Residual return extraction using Principal Component Equivalence method [in Japanese]

Kentaro Imajo, Kei Nakagawa, Kazuki Matoya, Masanori Hirano, Masana Aoki, Taku Imahase

The 38th Annual Conference of the Japanese Society for Artificial Intelligence, p. JSAI2024_3D5GS205, May 30, 2024

Conference

The 38th Annual Conference of the Japanese Society for Artificial Intelligence

Abstract

In this paper, we focus on the residual returns that are not explained by the common factors in financial asset returns. We propose a novel method to extract well-behaved residual returns based on principal component analysis (PCA). Traditional PCA requires determining the number of common factors, presenting a trade-off: increasing the number reduces common factors but also increases the potential for noise. Our proposed method randomly divides returns into two groups, extracts factors (PC) from one, and estimates eigenvalues from the other. Then, by creating a projection matrix that aims to transform eigenvalues to the same level, the proposed method can extract residual returns with better and more stable properties than PCA. Finally, we demonstrate that our method is capable of extracting residual returns with desirable properties through analysis based on both synthetic and real market data.

Keywords

Multi Agent; Financial Market; Micro Foundation;

doi

10.11517/pjsai.JSAI2024.0_3D5GS205

bibtex

@inproceedings{Imajo2024-jsai38,
  title={{Residual return extraction using Principal Component Equivalence method [in Japanese]}},
  author={Kentaro Imajo and Kei Nakagawa and Kazuki Matoya and Masanori Hirano and Masana Aoki and Taku Imahase},
  booktitle={The 38th Annual Conference of the Japanese Society for Artificial Intelligence},
  pages={JSAI2024_3D5GS205},
  doi={10.11517/pjsai.JSAI2024.0_3D5GS205},
  year={2024}
}