Put in simple terms, a Random Walk basically involves taking successive random steps and tracing the trajectory. I adapted the example from part one of Wilmott on Quantitative Finance and wrote a quick MATLAB script to simulate a simple random walk.

Basically flip a coin if it is heads multiply the equity seed by 1.01 and if its tails multiply by 0.99.

## MATLAB Simulation Results

The graphs below show successive runs of the script. I simulated 10,000 coin tosses in each test.

**Sim One:** You flipped 5069 heads & 4931 tails

**Sim Two**: You flipped 4946 heads & 5054 tails

## MATLAB Code

Writing the code in MATLAB is very straightforward I wrote the code in less then five minutes so it is a bit rough around the edges. I probably could refactor it but it does the job.

% Simple Simulated Random Walk Adapted % from part 1 Paul Wilmott on Quant Fin % create matrix with random int either 1 or 2 randMat = randi(2,10000,1); randEquity = 1:10000; headCount = 0; tailCount = 0; equitySeed = 100.00; for i=1:10000 if randMat(i)==1 % we flipped a heads headCount = headCount + 1; equitySeed = 1.01 * equitySeed; randEquity(i) = equitySeed; else % must have flipped tails tailCount = tailCount + 1; equitySeed = 0.99 * equitySeed; randEquity(i) = equitySeed; end end fprintf('You flipped %i heads & %i tails\n', headCount, tailCount); plot(randEquity); title('Simulated Random Walk'); xlabel('Number of Coin Tosses'); ylabel('Price in ($)'); % clean-up clear equitySeed headCount tailCount randMat i;

MATLAB makes it very easy to quickly conduct experiments and run simulations. It would be far to time consuming to manually flip a coin 10,000 times. This quick example highlights how a simple random walk can look very similar to stock or security charts. It also shows how powerful MATLAB is as a learning and prototyping tool

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