  Natural Language Processing(NLP) demonstrates a great potential to support
financial decision-making by analyzing the text from social media or news
outlets. In this work, we build a platform to study the NLP-aided stock
auto-trading algorithms systematically. In contrast to the previous work, our
platform is characterized by three features: (1) We provide financial news for
each specific stock. (2) We provide various stock factors for each stock. (3)
We evaluate performance from more financial-relevant metrics. Such a design
allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a
more realistic setting. In addition to designing an evaluation platform and
dataset collection, we also made a technical contribution by proposing a system
to automatically learn a good feature representation from various input
information. The key to our algorithm is a method called semantic role labeling
Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a
compact representation of each news paragraph. Based on SRLP, we further
incorporate other stock factors to make the final prediction. In addition, we
propose a self-supervised learning strategy based on SRLP to enhance the
out-of-distribution generalization performance of our system. Through our
experimental study, we show that the proposed method achieves better
performance and outperforms all the baselines' annualized rate of return as
well as the maximum drawdown of the CSI300 index and XIN9 index on real
trading. Our Astock dataset and code are available at
https://github.com/JinanZou/Astock.
