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| Title: | 使用半監督學習預測範疇三碳排建構投資組合之分析
Predicted Scope 3 Carbon Emissions Portfolios Analysis: Semi-supervised Learning Approach |
| Authors: | 閻家緯
Yen, Chia Wei |
| Contributors: | 楊曉文
Yang, Sharon S.
閻家緯
Yen, Chia Wei |
| Keywords: | 半監督學習
範疇三碳排
碳密度策略
績效回測
Semi-supervised Learning
Scope 3 Carbon Emissions
Portfolio Analysis |
| Date: | 2023 |
| Issue Date: | 2023-08-02 14:11:49 (UTC+8) |
| Abstract: | 本研究旨在使用半監督式學習 COREG 模型，以公司的財報資料當作解釋 變數預測範疇三碳排，並使用碳密度當作因子，建構出具有高碳和低碳性質的 投資組合進行績效回溯。本研究的第一部份比較使用隨機森林和 COREG 模型 在預測範疇三碳排上的解釋力，資料母體為台灣 2010 年至 2021 年中有揭露範 疇一和範疇二碳排的上市櫃公司，解釋變數使用公司每一年公開揭露的財報資 料，在預測期間的 2016 年至 2021 年間發現，在每年平均絕對誤差(MAE)相 同的情況下，COREG 模型的均方根對數誤差(RMSLE)平均每年相對於隨機 森林模型減少約40%，顯示隨機森林模型傾向低估碳排放，且 COREG 模型 在捕捉尾端碳排時能有比較優異的表現。在第二部分裡，本研究進一步利用碳 密度當作篩選因子，建立低碳和高碳密度的投資組合進行回溯測試，在回測期 間的 2018 年至 2022 年之間，主要有以下兩點發現，第一，使用直接碳排建構 的碳密度投資組合有低碳高報酬現象;而使用間接碳排建構的碳密度投資組合 則有高碳高報酬現象。第二，使用已揭露碳排建構的碳密度投資組合之夏普比 率皆無法打敗大盤;而使用 COREG 模型預測的範疇三碳排所建立的投資組 合，除了呈現高碳至低碳的排序性之外，這之中的前 10% 碳密度投資組合不 僅在報酬上勝過台灣加權報酬指數，其夏普比率也是唯一一組在回測期間勝過 報酬指數的碳密度投資組合。
The study proposes the use of semi-supervised learning COREG model to predict scope 3 carbon emissions using company financial data as explanatory variables. Also, we conduct backtesting to evaluate the performance of high and low carbon density portfolios. In the first part, we compare the performance of random forest and COREG models in predicting scope 3 carbon emissions. The dataset comprises listed and over-the-counter (OTC) companies in Taiwan from 2010 to 2021 that have disclosed both scope 1 and scope 2 carbon emissions. The explanatory variables consist of financial data disclosed by these companies. Results show that the COREG model achieves an average reduction of approximately 40% in RMSLE compared to the random forest model, with same MAE each year. This indicates that the random forest model tends to underestimate carbon emissions, while the COREG model performs better in capturing extreme carbon emissions. In the second part, we construct backtesing for low and high carbon density portfolio. From the period 2018 to 2022, firstly, we found portfolios constructed based on direct carbon emissions show negative relationship between carbon density and returns, while portfolios constructed based on indirect carbon emissions show a positive relationship between them. Second, portfolios constructed using disclosed carbon emissions failed to outperform the TAIEX Total Return Index in terms of Sharpe ratio. However, we found the predicted scope 3 carbon density portfolios exhibited a high-carbon-to-low- carbon returns pattern. Furthermore, the top 10% carbon density portfolio had the highest returns and Sharpe ratio among all the portfolios including market benchmark. |
| Reference: | 1. Zhou, P., Ang, B. W., & Han, J. Y. (2010). Total factor carbon emission performance: a Malmquist index analysis. Energy Economics, 32(1), 194-201.
2. Choi, B. B., Lee, D., & Psaros, J. (2013). An analysis of Australian company carbon emission disclosures. Pacific Accounting Review.
3. Kılıç, M., & Kuzey, C. (2018). The effect of corporate governance on carbon emission disclosures: Evidence from Turkey. International Journal of Climate Change Strategies and Management.
4. Zhang, Y. J. (2011). The impact of financial development on carbon emissions: An empirical analysis in China. Energy policy, 39(4), 2197-2203.
5. Acheampong, A. O., Amponsah, M., & Boateng, E. (2020). Does financial development mitigate carbon emissions? Evidence from heterogeneous financial economies. Energy Economics, 88, 104768.
6. Lee, O., Joo, H., Choi, H., & Cheon, M. (2022). Proposing an integrated approach to analyzing ESG data via machine learning and deep learning algorithms. Sustainability, 14(14), 8745.
7. Zhao, Y., Liu, R., Liu, Z., Liu, L., Wang, J., & Liu, W. (2023). A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning. Sustainability, 15(8), 6876.
8. Assael, J., Heurtebize, T., Carlier, L., & Soupé, F. (2023). Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning. Sustainability, 15(4), 3391.
9. Nguyen, Q., Diaz-Rainey, I., & Kuruppuarachchi, D. (2021). Predicting corporate carbon footprints for climate finance risk analyses: a machine learning approach. Energy Economics, 95, 105129.
10. Nguyen, Q., Diaz-Rainey, I., Kitto, A., McNeil, B., Pittman, N., & Zhang, R. (2022). Scope 3 Emissions: Data Quality and Machine Learning Prediction Accuracy
11. Serafeim, G., & Velez Caicedo, G. (2022). Machine Learning Models for Prediction of Scope 3 Carbon Emissions. Available at SSRN.
12. Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700.
13. De Lucia, C., Pazienza, P., & Bartlett, M. (2020). Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability, 12(13), 5317.
14. Chen, Q., & Liu, X. Y. (2020, October). Quantifying ESG alpha using scholar big data: an automated machine learning approach. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1-8).
15. De Franco, C., Geissler, C., Margot, V., & Monnier, B. (2020). Esg investments: Filtering versus machine learning approaches. arXiv preprint arXiv:2002.07477.
16. Bolton, P., & Kacperczyk, M. (2021). Do investors care about carbon risk?. Journal of financial economics, 142(2), 517-549.
17. Jacobsen, B., Lee, W., & Ma, C. (2019). The alpha, beta, and sigma of ESG: Better beta, additional alpha?. Journal of Portfolio Management, 45(6), 6-15.
18. Misani, N., & Pogutz, S. (2015). Unraveling the effects of environmental outcomes and processes on financial performance: A non-linear approach. Ecological economics, 109, 150-160.
19. Guo, T., Jamet, N., Betrix, V., Piquet, L. A., & Hauptmann, E. (2020). Esg2risk: A deep learning framework from esg news to stock volatility prediction. arXiv preprint arXiv:2005.02527.
20. Sokolov, A., Caverly, K., Mostovoy, J., Fahoum, T., & Seco, L. (2021). Weak supervision and black-litterman for automated esg portfolio construction. The Journal of Financial Data Science, 3(3), 129-138.
21. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
22. Zhou, Z. H., & Li, M. (July). Semi-supervised regression with co-training. In IJCAI (Vol. 5, pp. 908-913). |
| Description: | 碩士
國立政治大學
金融學系
110352033 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110352033 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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