FAR-Trans: An Investment Dataset for Financial Asset Recommendation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916320168116224 |
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| author | Sanz-Cruzado, Javier Droukas, Nikolaos McCreadie, Richard |
| author_facet | Sanz-Cruzado, Javier Droukas, Nikolaos McCreadie, Richard |
| contents | Financial asset recommendation (FAR) is a sub-domain of recommender systems which identifies useful financial securities for investors, with the expectation that they will invest capital on the recommended assets. FAR solutions analyse and learn from multiple data sources, including time series pricing data, customer profile information and expectations, as well as past investments. However, most models have been developed over proprietary datasets, making a comparison over a common benchmark impossible. In this paper, we aim to solve this problem by introducing FAR-Trans, the first public dataset for FAR, containing pricing information and retail investor transactions acquired from a large European financial institution. We also provide a bench-marking comparison between eleven FAR algorithms over the data for use as future baselines. The dataset can be downloaded from https://doi.org/10.5525/gla.researchdata.1658 . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_08692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FAR-Trans: An Investment Dataset for Financial Asset Recommendation Sanz-Cruzado, Javier Droukas, Nikolaos McCreadie, Richard Information Retrieval Computational Engineering, Finance, and Science Financial asset recommendation (FAR) is a sub-domain of recommender systems which identifies useful financial securities for investors, with the expectation that they will invest capital on the recommended assets. FAR solutions analyse and learn from multiple data sources, including time series pricing data, customer profile information and expectations, as well as past investments. However, most models have been developed over proprietary datasets, making a comparison over a common benchmark impossible. In this paper, we aim to solve this problem by introducing FAR-Trans, the first public dataset for FAR, containing pricing information and retail investor transactions acquired from a large European financial institution. We also provide a bench-marking comparison between eleven FAR algorithms over the data for use as future baselines. The dataset can be downloaded from https://doi.org/10.5525/gla.researchdata.1658 . |
| title | FAR-Trans: An Investment Dataset for Financial Asset Recommendation |
| topic | Information Retrieval Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2407.08692 |