Abstract
Stock prediction plays a key role in stock investments. Despite the promising achievements of existing solutions, there are still limitations. First, most methods focus on mining the local features from node neighbors, while ignoring non-local features in the stock market. Second, most existing works form the portfolio with the stocks with the highest predicted return, exposed to some risk factors that cause common price movements. To reduce the risk exposure, it is crucial to learn a diversified portfolio. To address the shortage of existing methods, this paper proposes a novel stock recommendation framework that enables both local and non-local feature learning for stock data. Different from the existing methods, the stocks are selected locally according to the ranks within each independent group. This strategy diversifies the recommended stocks effectively. Experimental results on multiple datasets from the U.S. and Chinese stock markets demonstrate the superiority of the proposed method over existing state-of-the-art methods.
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Acknowledgements
This work is supported by Science and Technology Innovation 2030 - ‘New Generation Artificial Intelligence’ Major Project (Grant Nos.: 2018AAA0100302) and partially supported by the National Natural Science Foundation of China (Grant No. 62076010 and 62276008).
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Yue, Z., Tan, Y. (2022). Non-local Graph Aggregation for Diversified Stock Recommendation. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_12
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