Conformal Prediction for Reliable Stock Selections

Ipek Kaya, Khuong An Nguyen
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:781-783, 2025.

Abstract

A major challenge in quantitative finance is not just predicting which stocks will outper- form but quantifying the uncertainty and reliability of those predictions. This is critical because financial markets are inherently noisy, volatile, and affected by countless unpre- dictable factors, meaning that even the best models can be wrong, sometimes dramatically so. Reliable measures of uncertainty are essential for risk- aware investment decisions, they help portfolio managers judge when to trust a prediction, size positions appropriately, and avoid overconfidence that can lead to costly losses. Currently, most machine learning approaches for stock selection produce only point predictions, offering no meaningful measure of confidence, which limits their practical value for investors who need to manage risk. Thus, in this paper, we bench- marked classical and deep learning models for US stock selection, and applied conformal prediction (CP) to generate well-calibrated prediction sets. Across all models, CP achieved empirical coverage closely matching the nom- inal confidence level, with most prediction sets being singletons. This result demonstrated the potential of applying CP for reliable and interpretable stock selection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-kaya25a, title = {Conformal Prediction for Reliable Stock Selections}, author = {Kaya, Ipek and Nguyen, Khuong An}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {781--783}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/kaya25a/kaya25a.pdf}, url = {https://proceedings.mlr.press/v266/kaya25a.html}, abstract = {A major challenge in quantitative finance is not just predicting which stocks will outper- form but quantifying the uncertainty and reliability of those predictions. This is critical because financial markets are inherently noisy, volatile, and affected by countless unpre- dictable factors, meaning that even the best models can be wrong, sometimes dramatically so. Reliable measures of uncertainty are essential for risk- aware investment decisions, they help portfolio managers judge when to trust a prediction, size positions appropriately, and avoid overconfidence that can lead to costly losses. Currently, most machine learning approaches for stock selection produce only point predictions, offering no meaningful measure of confidence, which limits their practical value for investors who need to manage risk. Thus, in this paper, we bench- marked classical and deep learning models for US stock selection, and applied conformal prediction (CP) to generate well-calibrated prediction sets. Across all models, CP achieved empirical coverage closely matching the nom- inal confidence level, with most prediction sets being singletons. This result demonstrated the potential of applying CP for reliable and interpretable stock selection.} }
Endnote
%0 Conference Paper %T Conformal Prediction for Reliable Stock Selections %A Ipek Kaya %A Khuong An Nguyen %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-kaya25a %I PMLR %P 781--783 %U https://proceedings.mlr.press/v266/kaya25a.html %V 266 %X A major challenge in quantitative finance is not just predicting which stocks will outper- form but quantifying the uncertainty and reliability of those predictions. This is critical because financial markets are inherently noisy, volatile, and affected by countless unpre- dictable factors, meaning that even the best models can be wrong, sometimes dramatically so. Reliable measures of uncertainty are essential for risk- aware investment decisions, they help portfolio managers judge when to trust a prediction, size positions appropriately, and avoid overconfidence that can lead to costly losses. Currently, most machine learning approaches for stock selection produce only point predictions, offering no meaningful measure of confidence, which limits their practical value for investors who need to manage risk. Thus, in this paper, we bench- marked classical and deep learning models for US stock selection, and applied conformal prediction (CP) to generate well-calibrated prediction sets. Across all models, CP achieved empirical coverage closely matching the nom- inal confidence level, with most prediction sets being singletons. This result demonstrated the potential of applying CP for reliable and interpretable stock selection.
APA
Kaya, I. & Nguyen, K.A.. (2025). Conformal Prediction for Reliable Stock Selections. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:781-783 Available from https://proceedings.mlr.press/v266/kaya25a.html.

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