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fulifeng / Temporal_Relational_Stock_Ranking

Code for paper "Temporal Relational Ranking for Stock Prediction"
https://arxiv.org/pdf/1809.09441.pdf
GNU Affero General Public License v3.0
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Missing data in prediction #15

Open victorxie996 opened 2 years ago

victorxie996 commented 2 years ago

Hi!

Appreciate for sharing such greate work!

My concern regarding to the pratical usability of this whole pipeline is: if you select a stock universe i.e., stocks in the CSI300 index, or just simply select n stocks (doesn't really matter), to train the model, then how is the model gonna functioning well (do the right prediction) if some stocks quit the market, or stopped trading for a short period? From my understanding the vertices and edges cannot be missing, and imputing values to nan would lead to inaccurate prediction?

QuantHao commented 2 years ago

Agree, this is why I am so far still not fully trust GNN could be useful.