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Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains

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  • Berger, Theo

Abstract

We provide an innovative application of explainable artificial intelligence to economic panel data. We apply boosted trees in combination with Shapley values to achieve post-model explanations. As a benchmark, we assess a pooled regression approach to discuss the economic information content of interpretable machine learning.

Suggested Citation

  • Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323001307
    DOI: 10.1016/j.frl.2023.103757
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Eugene F. Fama & Kenneth R. French, 2008. "Dissecting Anomalies," Journal of Finance, American Finance Association, vol. 63(4), pages 1653-1678, August.
    4. Gençay, Ramazan & Signori, Daniele & Xue, Yi & Yu, Xiao & Zhang, Keyi, 2015. "Economic links and credit spreads," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 157-169.
    5. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    7. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
    8. Kewei Hou & Chen Xue & Lu Zhang, 2020. "Replicating Anomalies," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2019-2133.
    9. Lin, Boqiang & Bai, Rui, 2022. "Machine learning approaches for explaining determinants of the debt financing in heavy-polluting enterprises," Finance Research Letters, Elsevier, vol. 44(C).
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    Cited by:

    1. Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).

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    More about this item

    Keywords

    Explainable artificial intelligence; Machine learning; Tree ensembles; Interpretable machine learning; Shapley values;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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