Coca-Cola: Historical Stock Prices(1961-2023)

Project Overview

This is focused on analyzing long-year( 1961-2023) stock price data for Coca-Cola, comparing it with the general stock market price for the same period. The proxy for the general stock market price is represented by the S&P 500, that is, the stock prices of the best and biggest 500 companies in the US. The major technique of analysis applied is linear regression to gain insight into Coca-Cola stock prices could explain the rise in the general stock market performance, or vice versa.

Data Preparation and Cleaning

The original data sourced from see here only contained Coca-Cola stock prices historical data. This was discovered after preliminary viewing of the data using the head and tail function in Python. There was a need to have S & P 500 data arranged horizontally. I resorted to using Python libraries to clean, arrange and download Coca-Cola historical stock prices (1961-2023) and S&P 500 historical stock prices (1961-2023), side by side.

• Data was arranged in columns to have fields such as opening stock prices, adj closing stock prices and closing stock prices.

• Python libraries were used to arrange the data and pulled them from sources such as Yahoo Finance

• Python codes for pulling and arranging data from source
  import yfinance as yf import pandas as pd

Define the ticker symbols for S&P 500 and Coca-Cola

sp500_ticker = '^GSPC' coca_cola_ticker = 'KO'

Define the date range (from 1962-01-01 to the present date)

start_date = '1962-01-01' end_date = pd.to_datetime('today').strftime('%Y-%m-%d')

Download historical data from Yahoo Finance

sp500_data = yf.download(sp500_ticker, start=start_date, end=end_date) coca_cola_data = yf.download(coca_cola_ticker, start=start_date, end=end_date)

Display the first few rows of the data

print("S&P 500 Data:") print(sp500_data.head())

print("\nCoca-Cola Data:") print(coca_cola_data.head())

Preliminary and Exploratory Analysis

The analysis is essentially a linear regression of a long range of datasets. The important thing is to first check for correlational analysis. Thus was carried out to check the pattern of association.

• correlation where Coca-Cola stock price is the x(independent variable)

• Correction where Coca-Cola stock price is the y(dependent variable)

In both analyses, Coca-Cola stock correlates with SP500 at 0.94 positive. This shows a strong positive significant relationship.

• Correlation with regression line further clearly depicts the direction of the association.

Regression Analysis

Analysis was done by switching the role( switching the variables) at a time when Coca-Cola stock price was treated as the X( the independent variable ) while SP 500 was taken as the dependent variable. In the second round of analysis, the roles were reversed. However, the result of the analysis proved to be the same.

The variables were switched to enrich the quality of the analysis. Theoretically, it might be difficult to emphatically establish which variable predicts the other among Coca-Cola and SP500 which proxied the general market stock prices. However, common-sensically, it could be safe to conclude that the general stock market might have some measure of impact over Coca- Coca-Cola stock prices.

• Summary of regression analysis:

Regression Results - Coca-Cola Closing Prices:

• R-squared: 0.887 (implies 88.7% explanatory power)
• F-statistic: Highly significant (p < 0.05)
• Intercept: 2.6242 (Estimated Coca-Cola closing price when S&P 500 is zero)
• SP500_Close Coefficient: 0.0161 (On average, each one-unit increase in S&P 500 corresponds to a 0.0161 increase in Coca-Cola closing prices)

Regression Results - S&P 500 Closing Prices:

• R-squared: 0.887 (implies 88.7% explanatory power)
• F-statistic: Highly significant (p < 0.05)
• Intercept: -40.5312 (Estimated S&P 500 closing price when Coca-Cola is zero)
• CocaCola_Close Coefficient: 55.2114 (On average, each one-unit increase in Coca-Cola corresponds to a 55.2114 increase in S&P 500 closing prices)

Overall Interpretation:

Both models exhibit high explanatory power and statistical significance over each other. The positive coefficients indicate a positive relationship. Please remember, that this has already been pointed to by the preliminary analysis, the correlation. As the S&P 500 increases, Coca-Cola closing prices tend to increase, and vice versa. This suggests potential interdependence between Coca-Cola and the broader market.

Conclusion

Thus, further research is needed to understand the foundational connection between Coca-Cola Stock prices and SP500.

References

1. Coca-Cola Historical Stock prices (1961 to date) Download from here
2. Yahoo Finance/ Python yfinance library