PCA vs t-SNE in asset pricing

So I am trying dimensionality reduction techniques on the SP500 FY2020 data.

I understand the CAPM model and the fact that doing a PCA determines my market variability factor (the first PCA component).

What I am wondering is, what intuitions (if any) does t-SNE give on the same data? Using scikit-learn I have embeddings for the first component, but does the embedding relate to CAPM in any way? Or for that matter any other asset pricing model?

What I have tried,

1. PCA for 99% variance and computed projections for the first PC (PC1 weight times daily change of every stock's price, then summed across all stocks)
2. A 2D t-SNE on the same data
3. A 1D t-SNE on the same data

Below is a plot of the above 3.