Constructing Binomial Tree to Describe Stock Price Movement

The future price movement of a stock can be approximated using a binomial tree. In a binomial tree, a stock can

Let’s say the current stock price is S. The price of the stock can either move or move down. We will refer to the up movement as u and a down movement as d. A down movement d will then be given by d where d is assumed to be equal to 1/u. We also need the probability of an up move (p) and the probability of a down move (1 - p).

The following diagram shows the price movement in a small time period Δt.

 

From each node at Su and Sd, the prices can again take two possible paths.

  • Su can move to Suu or Sud. Note that Sud will be the same as S (S*u*1/u).
  • Sd can move to Sdu (or just S) or Sdd.

This way a binomial tree can be built for multiple periods. The following diagram shows the binomial tree for 4 periods.

 

Let’s take an example to understand these values.

Initial stock price, S = $20

u = 1.02

d = 1/1.02

Probability of up move, p = 0.60

Probability of down move, (1-p) = 0.40

The possible stock values for period 1:

Su = 20*1.02 = 20.40 with a probability of 0.60

Sd = 19.60 with a probability of 0.40

The possible stock values for period 2:

Suu = 20.81 with a probability of 0.60*0.60 = 0.36

S = $20 with a probability of 2*0.60*0.40 = 0.48

Sdd = 19.22 with a probability 0.40*0.40 = 0.16

Similarly we can calculate the values for period 3 and period 4. Binomial trees have their application in pricing options and other financial concepts.

The following diagram shows this binomial tree upto two periods.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.