Predicting Stock Prices
Abstract
Google is a worldwide company whose stock price data appeals to a vast majority of people across the world. By using the Markov Chains calculations and applying moving average, we forecast Google’s stock prices for the immediate future. The moving averages are grouped into four varied states of states of results. The Markov Chain calculations are applied to the data to give a 4×4 transitional probability matrix that solves a system of equations. This gives four steady states (variables) representing the probability that a stock price for a particular day would fall into one of them. Actual data is applied to this information and achieve predictions of the following stock prices for the immediate future. In general, the Markov Chain method enables us to reliably forecast the stock prices for the next several days.
Introduction
Purpose of the study
This project aims to analyze a year’s worth of stock portfolio for Google using moving average and Markov Chains so to predict the company’s stock prices for the immediate future. Google was the best choice for this study because its popularity owing to its stature as the largest multinational corporation that offers Internet-related products and services.
Literature review
The Markov chain, named after its proprietor Andrey Markov, refers to a mathematical system that transition from one state to another, between a countable or finite number probable state. It is a random process where the next state depends purely on the present state as opposed to the sequence of events that came before it. In other words, the method has the property that, considering the present, the future is largely conditionally independent of the past.
Methods
Expected Results
Discussions and Conclusions
The data set of stock prices presented here are one year’s worth of prices for Google, Inc. The data was readily available online from the company’s website. It is worth noting that Google stock were picked at random and thus the company’s stock do not hold any particular personal interest to us except for academic reasons in this project. The opening and closing prices utilized begin on September 2, 2009 and end on September 3, 2010, giving a total of 263 days of prices used for this project. However, additional studies are needed to ascertain the stock prices for the near future considering the rapid growth that Google Company is experiencing on a daily basis.
