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GREEN BONDS IN CLIMATE FINANCE AND FORECASTING OF CORPORATE GREEN BOND INDEX VALUE WITH ARTIFICIAL INTELLIGENCE

Yıl 2022, , 138 - 157, 27.06.2022

Dilşad Tülgen Çetin

https://doi.org/10.54452/jrb.992368
Cited By: 6

Öz

The effects of global climate change and increasing environmental awareness have led to an increase in the significance of climate projects and, accordingly, climate finance and green bonds. Despite the increasing significance, the fact that the price forecasting studies on green bonds are extremely scarce has been the main motivation of this study. The aim of this paper is to forecast the corporate green bond prices with the Artificial Neural Network model and to determine the predictor by addressing the conceptual framework of green bonds. For this purpose, the Multi-Layer Feedback Artificial Neural Network (MLF-ANN) model, in which S&P 500 index prices are determined as input and S&P green bond index prices as output, is designed. To determine whether the conventional bond prices are the predictor of the corporate green bonds, the S&P 500 index was used as the sole input of the forecasting model. The findings show that corporate green bond prices are forecasted with 1.13% Mean Absolute Percentage Error (MAPE) and 98.93% Regression Determination Coefficient (R2). The results of the research provide data to maximize profits and/or minimize risk for green bond investors and market makers, while providing insight into the effectiveness of green bonds in financing climate projects for policy makers. This paper is the first study in the literature in terms of proving the effectiveness of the MLF-ANN model in forecasting corporate green bonds and revealing that conventional bonds are predictor of green bonds. Thus, it is expected that the study will shed light on future studies.

Anahtar Kelimeler

Green bonds forecasting Artificial Neural Network model Predictor of green bonds Conventional bonds.

Kaynakça

  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
  • Baker, M., Bergstresser, D., Serafeim, G., & Wurgler, J. (2018). Financing the response to climate change: The pricing and ownership of US green bonds (No. 25194). National Bureau of Economic Research. https://doi.org/10.3386/w25194
  • Baker, M., Bergstresser, D., Serafeim, G., & Wurgler, J. (2018). Financing the response to climate change: The pricing and ownership of US green bonds (No. 25194). National Bureau of Economic Research. https://doi.org/10.3386/w25194
  • Broadstock, D. C., & Cheng, L. T. W. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17–22. https://doi.org/10.1016/j.frl.2019.02.006
  • Broadstock, D. C., & Cheng, L. T. W. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17–22. https://doi.org/10.1016/j.frl.2019.02.006
  • CBI. (2016). Green Bonds Highlights 2016. Retrieved January 18, 2021, from https://www.climatebonds.net/resources/reports/green-bonds-highlights-2016
  • CBI. (2016). Green Bonds Highlights 2016. Retrieved January 18, 2021, from https://www.climatebonds.net/resources/reports/green-bonds-highlights-2016
  • CBI. (2020). Green Bonds Global State of the Market 2020. Retrieved January 19, 2021, from https://www.climatebonds.net/resources/reports/sustainable-debt-global-state-market-2020
  • CBI. (2020). Green Bonds Global State of the Market 2020. Retrieved January 19, 2021, from https://www.climatebonds.net/resources/reports/sustainable-debt-global-state-market-2020
  • Çetin, D. T. (2021). Çevre dostu proje finansmanında yeşil tahvil ihracı (Green bond issuance in environmentally friendly project financing). In A. Ç. Ceylan, F. Özbay, Z. Özomay, & M. B. Kurt (Eds.), Sosyal ve Beşerî Bilimlerde Araştırma ve Değerlendirmeler (1., pp. 237–252). Ankara: Gece Kitaplığı.
  • Çetin, D. T. (2021). Çevre dostu proje finansmanında yeşil tahvil ihracı (Green bond issuance in environmentally friendly project financing). In A. Ç. Ceylan, F. Özbay, Z. Özomay, & M. B. Kurt (Eds.), Sosyal ve Beşerî Bilimlerde Araştırma ve Değerlendirmeler (1., pp. 237–252). Ankara: Gece Kitaplığı.
  • Ehlers, T., & Packer, F. (2017). Green Bond Finance and Certification. BIS Quarterly Review, 89–104. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1709h.htm
  • Ehlers, T., & Packer, F. (2017). Green Bond Finance and Certification. BIS Quarterly Review, 89–104. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1709h.htm
  • Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds? Journal of Asset Management, 19(6), 371–383. https://doi.org/10.1057/s41260-018-0088-5
  • Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds? Journal of Asset Management, 19(6), 371–383. https://doi.org/10.1057/s41260-018-0088-5
  • Han, Y., Li, P., & Wu, S. (2020). Does Green Bond Improve Portfolio Diversification? Evidence from China. Evidence from China (July 1, 2020). https://doi.org/10.2139/ssrn.3639753
  • Han, Y., Li, P., & Wu, S. (2020). Does Green Bond Improve Portfolio Diversification? Evidence from China. Evidence from China (July 1, 2020). https://doi.org/10.2139/ssrn.3639753
  • Hong, H., Karolyi, G. A., & Scheinkman, J. A. (2020). Climate finance. The Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.1093/rfs/hhz146
  • Hong, H., Karolyi, G. A., & Scheinkman, J. A. (2020). Climate finance. The Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.1093/rfs/hhz146
  • ICMA. (2018, June). Green Bond Principles: Voluntary Process Guidelines for Issuing Green Bonds. Retrieved January 18, 2021, from https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/green-bond-principles-gbp/#translations
  • ICMA. (2018, June). Green Bond Principles: Voluntary Process Guidelines for Issuing Green Bonds. Retrieved January 18, 2021, from https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/green-bond-principles-gbp/#translations
  • Ismail, M., Jubley, N. Z., & Ali, Z. M. (2018). Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. AIP Conference Proceedings, 2013(1), 20022. AIP Publishing LLC. https://doi.org/10.1063/1.5054221
  • Ismail, M., Jubley, N. Z., & Ali, Z. M. (2018). Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. AIP Conference Proceedings, 2013(1), 20022. AIP Publishing LLC. https://doi.org/10.1063/1.5054221
  • Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences Vol. 218 ISEESE 2020. https://doi.org/10.1051/e3sconf/202021801026
  • Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences Vol. 218 ISEESE 2020. https://doi.org/10.1051/e3sconf/202021801026
  • Maheswari, B. U., Sujatha, R., Fantina, S., & Mansurali, A. (2021). ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price. Proceedings of the Second International Conference on Information Management and Machine Intelligence, 315–323. Springer. https://doi.org/10.1007/978-981-15-9689-6_35
  • Maheswari, B. U., Sujatha, R., Fantina, S., & Mansurali, A. (2021). ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price. Proceedings of the Second International Conference on Information Management and Machine Intelligence, 315–323. Springer. https://doi.org/10.1007/978-981-15-9689-6_35
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz Bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi. International Journal of Engineering and Innovative Research, 2(2), 102–120. Retrieved from https://dergipark.org.tr/en/pub/ijeir/issue/55163/730320
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz Bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi. International Journal of Engineering and Innovative Research, 2(2), 102–120. Retrieved from https://dergipark.org.tr/en/pub/ijeir/issue/55163/730320
  • Ngwakwe, C. (2021). Forecasting Corporate Green Investment Bonds–An Out of Sample Approach. The Journal of Accounting and Management, 11(1). Retrieved from https://dj.univ-danubius.ro/index.php/JAM/article/view/368/1187
  • Ngwakwe, C. (2021). Forecasting Corporate Green Investment Bonds–An Out of Sample Approach. The Journal of Accounting and Management, 11(1). Retrieved from https://dj.univ-danubius.ro/index.php/JAM/article/view/368/1187
  • OECD. (2017). Green Bonds: Mobilising Bond Markets for a Low-carbon Transition. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264272323-en
  • OECD. (2017). Green Bonds: Mobilising Bond Markets for a Low-carbon Transition. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264272323-en
  • Park, D., Park, J., & Ryu, D. (2020). Volatility spillovers between equity and green bond markets. Sustainability, 12(9), 3722. https://doi.org/10.3390/su12093722
  • Park, D., Park, J., & Ryu, D. (2020). Volatility spillovers between equity and green bond markets. Sustainability, 12(9), 3722. https://doi.org/10.3390/su12093722
  • Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263–291. https://doi.org/10.1080/20430795.2016.1237244
  • Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263–291. https://doi.org/10.1080/20430795.2016.1237244
  • Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38–50. https://doi.org/10.1016/j.eneco.2018.05.030
  • Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38–50. https://doi.org/10.1016/j.eneco.2018.05.030
  • Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25–38. https://doi.org/10.1016/j.econmod.2019.09.004
  • Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25–38. https://doi.org/10.1016/j.econmod.2019.09.004
  • Tang, D. Y., & Zhang, Y. (2020). Do shareholders benefit from green bonds? Journal of Corporate Finance, 61, 101427. https://doi.org/10.1016/j.jcorpfin.2018.12.001
  • Tang, D. Y., & Zhang, Y. (2020). Do shareholders benefit from green bonds? Journal of Corporate Finance, 61, 101427. https://doi.org/10.1016/j.jcorpfin.2018.12.001
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2(1), 39–47. https://doi.org/10.1016/j.fcij.2017.05.001
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2(1), 39–47. https://doi.org/10.1016/j.fcij.2017.05.001
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GREEN BONDS IN CLIMATE FINANCE AND FORECASTING OF CORPORATE GREEN BOND INDEX VALUE WITH ARTIFICIAL INTELLIGENCE

Yıl 2022, , 138 - 157, 27.06.2022

Dilşad Tülgen Çetin

https://doi.org/10.54452/jrb.992368
Cited By: 6

Öz

Küresel iklim değişikliğinin etkileri ve artan çevre bilinci, iklim projelerinin ve buna bağlı olarak iklim finansmanı ve yeşil tahvillerin öneminin artmasına yol açmıştır. Artan önemine karşın yeşil tahvillere ilişkin fiyat tahmin çalışmalarının son derece az olması, bu çalışmanın temel motivasyonunu oluşturmuştur. Bu makalenin amacı, yeşil tahvillerin kavramsal çerçevesini ele alarak kurumsal yeşil tahvil fiyatlarını Yapay Sinir Ağı modeli ile tahmin etmek ve tahmin ediciyi belirlemektir. Bu amaçla, girdi olarak S&P 500 endeks fiyatlarının ve çıktı olarak S&P yeşil tahvil endeks fiyatlarının belirlendiği Çok Katmanlı Geri Beslemeli Yapay Sinir Ağı modeli tasarlanmıştır. Konvansiyonel tahvil fiyatlarının kurumsal yeşil tahvillerin tahmincisi olup olmadığını belirlemek için tahmin modelinin tek girdisi olarak S&P 500 endeksi kullanılmıştır. Bulgular, kurumsal yeşil tahvil fiyatlarının %1,13 Ortalama Mutlak Yüzde Hatası (MAPE) ve %98,93 Regresyon Belirleme Katsayısı (R2) ile tahmin edildiğini göstermektedir. Araştırmanın sonuçları, yeşil tahvil yatırımcıları ve piyasa yapıcıları için kârı en üst düzeye çıkarmak ve/veya riski en aza indirmek için veriler sağlarken, politika yapıcılar için iklim projelerini finanse etmede yeşil tahvillerin etkinliğine ilişkin iç görü sağlamaktadır Bu makale, MLF-ANN modelinin kurumsal yeşil tahvillerin tahmininde etkinliğini kanıtlaması ve konvansiyonel tahvillerin yeşil tahvillerin tahmincisi olduğunu ortaya koyması açısından literatürdeki ilk çalışmadır. Bu nedenle çalışmanın ileride yapılacak çalışmalara ışık tutması beklenmektedir.

Anahtar Kelimeler

Yeşil tahvil tahmini Yapay Sinir Ağı modeli Yeşil tahvillerin tahmincisi Konvansiyonel tahviller

Kaynakça

  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165–1195. https://doi.org/10.1007/s00521-010-0362-z
  • Baker, M., Bergstresser, D., Serafeim, G., & Wurgler, J. (2018). Financing the response to climate change: The pricing and ownership of US green bonds (No. 25194). National Bureau of Economic Research. https://doi.org/10.3386/w25194
  • Baker, M., Bergstresser, D., Serafeim, G., & Wurgler, J. (2018). Financing the response to climate change: The pricing and ownership of US green bonds (No. 25194). National Bureau of Economic Research. https://doi.org/10.3386/w25194
  • Broadstock, D. C., & Cheng, L. T. W. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17–22. https://doi.org/10.1016/j.frl.2019.02.006
  • Broadstock, D. C., & Cheng, L. T. W. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17–22. https://doi.org/10.1016/j.frl.2019.02.006
  • CBI. (2016). Green Bonds Highlights 2016. Retrieved January 18, 2021, from https://www.climatebonds.net/resources/reports/green-bonds-highlights-2016
  • CBI. (2016). Green Bonds Highlights 2016. Retrieved January 18, 2021, from https://www.climatebonds.net/resources/reports/green-bonds-highlights-2016
  • CBI. (2020). Green Bonds Global State of the Market 2020. Retrieved January 19, 2021, from https://www.climatebonds.net/resources/reports/sustainable-debt-global-state-market-2020
  • CBI. (2020). Green Bonds Global State of the Market 2020. Retrieved January 19, 2021, from https://www.climatebonds.net/resources/reports/sustainable-debt-global-state-market-2020
  • Çetin, D. T. (2021). Çevre dostu proje finansmanında yeşil tahvil ihracı (Green bond issuance in environmentally friendly project financing). In A. Ç. Ceylan, F. Özbay, Z. Özomay, & M. B. Kurt (Eds.), Sosyal ve Beşerî Bilimlerde Araştırma ve Değerlendirmeler (1., pp. 237–252). Ankara: Gece Kitaplığı.
  • Çetin, D. T. (2021). Çevre dostu proje finansmanında yeşil tahvil ihracı (Green bond issuance in environmentally friendly project financing). In A. Ç. Ceylan, F. Özbay, Z. Özomay, & M. B. Kurt (Eds.), Sosyal ve Beşerî Bilimlerde Araştırma ve Değerlendirmeler (1., pp. 237–252). Ankara: Gece Kitaplığı.
  • Ehlers, T., & Packer, F. (2017). Green Bond Finance and Certification. BIS Quarterly Review, 89–104. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1709h.htm
  • Ehlers, T., & Packer, F. (2017). Green Bond Finance and Certification. BIS Quarterly Review, 89–104. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1709h.htm
  • Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds? Journal of Asset Management, 19(6), 371–383. https://doi.org/10.1057/s41260-018-0088-5
  • Hachenberg, B., & Schiereck, D. (2018). Are green bonds priced differently from conventional bonds? Journal of Asset Management, 19(6), 371–383. https://doi.org/10.1057/s41260-018-0088-5
  • Han, Y., Li, P., & Wu, S. (2020). Does Green Bond Improve Portfolio Diversification? Evidence from China. Evidence from China (July 1, 2020). https://doi.org/10.2139/ssrn.3639753
  • Han, Y., Li, P., & Wu, S. (2020). Does Green Bond Improve Portfolio Diversification? Evidence from China. Evidence from China (July 1, 2020). https://doi.org/10.2139/ssrn.3639753
  • Hong, H., Karolyi, G. A., & Scheinkman, J. A. (2020). Climate finance. The Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.1093/rfs/hhz146
  • Hong, H., Karolyi, G. A., & Scheinkman, J. A. (2020). Climate finance. The Review of Financial Studies, 33(3), 1011–1023. https://doi.org/10.1093/rfs/hhz146
  • ICMA. (2018, June). Green Bond Principles: Voluntary Process Guidelines for Issuing Green Bonds. Retrieved January 18, 2021, from https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/green-bond-principles-gbp/#translations
  • ICMA. (2018, June). Green Bond Principles: Voluntary Process Guidelines for Issuing Green Bonds. Retrieved January 18, 2021, from https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/green-bond-principles-gbp/#translations
  • Ismail, M., Jubley, N. Z., & Ali, Z. M. (2018). Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. AIP Conference Proceedings, 2013(1), 20022. AIP Publishing LLC. https://doi.org/10.1063/1.5054221
  • Ismail, M., Jubley, N. Z., & Ali, Z. M. (2018). Forecasting Malaysian foreign exchange rate using artificial neural network and ARIMA time series. AIP Conference Proceedings, 2013(1), 20022. AIP Publishing LLC. https://doi.org/10.1063/1.5054221
  • Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences Vol. 218 ISEESE 2020. https://doi.org/10.1051/e3sconf/202021801026
  • Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences Vol. 218 ISEESE 2020. https://doi.org/10.1051/e3sconf/202021801026
  • Maheswari, B. U., Sujatha, R., Fantina, S., & Mansurali, A. (2021). ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price. Proceedings of the Second International Conference on Information Management and Machine Intelligence, 315–323. Springer. https://doi.org/10.1007/978-981-15-9689-6_35
  • Maheswari, B. U., Sujatha, R., Fantina, S., & Mansurali, A. (2021). ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price. Proceedings of the Second International Conference on Information Management and Machine Intelligence, 315–323. Springer. https://doi.org/10.1007/978-981-15-9689-6_35
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz Bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi. International Journal of Engineering and Innovative Research, 2(2), 102–120. Retrieved from https://dergipark.org.tr/en/pub/ijeir/issue/55163/730320
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz Bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi. International Journal of Engineering and Innovative Research, 2(2), 102–120. Retrieved from https://dergipark.org.tr/en/pub/ijeir/issue/55163/730320
  • Ngwakwe, C. (2021). Forecasting Corporate Green Investment Bonds–An Out of Sample Approach. The Journal of Accounting and Management, 11(1). Retrieved from https://dj.univ-danubius.ro/index.php/JAM/article/view/368/1187
  • Ngwakwe, C. (2021). Forecasting Corporate Green Investment Bonds–An Out of Sample Approach. The Journal of Accounting and Management, 11(1). Retrieved from https://dj.univ-danubius.ro/index.php/JAM/article/view/368/1187
  • OECD. (2017). Green Bonds: Mobilising Bond Markets for a Low-carbon Transition. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264272323-en
  • OECD. (2017). Green Bonds: Mobilising Bond Markets for a Low-carbon Transition. Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264272323-en
  • Park, D., Park, J., & Ryu, D. (2020). Volatility spillovers between equity and green bond markets. Sustainability, 12(9), 3722. https://doi.org/10.3390/su12093722
  • Park, D., Park, J., & Ryu, D. (2020). Volatility spillovers between equity and green bond markets. Sustainability, 12(9), 3722. https://doi.org/10.3390/su12093722
  • Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263–291. https://doi.org/10.1080/20430795.2016.1237244
  • Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263–291. https://doi.org/10.1080/20430795.2016.1237244
  • Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38–50. https://doi.org/10.1016/j.eneco.2018.05.030
  • Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38–50. https://doi.org/10.1016/j.eneco.2018.05.030
  • Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25–38. https://doi.org/10.1016/j.econmod.2019.09.004
  • Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25–38. https://doi.org/10.1016/j.econmod.2019.09.004
  • Tang, D. Y., & Zhang, Y. (2020). Do shareholders benefit from green bonds? Journal of Corporate Finance, 61, 101427. https://doi.org/10.1016/j.jcorpfin.2018.12.001
  • Tang, D. Y., & Zhang, Y. (2020). Do shareholders benefit from green bonds? Journal of Corporate Finance, 61, 101427. https://doi.org/10.1016/j.jcorpfin.2018.12.001
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2(1), 39–47. https://doi.org/10.1016/j.fcij.2017.05.001
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal, 2(1), 39–47. https://doi.org/10.1016/j.fcij.2017.05.001
  • UNFCCC. (2021). The Paris Agreement. Retrieved August 1, 2021, from https://unfccc.int/process-and-meetings/the-paris-agreement/what-is-the-paris-agreement
  • UNFCCC. (2021). The Paris Agreement. Retrieved August 1, 2021, from https://unfccc.int/process-and-meetings/the-paris-agreement/what-is-the-paris-agreement
  • Yakut, E., Elmas, B., & Selahattin, Y. (2014). Yapay Sinir Ağları ve Destek Vektör Makineleri Yöntemleriyle Borsa Endeksi Tahmini. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1), 139–157. Retrieved from https://dergipark.org.tr/en/pub/sduiibfd/issue/20816/222712
  • Yakut, E., Elmas, B., & Selahattin, Y. (2014). Yapay Sinir Ağları ve Destek Vektör Makineleri Yöntemleriyle Borsa Endeksi Tahmini. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1), 139–157. Retrieved from https://dergipark.org.tr/en/pub/sduiibfd/issue/20816/222712
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Dilşad Tülgen Çetin 0000-0001-9321-6991

Yayımlanma Tarihi 27 Haziran 2022
Gönderilme Tarihi 8 Eylül 2021
Kabul Tarihi 23 Mart 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA 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. https://doi.org/10.54452/jrb.992368

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