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as 06-21-2024 4:00pm EST

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Stocks

Consumer Discretionary

Clothing/Shoe/Accessory Stores

Nasdaq

The Cato Corp operates as a specialty retailer of fashion apparel and accessories primarily in the southeastern United States. Its primary objective is to be the fashion specialty retailer for fashion and value in its markets. The company operates through the following business segments: Retail and Credit. The Retail segment which generates the majority of revenue offers fashion specialty stores. The Credit segment involves credit card services.

More About Our CATO ML Model...

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

To train our stocks CATO 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 CATO 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 CATO 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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