Official Code for the Paper "Meta-Learning for Cross-Sectional Return Prediction in Financial Markets"

In this paper, we propose the Financial Prior-Data Fitted Network (FinPFN), a meta-learning framework utilizing a Transformer architecture for cross-sectional stock return prediction and classification.

Getting Started

This is a Python project, we used Python 3.9 in development and recommend to use a virtualenv or conda. To use our code, clone the project with

git clone [email protected]:wangy8989/FinPFN.git

install all dependencies with

pip install -r requirements.txt

The code is forked from TransformersCanDoBayesianInference.

Training a model

financial_dataloader.py and data_utils.py provides the dataloader of financial data prior for the Transformer.

financial_model_training.py provides methods to train and evaluate a PFN model with data prior.

config =
{'lr': 3e-05,
 'epochs': 60,
 'dropout': 0.0,
 'emsize': 256,
 'batch_size': 64,
 'nlayers': 5,
 'num_outputs': 10,
 'num_features': 30,
 'steps_per_epoch': 100,
 'nhead': 4,
 'seq_len': 100,
 'nhid_factor': 2,
 'validation_period': 1}
prior_config =
{'date_style': 'consecutive',
 'sample_replacement': False,
 'multiclass': 10,
 'num_outputs': 10,
 'num_features': 30,
 'fuse_x_y': False,
 'device': 'cpu'}
model = train_model(config, prior_config, save=True)

Evaluating Models

Financial_Data_Model_Training.ipynb provides a workflow to evaluate baselines and the transformer.

method_list = ["FinPFN"]
eval_pos = 50  #half of sequence length=100
output_df = test_model(eval_pos, method_list, prior_config)

Cite

When using, please cite FinPFN

PFNs were introduced in

@inproceedings{
    muller2022transformers,
    title={Transformers Can Do Bayesian Inference},
    author={Samuel M{\"u}ller and Noah Hollmann and Sebastian Pineda Arango and Josif Grabocka and Frank Hutter},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=KSugKcbNf9}
}

TabPFNs were introduced in

@article{hollmann2025tabpfn,
 title={Accurate predictions on small data with a tabular foundation model},
 author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
         Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
         Schirrmeister, Robin Tibor and Hutter, Frank},
 journal={Nature},
 year={2025},
 month={01},
 day={09},
 doi={10.1038/s41586-024-08328-6},
 publisher={Springer Nature},
 url={https://www.nature.com/articles/s41586-024-08328-6},
}