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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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    Cited by:

    1. Graham, Byron & Bonner, Karen, 2024. "The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach," Journal of Business Research, Elsevier, vol. 175(C).
    2. Gao, Wei & Ju, Ming & Yang, Tongyang, 2023. "Severe weather and peer-to-peer farmers’ loan default predictions: Evidence from machine learning analysis," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Zhou, Linjiang & Shi, Xiaochuan & Bao, Yaxiong & Gao, Lihua & Ma, Chao, 2023. "Explainable artificial intelligence for digital finance and consumption upgrading," Finance Research Letters, Elsevier, vol. 58(PC).
    4. 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).
    5. Zheng, Xiaxuan & Chen, Yueyan, 2024. "Does supply-chain-finance help to improve the efficiency of outward foreign direct investment?," Finance Research Letters, Elsevier, vol. 59(C).
    6. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
    7. Kovvuri, Veera Raghava Reddy & Fu, Hsuan & Fan, Xiuyi & Seisenberger, Monika, 2023. "Fund performance evaluation with explainable artificial intelligence," Finance Research Letters, Elsevier, vol. 58(PB).
    8. Adil Haniev, 2024. "Intangible Assets and US Stock Returns: An analysis using the Index Method, Panel Regression, and Machine Learning," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(3), pages 833-854.

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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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