This timeline depicts the market share of The Coca-Cola Company in the United States from 2013 to 2024. In 2024, Coca-Cola's U.S. market share amounted to **** percent. Other soft drink industry market shares may be found here. The Coca-Cola CompanyThe Coca-Cola Company is a producer, retailer and marketer of non-alcoholic beverages and is well-known for its soft drink, Coca-Cola. The company was founded in 1892 and comprises the corporate division, which is headquartered in Atlanta, GA, and about 300 bottling partners worldwide.The product portfolio of Coca-Cola includes non-alcoholic beverages such as soft drinks, bottled water, sports drinks and energy drinks. The company’s most famous soft drink is undoubtedly the soft drink Coca-Cola. Brand rankings list the brand, for good reasons, as one of the most valuable and recognizable brands worldwide. The history of Coca-Cola began in 1886 when Atlanta pharmacist Dr. John S. Pemberton created a flavored syrup with a distinctive taste which could be sold at soda fountains by mixing the two components together. The secret formula, which originated in the United States, is still used today for producing Coca-Cola around the world.

Over the last years, Coca-Cola has spent an average of ************* dollars a year on advertising worldwide, aside from 2020, with only about *** billion U.S dollars, and 2024, with over ************* dollars spent. Spending in the United States accounts for over ** percent of that cost, totally *** million U.S. dollars in 2022. Advertising innovation Soft drinks still made up the overwhelming majority of Coca Cola’s sales volume in 2017. The company spent *** million dollars advertising its flagship brand, Coke, in 2022, more than any other non-alcoholic beverage brand in the United States in that year. Coca-Cola has a long history of innovative and appealing advertising campaigns, going back to art done by American painter Norman Rockwell, to the iconic polar bears of Christmas advertising. Dominance across mediums The Coca-Cola brand’s image is not only maintained through traditional advertising mediums but social media as well. It ranked fifth on Facebook, in terms of fans, as of September 2023, with almost *** million fans. Coca-Cola’s YouTube channel was equally popular with roughly **** billion views accrued by the channel.

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The Coca-Cola Company is an North American multinational beverage corporation incorporated under Delaware's General Corporation Law[a] and headquartered in Atlanta, Georgia. The Coca-Cola Company has interests in the manufacturing, retailing, and marketing of non-alcoholic beverage concentrates and syrups, and alcoholic beverages. The company produces Coca-Cola, the sugary drink for which it is best known for, invented in 1886 by pharmacist John Stith Pemberton. At the time, the product was made with coca leaves, which added an amount of cocaine to the drink, and with kola nuts, which added caffeine, so that the coca and the kola together provided a stimulative effect. This stimulative effect is the reason the drink was sold to the public as a healthy "tonic", and the coca and the kola are also the source of the name of the product and of the company.In 1889, the formula and brand were sold for $2,300 (roughly $68,000 in 2021) to Asa Griggs Candler, who incorporated The Coca-Cola Company in Atlanta in 1892.

Since 1919, Coca-Cola has been a publicly traded company. Its stock is listed on the New York Stock Exchange under the ticker symbol "KO". One share of stock purchased in 1919 for $40, with all dividends reinvested, would have been worth $9.8 million in 2012, a 10.7% annual increase adjusted for inflation. A predecessor bank of SunTrust received $100,000 for underwriting Coca-Cola's 1919 public offering; the bank sold that stock for over $2 billion in 2012. In 1987, Coca-Cola once again became one of the 30 stocks which makes up the Dow Jones Industrial Average, which is commonly referenced as a proxy for stock market performance; it had previously been a Dow stock from 1932 to 1935. Coca-Cola has paid a dividend since 1920 and, as of 2019, had increased it each year for 57 years straight.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Coca-Cola (KO): Fizz or Fizzle?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can Coca-Cola (KO) Still Fizzle Out?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data