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Causal Network Representations in Factor Investing

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Causal Network Representations in Factor Investing. / Howard, Clint; Lohre, Harald; Mudde, Sebastiaan.
In: Intelligent Systems in Accounting, Finance and Management, Vol. 32, No. 1, e70001, 31.03.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Howard, C, Lohre, H & Mudde, S 2025, 'Causal Network Representations in Factor Investing', Intelligent Systems in Accounting, Finance and Management, vol. 32, no. 1, e70001. https://doi.org/10.1002/isaf.70001

APA

Howard, C., Lohre, H., & Mudde, S. (2025). Causal Network Representations in Factor Investing. Intelligent Systems in Accounting, Finance and Management, 32(1), Article e70001. https://doi.org/10.1002/isaf.70001

Vancouver

Howard C, Lohre H, Mudde S. Causal Network Representations in Factor Investing. Intelligent Systems in Accounting, Finance and Management. 2025 Mar 31;32(1):e70001. Epub 2025 Mar 1. doi: 10.1002/isaf.70001

Author

Howard, Clint ; Lohre, Harald ; Mudde, Sebastiaan. / Causal Network Representations in Factor Investing. In: Intelligent Systems in Accounting, Finance and Management. 2025 ; Vol. 32, No. 1.

Bibtex

@article{76bd91f030b1419296253978fb9888b2,
title = "Causal Network Representations in Factor Investing",
abstract = "This paper explores the application of causal discovery algorithms to factor investing, addressing recent criticisms of correlation‐based models. We create novel causal network representations of the S&P 500 universe and apply them to three investment scenarios. Our findings suggest that causal approaches can complement traditional methods in areas such as stock peer group identification, factor construction, and market timing. While causal networks offer new insights and sometimes outperform correlation‐based methods in terms of risk‐adjusted returns, they do not consistently surpass traditional approaches. The causal method though shows promise in identifying unique market relationships and potential hedging opportunities. However, its practical implementation presents challenges due to computational complexity and interpretation difficulties. Our study demonstrates the potential value of causal discovery in factor investing, while also identifying areas for further research and refinement.",
keywords = "financial networks, asset pricing, causal discovery, factor investing, market timing",
author = "Clint Howard and Harald Lohre and Sebastiaan Mudde",
year = "2025",
month = mar,
day = "31",
doi = "10.1002/isaf.70001",
language = "English",
volume = "32",
journal = "Intelligent Systems in Accounting, Finance and Management",
issn = "1055-615X",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Causal Network Representations in Factor Investing

AU - Howard, Clint

AU - Lohre, Harald

AU - Mudde, Sebastiaan

PY - 2025/3/31

Y1 - 2025/3/31

N2 - This paper explores the application of causal discovery algorithms to factor investing, addressing recent criticisms of correlation‐based models. We create novel causal network representations of the S&P 500 universe and apply them to three investment scenarios. Our findings suggest that causal approaches can complement traditional methods in areas such as stock peer group identification, factor construction, and market timing. While causal networks offer new insights and sometimes outperform correlation‐based methods in terms of risk‐adjusted returns, they do not consistently surpass traditional approaches. The causal method though shows promise in identifying unique market relationships and potential hedging opportunities. However, its practical implementation presents challenges due to computational complexity and interpretation difficulties. Our study demonstrates the potential value of causal discovery in factor investing, while also identifying areas for further research and refinement.

AB - This paper explores the application of causal discovery algorithms to factor investing, addressing recent criticisms of correlation‐based models. We create novel causal network representations of the S&P 500 universe and apply them to three investment scenarios. Our findings suggest that causal approaches can complement traditional methods in areas such as stock peer group identification, factor construction, and market timing. While causal networks offer new insights and sometimes outperform correlation‐based methods in terms of risk‐adjusted returns, they do not consistently surpass traditional approaches. The causal method though shows promise in identifying unique market relationships and potential hedging opportunities. However, its practical implementation presents challenges due to computational complexity and interpretation difficulties. Our study demonstrates the potential value of causal discovery in factor investing, while also identifying areas for further research and refinement.

KW - financial networks

KW - asset pricing

KW - causal discovery

KW - factor investing

KW - market timing

U2 - 10.1002/isaf.70001

DO - 10.1002/isaf.70001

M3 - Journal article

VL - 32

JO - Intelligent Systems in Accounting, Finance and Management

JF - Intelligent Systems in Accounting, Finance and Management

SN - 1055-615X

IS - 1

M1 - e70001

ER -