We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gtfintechlab/fin-num-claim | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| check_for_numeric_data | Ran | gtfintechlab/fin-num-claim/code/WS_model.py pointer only (licence: NOASSERTION) · get_code("5430f2e5f4ede39f") |
| split_into_sentences | Ran | gtfintechlab/fin-num-claim/code/WS_model.py pointer only (licence: NOASSERTION) · get_code("9a2f7841367ae5ee") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of optimism. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.11728")
get_code_for_paper("2402.11728")
have("2402.11728")
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