Modeling Stock Prices Using Monte-Carlo Simulation and Excel
Seifedine Kadry, Abdelkhalak El Hami · Advances in computational intelligence and robotics book series · 2016
Monte Carlo simulation or experiments is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. Risk analysis is part of every decision we make. We are constantly faced with uncertainty, ambiguity, and variability. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action... It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values. By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis. In this chapter, a history of the Monte-Carlo simulation and its mechanism is given. We will study, step by step, how to apply this technique to modeling and predict stock prices in financial market Excel through a real data.