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Natural Language Processing(NLP) demonstrates a great potential to supportfinancial decision-making by analyzing the text from social media or newsoutlets. In this work, we build a platform to study the NLP-aided stockauto-trading algorithms systematically. In contrast to the previous work, ourplatform is characterized by three features: (1) We provide financial news foreach specific stock. (2) We provide various stock factors for each stock. (3)We evaluate performance from more financial-relevant metrics. Such a designallows us to develop and evaluate NLP-aided stock auto-trading algorithms in amore realistic setting. In addition to designing an evaluation platform anddataset collection, we also made a technical contribution by proposing a systemto automatically learn a good feature representation from various inputinformation. The key to our algorithm is a method called semantic role labelingPooling (SRLP), which leverages Semantic Role Labeling (SRL) to create acompact representation of each news paragraph. Based on SRLP, we furtherincorporate other stock factors to make the final prediction. In addition, wepropose a self-supervised learning strategy based on SRLP to enhance theout-of-distribution generalization performance of our system. Through ourexperimental study, we show that the proposed method achieves betterperformance and outperforms all the baselines' annualized rate of return aswell as the maximum drawdown of the CSI300 index and XIN9 index on realtrading. Our Astock dataset and code are available athttps://github.com/JinanZou/Astock.

Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model