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as 12-20-2024 4:00pm EST

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Stocks

Consumer Staples

Beverages (Production/Distribution)

Nasdaq

CCEP is the second-largest bottling partner in the Coca-Cola system by volume, behind Coca-Cola Femsa, and primarily operates in developed Europe (80% of 2023 revenue and EBIT) and Australasia (20%).In 2023, CCEP sold 3.3 billion unit cases of beverages, which we estimate equates to roughly 9% of the global Coke system volume. Coke's largest bottler, Coca-Cola Femsa sold 4 billion unit cases (12%), and the third-largest, Coca-Cola HBC, serving Eastern Europe and North Africa, sold 2.8 billion unit cases (8%).TCCC owns 19% of the equity of CCEP, Olive Partners, a holding company of bottling operations, owns a further 36%, and the remaining 45% is free float.

More About Our CCEP ML Model...

What kind of parameters do you use to train CCEP stocks ML model?

To train our stocks CCEP ML model, we use historical data with over 25 parameters, including volume indicators, volatility indicators, momentum indicators, trend indicators, and other metrics.

How often do you update CCEP ML model?

We update our ML model either weekly or biweekly, depending on the market capitalization of the stocks, using TensorFlow, typically over the weekend.

Why is the accuracy of your CCEP model low?

Unfortunately, our current data provider offers limited historical data, which impacts the accuracy of our ML model. However, as we continue to gather more data over time and add more indicators, we expect the accuracy of our model to improve.

How can I provide and share more data with you to increase your ML model accuracy?

We would greatly appreciate your contribution. Please send an email to [email protected]

Do you offer an ML model for shorter time periods, such as 5 minutes or 15 minutes?

Yes, we offer it for strategy purposes only, with intervals: 1 minute, 2 minutes, 5 minutes, 15 minutes, and 30 minutes etc.

Can I rely on your ML model to make financial decisions regarding buying or selling stocks, or is it only intended for learning purposes?

Our ML model is primarily designed for educational purposes and is not intended to provide financial advice. We strongly recommend consulting with a financial advisor before making any buying or selling decisions.

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