Yeşil Tahvil Fiyat Tahmini: Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Analizi

Öz

Today, green financial products have garnered recognition and are consequently regarded as alternative assets. Green bonds exclusively allocate their funds to environmentally sustainable initiatives. Green bonds facilitate companies in enhancing both their financial and environmental performance by promoting innovations stemming from green initiatives and long-term green investments. This study focused on predicting the price of the green bond index in Japan. The input factors for price prediction in Japan include Nikkei225, USD/JPY, and crude oil price assets, which are seen as alternative investment options for Japanese investors. The study utilized a dataset spanning 693 days, from 06.05.2021 to 02.05.2024. The acquired data is partitioned into two distinct sets: one for training and one for testing. 80% of the data was allocated for training purposes, while the remaining 20% was designated for testing. The study utilized various prediction approaches including RF, MLP, GBR, XGBoost, LSTM, SVR, Catboost, and Linear Regression. The performance of these models was compared using evaluation metrics such as MSE, RMSE, MAE, MAPE, and R2 values. The research revealed that the GBR model exhibited the highest performance on the training data set, whereas the XGBoost and RF models yielded superior prediction results on the test data set.

Anahtar Kelimeler

• Yeşil tahviller
• Zaman serisi analizi
• Finansal Tahminleme
• Makine Öğrenmesi
• Karar Destek.

GREEN BOND INDEX PRICE FORECASTING: COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS

Öz

Today, green financial products have garnered recognition and are consequently regarded as alternative assets. Green bonds exclusively allocate their funds to environmentally sustainable initiatives. Green bonds facilitate companies in enhancing both their financial and environmental performance by promoting innovations stemming from green initiatives and long-term green investments. This study focused on predicting the price of the green bond index in Japan. The input factors for price prediction in Japan include Nikkei225, USD/JPY, and crude oil price assets, which are seen as alternative investment options for Japanese investors. The study utilized a dataset spanning 693 days, from 06.05.2021 to 02.05.2024. The acquired data is partitioned into two distinct sets: one for training and one for testing. 80% of the data was allocated for training purposes, while the remaining 20% was designated for testing. The study utilized various prediction approaches including RF, MLP, GBR, XGBoost, LSTM, SVR, Catboost, and Linear Regression. The performance of these models was compared using evaluation metrics such as MSE, RMSE, MAE, MAPE, and R2 values. The research revealed that the GBR model exhibited the highest performance on the training data set, whereas the XGBoost and RF models yielded superior prediction results on the test data set.

Anahtar Kelimeler

• Green bonds
• Time series analysis
• Financial Forecasting
• Machine Learning
• Decision Support.

Kaynakça

1. Adekoya, O. B., Abakah, E. J., & Oliyide, J. A. (2023). Factors behind the performance of green bond markets. International Review of Economics & Finance, 88, 92-106.
2. Adekoya, O. B., Abakah, E. J., & Oliyide, J. A. (2023). Factors behind the performance of green bond markets. International Review of Economics & Finance, 88, 92-106.
3. Bachelet, M. J., Becchetti, L., & Manfredonia, S. (2019). The green bonds premium puzzle: The role of issuer characteristics and third-party verification. Sustainability, 11(4), 1098.
4. Bachelet, M. J., Becchetti, L., & Manfredonia, S. (2019). The green bonds premium puzzle: The role of issuer characteristics and third-party verification. Sustainability, 11(4), 1098.
5. Chatziantoniou, I., Abakah, E. J. A., Gabauer, D., & Tiwari, A. K. (2022). Quantile time–frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production, 361, 132088.
6. Chatziantoniou, I., Abakah, E. J. A., Gabauer, D., & Tiwari, A. K. (2022). Quantile time–frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production, 361, 132088.
7. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2939.672.2939785
8. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2939.672.2939785

9. Climate Bonds Initiative Report, (2024a). https://www.climatebonds.net/resources/reports/quarterly-marketupdate-q1-2024, Access Date:03.08.2024
10. Climate Bonds Initiative Report, (2024a). https://www.climatebonds.net/resources/reports/quarterly-marketupdate-q1-2024, Access Date:03.08.2024
11. Çetin, D. T. (2022). Green bonds in climate finance and forecasting of corporate green bond index value with artificial intelligence. Journal of Research in Business, 7(1), 138-157.
12. Çetin, D. T. (2022). Green bonds in climate finance and forecasting of corporate green bond index value with artificial intelligence. Journal of Research in Business, 7(1), 138-157.
13. Dumlu, T., & Keleş, E. (2023). Kurumsal Yeşil Tahviller ve Firma Değeri: Türkiye Uygulamaları. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 8(1), 261-269. Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499-516.
14. Dumlu, T., & Keleş, E. (2023). Kurumsal Yeşil Tahviller ve Firma Değeri: Türkiye Uygulamaları. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 8(1), 261-269. Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499-516.
15. Ghosh, I., Alfaro-Cortés, E., Gámez, M., & García-Rubio, N. (2023). Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI. International Review of Financial Analysis, 87, 102558.
16. Ghosh, I., Alfaro-Cortés, E., Gámez, M., & García-Rubio, N. (2023). Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI. International Review of Financial Analysis, 87, 102558.
17. Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, 120346.
18. Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, 120346.
19. Güneş, H. (2023). Yeşil tahvil ve ülke tahvilleri arasındaki nedensellik. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 25(45), 1244-1263.
20. Güneş, H. (2023). Yeşil tahvil ve ülke tahvilleri arasındaki nedensellik. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 25(45), 1244-1263.
21. Gyamerah, S. A., & Asare, C. (2024). The impacts of global economic policy uncertainty on green bond returns: A systematic literature review. Heliyon, 10(3), e25076. https://doi.org/10.1016/j.heliyon.2024.e25076
22. Gyamerah, S. A., & Asare, C. (2024). The impacts of global economic policy uncertainty on green bond returns: A systematic literature review. Heliyon, 10(3), e25076. https://doi.org/10.1016/j.heliyon.2024.e25076
23. Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds?. Journal of Asset Management, 19(6), 371-383.
24. Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds?. Journal of Asset Management, 19(6), 371-383.
25. Han, Y., Kim, J., & Enke, D. (2023). A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost. Expert Systems with Applications, 211, 118581.
26. Han, Y., Kim, J., & Enke, D. (2023). A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost. Expert Systems with Applications, 211, 118581.
27. ICMA (International Capital Market Association) (2021). Sürdürülebilirlik Tahvili Rehberleri https://www.icmagroup.org/assets/documents/Regulatory/Green-Bonds/Translations/2021/Turkish-SBG-2021.pdf?vid=2. . Access.Date:07.05.2024
28. ICMA (International Capital Market Association) (2021). Sürdürülebilirlik Tahvili Rehberleri https://www.icmagroup.org/assets/documents/Regulatory/Green-Bonds/Translations/2021/Turkish-SBG-2021.pdf?vid=2. . Access.Date:07.05.2024
29. Jabeur, S. B., Mefteh-Wali, S., & Viviani, J. L. (2024). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 334(1), 679-699.
30. Jabeur, S. B., Mefteh-Wali, S., & Viviani, J. L. (2024). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 334(1), 679-699.
31. Kocaarslan, B., & Soytas, U. (2023). The role of major markets in predicting the US municipal green bond market performance: New evidence from machine learning models. Technological Forecasting and Social Change, 196, 122820.
32. Kocaarslan, B., & Soytas, U. (2023). The role of major markets in predicting the US municipal green bond market performance: New evidence from machine learning models. Technological Forecasting and Social Change, 196, 122820.
33. Kocaarslan, B., & Mushtaq, R. (2024). The impact of liquidity conditions on the time-varying link between US municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach. Energy Policy, 184, 113911.
34. Kocaarslan, B., & Mushtaq, R. (2024). The impact of liquidity conditions on the time-varying link between US municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach. Energy Policy, 184, 113911.
35. Kurtoğlu, S., & Çakan, C. D. (2022). Sürdürülebilir Ekonomide Yeşil Tahvil ve Bir Türkiye Örneği. International Journal of Disciplines in Economics & Administrative Sciences Studies, 8(44), 523-534.
36. Kurtoğlu, S., & Çakan, C. D. (2022). Sürdürülebilir Ekonomide Yeşil Tahvil ve Bir Türkiye Örneği. International Journal of Disciplines in Economics & Administrative Sciences Studies, 8(44), 523-534.
37. Liu, F., Qin, C., Qin, M., Stefea, P., & Norena-Chavez, D. (2024). Geopolitical risk: An opportunity or a threat to the green bond market?. Energy Economics, 107391.
38. Liu, F., Qin, C., Qin, M., Stefea, P., & Norena-Chavez, D. (2024). Geopolitical risk: An opportunity or a threat to the green bond market?. Energy Economics, 107391.
39. Liu, M., & Ying, Q. (2023). The role of online news sentiment in carbon price prediction of China’s carbon markets. Environmental Science and Pollution Research, 30(14), 41379-41387.
40. Liu, M., & Ying, Q. (2023). The role of online news sentiment in carbon price prediction of China’s carbon markets. Environmental Science and Pollution Research, 30(14), 41379-41387.
41. Liu, Y., Huang, H., Mbanyele, W., Wang, F., & Liu, H. (2024). Does the issuance of green bonds nudge environmental responsibility engagements? Evidence from the Chinese green bond market. Financial Innovation, 10(1), 92.
42. Liu, Y., Huang, H., Mbanyele, W., Wang, F., & Liu, H. (2024). Does the issuance of green bonds nudge environmental responsibility engagements? Evidence from the Chinese green bond market. Financial Innovation, 10(1), 92.
43. Lukšić, I., Bošković, B., Novikova, A., & diğerleri. (2022). Innovative financing of the sustainable development goals in the countries of the Western Balkans. Energy, Sustainability and Society, 12(15). https://doi.org/10.1186/s13705.022.00340-w
44. Lukšić, I., Bošković, B., Novikova, A., & diğerleri. (2022). Innovative financing of the sustainable development goals in the countries of the Western Balkans. Energy, Sustainability and Society, 12(15). https://doi.org/10.1186/s13705.022.00340-w
45. Md, A. Q., Kapoor, S., AV, C. J., Sivaraman, A. K., Tee, K. F., Sabireen, H., & Janakiraman, N. (2023). Novel optimization approach for stock price forecasting using multi-layered sequential LSTM. Applied Soft Computing, 134, 109830.
46. Md, A. Q., Kapoor, S., AV, C. J., Sivaraman, A. K., Tee, K. F., Sabireen, H., & Janakiraman, N. (2023). Novel optimization approach for stock price forecasting using multi-layered sequential LSTM. Applied Soft Computing, 134, 109830.
47. Mendoza, C., Kristjanpoller, W., & Minutolo, M. C. (2023). Market index price prediction using Deep Neural Networks with a Self-Similarity approach. Applied Soft Computing, 146, 110700.
48. Mendoza, C., Kristjanpoller, W., & Minutolo, M. C. (2023). Market index price prediction using Deep Neural Networks with a Self-Similarity approach. Applied Soft Computing, 146, 110700.
49. Nadirgil, O. (2023). Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. Journal of Environmental Management, 342, 118061.
50. Nadirgil, O. (2023). Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. Journal of Environmental Management, 342, 118061.
51. Nie, Q., Chen, S., Chen, Y., & Hu, Y. (2023). Integrated prediction of green bond return under the dual risks of climate change and energy crisis. Frontiers in Environmental Science, 11, 1336867. Nikko Asset Management (nikko am), (2024). https://en.nikkoam.com/ . Access.Date:08.05.2024
52. Nie, Q., Chen, S., Chen, Y., & Hu, Y. (2023). Integrated prediction of green bond return under the dual risks of climate change and energy crisis. Frontiers in Environmental Science, 11, 1336867. Nikko Asset Management (nikko am), (2024). https://en.nikkoam.com/ . Access.Date:08.05.2024
53. Patel, T., & Sahu, J. (2023, October). Comparative analysis of regression techniques for accurate house price prediction using machine learning: a statistical analysis method. In 2023 IEEE International Conferenceon Blockchain and Distributed Systems Security (ICBDS) (pp. 1-6). IEEE.
54. Patel, T., & Sahu, J. (2023, October). Comparative analysis of regression techniques for accurate house price prediction using machine learning: a statistical analysis method. In 2023 IEEE International Conferenceon Blockchain and Distributed Systems Security (ICBDS) (pp. 1-6). IEEE.
55. Piñeiro-Chousa, J., López-Cabarcos, M. Á., Caby, J., & Šević, A. (2021). The influence of investor sentiment on the green bond market. Technological Forecasting and Social Change, 162, 120351.
56. Piñeiro-Chousa, J., López-Cabarcos, M. Á., Caby, J., & Šević, A. (2021). The influence of investor sentiment on the green bond market. Technological Forecasting and Social Change, 162, 120351.
57. Rastgoo, A., & Khajavi, H. (2023). A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of catboost and arithmetic optimization algorithm. Expert Systems with Applications, 229, 120576.
58. Rastgoo, A., & Khajavi, H. (2023). A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of catboost and arithmetic optimization algorithm. Expert Systems with Applications, 229, 120576.
59. T.C. Enerji ve Tabii Kaynaklar Bakanlığı. (2024). Elektrik üretiminde yenilenebilir enerji kaynaklarının payı arttı. April 1, 2024, Retrivied from https://enerji.gov.tr/haber-detay?id=21270 adresinden erişildi.
60. T.C. Enerji ve Tabii Kaynaklar Bakanlığı. (2024). Elektrik üretiminde yenilenebilir enerji kaynaklarının payı arttı. April 1, 2024, Retrivied from https://enerji.gov.tr/haber-detay?id=21270 adresinden erişildi.
61. Turan, N. (2022). Yeni bir borç enstrumani olarak yeşil tahviller: global piyasalar ve türkiye’deki mevcut durum üzerine tespitler. Sayıştay Dergisi, 33(125), 311-341.
62. Turan, N. (2022). Yeni bir borç enstrumani olarak yeşil tahviller: global piyasalar ve türkiye’deki mevcut durum üzerine tespitler. Sayıştay Dergisi, 33(125), 311-341.
63. Turguttopbaş, N. (2020). Sürdürülebilirlik, yeşil finans ve ilk türk yeşil tahvil ihraci. Finansal Araştırmalar ve Çalışmalar Dergisi, 12(22), 267-283.
64. Turguttopbaş, N. (2020). Sürdürülebilirlik, yeşil finans ve ilk türk yeşil tahvil ihraci. Finansal Araştırmalar ve Çalışmalar Dergisi, 12(22), 267-283.
65. Umar, Z., Hadhri, S., Abakah, E. J. A., Usman, M., & Umar, M. (2024). Return and volatility spillovers among oil price shocks and international green bond markets. Research in International Business and Finance, 69, 02254.
66. Umar, Z., Hadhri, S., Abakah, E. J. A., Usman, M., & Umar, M. (2024). Return and volatility spillovers among oil price shocks and international green bond markets. Research in International Business and Finance, 69, 02254.
67. Wang, X., Gao, S., Zhou, S., Guo, Y., Duan, Y., & Wu, D. (2021). Prediction of house price index based on bagging integrated WOA-SVR model. Mathematical Problems in Engineering, 2021, 1-15.
68. Wang, X., Gao, S., Zhou, S., Guo, Y., Duan, Y., & Wu, D. (2021). Prediction of house price index based on bagging integrated WOA-SVR model. Mathematical Problems in Engineering, 2021, 1-15.
69. Wang J., Tang J. & Guo K. (2022). Green bond index prediction based on CEEMDAN-LSTM. Front. Energy Res. 9:793413. doi: 10.3389/fenrg.2021.793413
70. Wang J., Tang J. & Guo K. (2022). Green bond index prediction based on CEEMDAN-LSTM. Front. Energy Res. 9:793413. doi: 10.3389/fenrg.2021.793413
71. Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent advances and pitfalls. Digital Finance, 4, 63–88. https://doi.org/10.1007/s42521.021.00046-2
72. Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent advances and pitfalls. Digital Finance, 4, 63–88. https://doi.org/10.1007/s42521.021.00046-2
73. Xie, Q., Hao, J., Li, J., & Zheng, X. (2022). Carbon price prediction considering climate change: A text-based framework. Economic Analysis and Policy, 74, 382-401.
74. Xie, Q., Hao, J., Li, J., & Zheng, X. (2022). Carbon price prediction considering climate change: A text-based framework. Economic Analysis and Policy, 74, 382-401.
75. Yağcılar, G. G., & Yilmaz, F. (2022). Yeşil tahvil ihraci duyurularina pay senedi yatirimcilarinin tepkisi: türkiye’deki bankalarin yeşil tahvil ihraçlarina ilişkin olay çalişmasi. Journal of Business Innovation and Governance, 5(2), 147-162.
76. Yağcılar, G. G., & Yilmaz, F. (2022). Yeşil tahvil ihraci duyurularina pay senedi yatirimcilarinin tepkisi: türkiye’deki bankalarin yeşil tahvil ihraçlarina ilişkin olay çalişmasi. Journal of Business Innovation and Governance, 5(2), 147-162.
77. Zerbib, O. D. (2019). The effect of pro-environmental preferences on bond prices: Evidence from green bonds. Journal of banking & finance, 98, 39-60.
78. Zerbib, O. D. (2019). The effect of pro-environmental preferences on bond prices: Evidence from green bonds. Journal of banking & finance, 98, 39-60.
79. Zhang, Y., & Umair, M. (2023). Examining the interconnectedness of green finance: an analysis of dynamic spillover effects among green bonds, renewable energy, and carbon markets. Environmental Science and Pollution Research, 30(31), 77605-77621.
80. Zhang, Y., & Umair, M. (2023). Examining the interconnectedness of green finance: an analysis of dynamic spillover effects among green bonds, renewable energy, and carbon markets. Environmental Science and Pollution Research, 30(31), 77605-77621.
81. Zhang, Y., Li, Y., & Chen, X. (2024). Does green bond issuance affect stock price crash risk? Evidence from China. Finance Research Letters, 60, 104908.
82. Zhang, Y., Li, Y., & Chen, X. (2024). Does green bond issuance affect stock price crash risk? Evidence from China. Finance Research Letters, 60, 104908.
83. Zheng, J., Wang, Y., Li, S., & Chen, H. (2021). The stock index prediction based on SVR model with bat optimization algorithm. Algorithms, 14(10), 299.
84. Zheng, J., Wang, Y., Li, S., & Chen, H. (2021). The stock index prediction based on SVR model with bat optimization algorithm. Algorithms, 14(10), 299.
85. Zheng, Y., Luo, J., Chen, J., Chen, Z., & Shang, P. (2023). Natural gas spot price prediction research under the background of Russia-Ukraine conflict-based on FS-GA-SVR hybrid model. Journal of Environmental Management, 344, 118446.
86. Zheng, Y., Luo, J., Chen, J., Chen, Z., & Shang, P. (2023). Natural gas spot price prediction research under the background of Russia-Ukraine conflict-based on FS-GA-SVR hybrid model. Journal of Environmental Management, 344, 118446.
87. Zhou, X., & Cui, Y. (2019). Green bonds, corporate performance, and corporate social responsibility. Sustainability, 11(23), 6881.
88. Zhou, X., & Cui, Y. (2019). Green bonds, corporate performance, and corporate social responsibility. Sustainability, 11(23), 6881.