Predicting Stock Prices Using Linear Regression: A Practical Example in the Technology Industry

Introduction

Understanding the factors that influence stock prices is crucial for finance students and professionals. Linear regression is an effective tool for predicting stock prices based on historical data and key performance indicators (KPIs). In this article, we will walk through a practical example of predicting a stock price in the technology industry using two specific KPIs: Free Cash Flow (FCF) and Monthly Active Users (MAU) of an online tech platform.

Why Free Cash Flow (FCF) and Monthly Active Users (MAU)?

• Free Cash Flow (FCF): FCF represents the cash generated by a company after accounting for capital expenditures. It is a crucial indicator of a company's financial health and its ability to generate cash that can be used for expansion, dividends, or debt reduction.

• Monthly Active Users (MAU): For technology companies, especially those with online platforms or software services, MAU is a key metric indicating user engagement and potential for revenue generation. Higher MAU can signal strong user growth and engagement, positively impacting stock prices.

  
  

Step-by-Step Guide

Step 1: Enable the Data Analysis ToolPak in Excel

1. Open Excel and navigate to File -> Options.

2. In the Options dialog box, select Add-ins -> Excel Add-ins and click Go.

3. Check the Analysis ToolPak option and click OK.

Step 2: Prepare the Dataset

For this example, assume we have historical data for a technology company, including stock prices, FCF, and MAU for the past 10 years. Here is a sample dataset:

Year	Stock Price	FCF ($M)	MAU (Millions)
2014	150	500	20
2015	160	550	25
2016	170	600	30
2017	180	650	35
2018	190	700	40
2019	200	750	45
2020	220	800	50
2021	230	850	55
2022	240	900	60
2023	250	950	65

Step 3: Performing Linear Regression in Excel

1. Open the dataset in Excel.

2. Navigate to the Data tab and click on Data Analysis in the Analysis group.

3. Select Regression from the list of analysis tools and click OK.

4. In the regression dialog box:

   • For the Input Y Range, select the column containing stock prices (e.g., C2:C11).

   • For the Input X Range, select the columns containing FCF and MAU data (e.g., D2:E11).

5. Ensure that the Labels box is checked.

6. Select New Worksheet Ply in the Output options section.

7. Click OK to run the regression analysis.

Step 4: Interpreting the Results

After performing the regression, a new worksheet will appear with various results, including summary statistics, ANOVA, and coefficients.

1. Summary Statistics

• Multiple R: Indicates the strength and direction of the linear relationship. A value close to 1 suggests a strong correlation.

• R Square: Represents the proportion of variance in the stock price explained by FCF and MAU. Values closer to 1 indicate a good model fit.

• Adjusted R Square: Adjusted for the number of predictors. Useful for comparing models.

• Standard Error: Measures the average distance that observed values fall from the regression line.

• Observations: Total number of data points analyzed.

  
  

2. ANOVA (Analysis of Variance)

• Degrees of Freedom (df): Number of values in the final calculation that are free to vary.

• Sum of Squares (SS): Quantifies variation explained by the model and residuals.

• Mean Square (MS): Derived by dividing the Sum of Squares by the Degrees of Freedom.

• F-statistic (F): Determines the model’s overall significance.

• Significance F: The P-value associated with the F-statistic. A value less than 0.05 indicates the model fits well.

  
  

3. Coefficients and Other Metrics

• Coefficients: Represent the estimated change in the stock price for a one-unit change in FCF or MAU.

• Standard Error: Measures the average distance between observed values and the regression line.

• t Stat and P-value: Indicate the statistical significance of each coefficient.

• Confidence Intervals: Provide the range within which the true coefficients are expected to fall.

Example Interpretation

• Coefficient for FCF: Suppose the coefficient is 0.2. This indicates that for every $1 million increase in FCF, the stock price increases by $0.2.

• Coefficient for MAU: Suppose the coefficient is 4. This indicates that for every 1 million increase in MAU, the stock price increases by $4.

• R Square: Suppose R Square is 0.92. This means that 92% of the variance in the stock price can be explained by FCF and MAU.

Step 5: Visualizing the Results

1. Insert a Scatter Plot:

   • Highlight the cells containing stock prices and one KPI (e.g., FCF).

   • Navigate to Insert -> Scatter chart icon.

2. Relabel the Chart and Axes:

   • Rename the chart and axes to accurately describe the relationship (e.g., "Stock Price vs. FCF").

3. Add a Trendline:

   • Click on any data point on the scatter plot, then right-click and select Add Trendline.

4. Format the Trendline:

   • Adjust the trendline’s visibility by changing its width and color.

Final Thoughts

By following this guide, finance students can effectively use linear regression in Excel to predict stock prices based on key performance indicators such as Free Cash Flow (FCF) and Monthly Active Users (MAU). This method allows for a deeper understanding of the factors influencing stock prices and provides a practical approach to applying theoretical knowledge in a real-world context. Practice with different datasets and KPIs to refine your predictive modeling skills further.