References
[1] . Acemoglu, D., Carvalho, V. M., Ozdaglar, A., and Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623
[2] . Acemoglu, D., Ozdaglar, A., and Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. https://doi.org/10.1257/aer.20130456
[3] . Acharya, V. V., Berner, R., Engle, R., Jung, H., Stroebel, J., Zeng, X., and Zhao, Y. (2023). Climate stress testing. Annual Review of Financial Economics, 15, 291–326. https://doi.org/10.1146/annurev-financial-110821-022532
[4] . Alogoskoufis, S., Dunz, N., Emambakhsh, T., Hennig, T., Kaijser, M., Kouratzoglou, C., Muñoz, M. A., Parisi, L., and Salleo, C. (2021). ECB economy-wide climate stress test. ECB Occasional Paper No. 281. European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpops/ecb.op281~05a7735b1c.en.pdf
[5] . Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
[6] . Altman, E. I. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, 6(4), 311–323. https://doi.org/10.1016/j.ememar.2005.09.007
[7] . Aroussi, R. (2026). yfinance: Download market data from Yahoo! Finance's API. Software documentation. https://github.com/ranaroussi/yfinance
[8] . Aswani, J., Raghunandan, A., and Rajgopal, S. (2024). Are carbon emissions associated with stock returns? Review of Finance, 28(1), 75–106. https://doi.org/10.1093/rof/rfad013
[9] . Bank of England. (2022). Results of the 2021 Climate Biennial Exploratory Scenario. https://www.bankofengland.co.uk/stress-testing/2022/results-of-the-2021-climate-biennial-exploratory-scenario
[10] . Barrot, J.-N., and Sauvagnat, J. (2016). Input specificity and the propagation of idiosyncratic shocks in production networks. Quarterly Journal of Economics, 131(3), 1543–1592. https://doi.org/10.1093/qje/qjw018
[11] . Battiston, S., Mandel, A., Monasterolo, I., Schütze, F., and Visentin, G. (2017). A climate stress-test of the financial system. Nature Climate Change, 7, 283–288. https://doi.org/10.1038/nclimate3255
[12] . Bolton, P., and Kacperczyk, M. (2021). Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517–549. https://doi.org/10.1016/j.jfineco.2021.05.008
[13] . Bronstein, M. M., Bruna, J., Cohen, T., and Veli?kovi?, P. (2021). Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv. https://doi.org/10.48550/arXiv.2104.13478
[14] . Carvalho, V. M. (2014). From micro to macro via production networks. Journal of Economic Perspectives, 28(4), 23–48. https://doi.org/10.1257/jep.28.4.23
[15] . Carvalho, V. M., Nirei, M., Saito, Y. U., and Tahbaz-Salehi, A. (2021). Supply chain disruptions: Evidence from the Great East Japan earthquake. Quarterly Journal of Economics, 136(2), 1255–1321. https://doi.org/10.1093/qje/qjaa044
[16] . Dietz, S., Bowen, A., Dixon, C., and Gradwell, P. (2016). Climate value at risk of global financial assets. Nature Climate Change, 6, 676–679. https://doi.org/10.1038/nclimate2972
[17] . Financial Stability Board. (2020). The implications of climate change for financial stability. https://www.fsb.org/2020/11/the-implications-of-climate-change-for-financial-stability/
[18] . Giglio, S., Kelly, B., and Stroebel, J. (2021). Climate finance. Annual Review of Financial Economics, 13, 15–36. https://doi.org/10.1146/annurev-financial-102620-103311
[19] . Hamilton, W. L., Ying, R., and Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html
[20] . Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2
[21] . Hong, H., Karolyi, G. A., and Scheinkman, J. A. (2020). Climate finance. Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.1093/rfs/hhz146
[22] . Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science and Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
[23] . Inoue, H., and Todo, Y. (2019a). Firm-level propagation of shocks through supply-chain networks. Nature Sustainability, 2, 841–847. https://doi.org/10.1038/s41893-019-0351-x
[24] . Inoue, H., and Todo, Y. (2019b). Propagation of negative shocks across nation-wide firm networks. PLOS ONE, 14(3), e0213648. https://doi.org/10.1371/journal.pone.0213648
[25] . Inoue, H., and Todo, Y. (2020). The propagation of the economic impact through supply chains: The case of a mega-city lockdown against the spread of COVID-19. PLOS ONE, 15(9), e0239251. https://doi.org/10.1371/journal.pone.0239251
[26] . Intergovernmental Panel on Climate Change. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Cambridge University Press. https://doi.org/10.1017/9781009325844
[27] . Kinnan, C., Wang, S.-Y., Wang, Y., and Zhang, Y. (2024). The economic impacts of climate risk: Evidence from Chinese firms. American Economic Review, 114. https://www.aeaweb.org/articles?id=10.1257/aer.20220892
[28] . Kipf, T. N., and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations. https://arxiv.org/abs/1609.02907
[29] . Li, Q., Shan, H., Tang, Y., and Yao, V. (2024). Corporate climate risk: Measurements and responses. Review of Financial Studies, 37(6), 1778–1830. https://doi.org/10.1093/rfs/hhad094
[30] . McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
[31] . Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29(2), 449–470. https://doi.org/10.2307/2978814
[32] . Network for Greening the Financial System. (2024a). NGFS long-term climate scenarios for central banks and supervisors. Phase V. https://www.ngfs.net/ngfs-scenarios-portal/
[33] . Network for Greening the Financial System. (2024b). NGFS scenarios technical documentation. Phase V. https://www.ngfs.net/ngfs-scenarios-portal/
[34] . Newman, M. E. J. (2018). Networks (2nd ed.). Oxford University Press. https://doi.org/10.1093/oso/9780198805090.001.0001
[35] . Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
[36] . Paszke, A., Gross, S., Massa, F., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html
[37] . Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://jmlr.org/papers/v12/pedregosa11a.html
[38] . Ranger, N., Mahul, O., and Monasterolo, I. (2022). Assessing financial risks from physical climate shocks: A framework for scenario generation. World Bank Policy Research Working Paper 9995. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/470581648469518534/
[39] . Schuermann, T. (2014). Stress testing banks. International Journal of Forecasting, 30(3), 717–728. https://doi.org/10.1016/j.ijforecast.2013.10.003
[40] . Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S. (2018). Pitfalls of graph neural network evaluation. Relational Representation Learning Workshop, NeurIPS. https://arxiv.org/abs/1811.05868
[41] . Stroebel, J., and Wurgler, J. (2021). What do you think about climate finance? Journal of Financial Economics, 142(2), 487–498. https://doi.org/10.1016/j.jfineco.2021.08.004
[42] . Task Force on Climate-related Financial Disclosures. (2017). Recommendations of the Task Force on Climate-related Financial Disclosures. Financial Stability Board. https://www.fsb-tcfd.org/recommendations/
[43] . Tokui, J., Kawasaki, K., and Miyagawa, T. (2017). The economic impact of supply chain disruptions from the Great East-Japan earthquake. Japan and the World Economy, 41, 59–70. https://doi.org/10.1016/j.japwor.2016.12.005
[44] . U.S. Securities and Exchange Commission. (2025a). EDGAR application programming interfaces. https://www.sec.gov/search-filings/edgar-application-programming-interfaces
[45] . U.S. Securities and Exchange Commission. (2025b). Inline XBRL. https://www.sec.gov/structureddata/osd-inline-xbrl.html
[46] . Veli?kovi?, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018). Graph attention networks. International Conference on Learning Representations. https://arxiv.org/abs/1710.10903
[47] . World Economic Forum. (2024). The Global Risks Report 2024 (19th ed.). https://www.weforum.org/publications/global-risks-report-2024/
[48] . Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386
[49] . Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2019). How powerful are graph neural networks? International Conference on Learning Representations. https://arxiv.org/abs/1810.00826
[50] . Zhou, J., Cui, G., Hu, S., et al. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001