As you may know, NN model doesn’t perform well for tabular data which consists of technical indicators, rather than that, decision trees outperform in most cases.
Also, at least in my experimental environment, NN’s performance on raw stock price data is terrible to solve Numerai Signals.
I know there are some papers and articles that support this fact with great experiments and I already tried NN approaches on raw price data and technical indicators, then unfortunately I completely agree with these facts.
However, I don’t want to stop to believe the power of Neural Networks, I want to explore their extraordinal capability to move forward to the next step.
Especially, I’m now focusing on representation learning for time series data, like, TS2Vec, Multi-Task Self-Supervised Time-Series Representation Learning, Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion, FEAT: A GENERAL FRAMEWORK FOR FEATURE-AWARE MULTIVARIATE TIME-SERIES REPRESENTATION LEARNING.
Can we use or modify these ideas for stock price?
So here is an idea posted on Reddit, however, my brain is very limited, so I want to hear other approaches with NN which probably works for stock price data.
I know these methods cannot be shared with other people because they cannot keep their originality, however, I think it’s good to discuss a new approach.