Deep Learning - Recurrent Neural Networks with TensorFlow - Stock Return Predictions Using LSTMs (Part 2)

The video tutorial explains the preference for predicting stock returns over stock prices, using a formula similar to calculating discounts. It details data processing with Pandas, including shifting and subtracting closing prices to calculate returns. The tutorial covers normalizing returns with a standard scaler and training an RNN model, highlighting challenges like overfitting. It concludes with model evaluation through one-step and multi-step forecasts, noting the model's limitations in learning effectively.

Describe the process of calculating returns as mentioned in the text.

What challenges does the model face during training according to the text?

What does the text suggest about the results of the multi-step forecast?