Ploutos: Towards interpretable stock movement prediction with financial large language model
By Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang
Rating
1557
Battle Count: 44
Relevance
9/10
The paper presents a highly relevant approach for stock movement prediction with interpretable rationales, which is directly applicable to quantitative trading strategies.
Implementation Complexity
8/10
The implementation involves complex components including multiple expert models, rearview-mirror prompting, and dynamic token weighting, which may require significant effort to reproduce.
Reproducibility
3/5
The paper provides detailed descriptions of the methodology and evaluation process, but the full implementation details and datasets are not explicitly shared.
About this paper
Methodology: Ploutos. Problem types: Classification, Time Series Forecasting, Natural Language Processing.
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