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Predictive Data Analysis with Python

Section 5: Predicting Future Stock Behavior

Explore how to use Monte Carlo simulations alongside the random walk theory to predict future stock prices. Understand the calculation of drift, random variables, and logarithmic returns to model stock price movements using Python tools. Gain practical skills to implement and visualize multiple future price paths for stock market analysis.

We'll cover the following...

There are two main techniques used to analyze stock behavior.

  1. Fundamental analysis: This mostly deals with the intrinsic value of companies based on the various changes in their financials on a regular basis.

  2. Technical analysis: This provides results based on the historical data of a company’s stock.

Fundamental analysis is beyond the scope of this course, and the information required for it is also not easily accessible.

Until now, our focus has been on technical analysis, as we calculated various results from the historical data of the companies. However, these techniques won’t help us predict the random and irregular behavior of stocks.

Random walk theory

Many analysts believe that the stock market prices follow the random walk theory. This theory states the following:

The stock market may take random, irregular, and unpredictable paths in determining the prices of stocks. It also assumes that past trends are useless in predicting future prices and that the future price only depends on the current price of the stock.

Detailed information about this theory can be found here. This theory rejects both fundamental and technical analysis techniques with rational arguments.

We will also use the random walk theory to determine the future behavior of stocks. The Monte Carlo simulations will be deployed to assess the results using this theory.

MonteCarlo simulations

This method uses randomness to solve problems. It converts the randomness in the variables into probability distributions. It then generates a range of future price values in a normal distribution instead of just one value. More detailed information about this can be found here.

The following is the formula for MonteCarlo simulations:

St+1=St∗eDrift+RvS_{t+1}=S_{t}*e^{Drift+Rv} ...