Stock Price Prediction using Linear Regression

In order to construct predictions when we have discovered a relationship between two or more variables of interest, we can utilize linear regression. There are only two variables in simple linear regression: one independent variable and one dependent variable. Stock prediction is just one of the various use cases of the algorithm.

Other examples of Simple Linear Regression

  1. Predicting the price of a house based on its square footage: In this case, the square footage of the house would be the independent variable, and the price would be the dependent variable. A simple linear regression model could be used to predict the price of a house based on its square footage, taking into account the linear relationship between these two variables.

  2. Predicting the number of units sold based on the price of a product: In this case, the price of the product would be the independent variable, and the number of units sold would be the dependent variable. A simple linear regression model could be used to predict the number of units that would be sold at different price points, based on the linear relationship between these two variables.

  3. Predicting the amount of rainfall in a region based on the average temperature: In this case, the average temperature would be the independent variable, and the amount of rainfall would be the dependent variable. A simple linear regression model could be used to predict the amount of rainfall that is likely to occur at different temperatures, based on the linear relationship between these two variables.

The Data

SPY was used to obtain the data used in this project. SPY is the ticker symbol for the SPDR S&P 500 ETF, which is a exchange-traded fund (ETF) that tracks the performance of the S&P 500 index. The S&P 500 index is a market-capitalization-weighted index that consists of 500 large-cap stocks listed on the New York Stock Exchange (NYSE) or the NASDAQ. The SPY ETF is designed to provide investors with a convenient and cost-effective way to invest in the broad U.S. stock market.

As an ETF, SPY is traded on stock exchanges like a stock, and it can be bought and sold through a broker. The price of SPY is based on the value of the stocks in the S&P 500 index, and it is intended to provide investors with a return that is similar to the performance of the underlying index.

Result

price_prediction

Limitations of the model

Simple linear regression is not the best strategy to predict stock prices (it will not make you a lot of money) since not all stocks increase linearly over time. Any random and unforeseen event will cause this linear regression model to collapse.