of an up move is closest to ____?

My book gives the solution as follows: $$ p \cdot 105 + (1-p) \cdot 97 = 102$$and thus computes the value of $ \text p$. However. I can't understand how a first step average of the process equals to the average terminal stock price that comes after one million Bernoulli trials. Can someone help me with this question?

## Answer by Cettt (score 1)

https://quant.stackexchange.com/a/51475

Assume that you did $N = 10^6$ Bernoulli trials. These trials can end up in one of two states up and down.

- In the "up" case the stock is worth \$105. Assume that we have a total of $U$ up cases.

- In the "down" case the stock is worth \$97. Assume that we have a total of $D$ down cases. Clearly $U + D = N$.

This means that a proportion of $\frac{U}{N}$ of your trials results in an upstate. Relative frequencies are a good approximation for probabilities so you could also say the probability that the trial will result in the upstate is given by $\frac{U}{N}$. Define this quantity as $p$, i.e. $$p = \frac{U}{N}.$$

Similarly, you can define the probability that your trial will result in the down state as $$ \frac D N = \frac {N - U}N = 1-p. $$

Given these probabilities you can compute the expected value: $$ e = p \cdot 105 + (1-p) \cdot 97. $$

Now if you know that $e = 102$ you can easily solve for $p$: $$ p = \frac{e - 97}{105 - 97} = \frac 58. $$

## Answer by Kshitij Dutt (score 0)

https://quant.stackexchange.com/a/74926

The question mentions that 1 million Bernoulli trials are conducted. Suppose the result of $i^{th}$ trial is $X_i$, then the average of all $X_i$s is $\\\$102$. Thus, we use the weighted average formula.

If the question stated that the average was $\\\$102$ AFTER 1 million trials, then the following approach needs to be adopted

Probability of $n$ successes in $N$ trials is $ {N\choose n}p^n(1-p)^{N-n}$. Thus, the final value of the stock price can be $100*{N\choose n}1.05^n*p^n*0.97^{N-n}(1-p)^{N-n}$ assuming $n$ up moves and (thus) $N-n$ down moves.

Therefore, the average stock price should be $\sum_{n=0}^N100*{N\choose n}1.05^n*p^n*0.97^{N-n}(1-p)^{N-n}=102$

In other words: $\sum_{n=0}^N{N\choose n}1.05^n*p^n*0.97^{N-n}(1-p)^{N-n}=1.02$ where $N=1,000,000$

This simplifies to: $\sum_{n=0}^N{N\choose n}(1.05p)^n*(0.97-0.97p)^{N-n}=1.02$

Comparing with the formula of binomial expansion

$(1.05p+0.97-0.97p)^N=1.02$

Since $N$ is large, we get: $(0.97+0.08p)\approx1$, which gives $p=0.375$

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.