Predict Stock Prices Using RNN: Part 2
Lilian Weng
A tutorial demonstrates building a recurrent neural network for stock price prediction that handles multiple stocks by incorporating stock symbol embedding vectors as input features alongside price data. The model trains on 50 stocks from the S&P 500 using a 90/10 train-test split with configuration parameters including embedding size of 3, LSTM size of 256, and 50 training epochs. The embedding vectors learned by the model cluster similar stocks together, such as Google and Facebook, enabling the model to learn shared patterns across related companies.
Why it matters
In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input.
