Research Article
BibTex RIS Cite

Halka Arzlar ve Borsa İstanbul Halka Arz Endeksinin Değerlendirmesi

Year 2023, Volume: 23 Issue: 4, 695 - 708, 22.10.2023
https://doi.org/10.21121/eab.1362952

Abstract

Bu çalışma, sermaye piyasalarının önemini vurgulayarak, halka arzların şirketler ve yatırımcılar için sunduğu fırsatlar ve risklere odaklanmaktadır. Sermaye piyasaları hem yerli hem de yabancı yatırımcılar için uzun vadeli yatırımlar yapma imkanı sunarak ekonomik büyümeye katkı sağlar. Halka arzlar, sermayenin tabana yayılmasını ve küçük yatırımcıların piyasaya katılmasını sağlayarak ekonomik istikrarı destekler. Ancak, sadece halka arza katılım amacıyla yeni hesaplar açılması, piyasadaki işlemleri sınırlayabilir ve uzun vadeli yatırımların önünde bir engel oluşturabilir. Türkiye'deki finansal piyasalar, yapısal düzenlemeler ve küresel entegrasyon çabalarıyla hızla gelişmiştir. Ancak finansal krizler ve pandemik olaylar gibi dönemlerde dalgalanmalar yaşanmıştır. Halka arzlar, şirketleri finansal piyasalara getirerek kaynakları yatırıma dönüştürme açısından kritik bir rol oynamaktadır. Bu nedenle, halka arzların sayısını artırmak ve sermaye piyasalarının gelişimini teşvik etmek amacıyla birçok projeye imza atılmıştır. Çalışmanın analiz kısmında Borsa İstanbul Halka Arz Endeksi'nin (XHARZ) performansını incelemekte ve performans tahmini yapılmaktadır. Geleneksel ARIMA Modeli ve yapay zeka temelli XGBoost modelli kullanılarak tahminler yapılmış, ardından bu modellerin performansları karşılaştırılmıştır. Analiz sonuçları makine öğrenimi temelli XGBoost Modeli'nin en iyi tahmin performansını sağladığını göstermektedir.

References

  • Avcı, S.B., Akdoğu, E., Şimşir, Ş.A. (2020). Borsa İstanbul ilk halka arz piyasa dinamikleri ve düşük fiyatlama. Center of Excellence in Finance Araştırma Raporu. Sabancı Üniversitesi. 1-24
  • Aygören, H., Sarıtaş, H. ve Morallı T. (2012). İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4 (1), 73-88
  • Babu, A.S., Reddy, S.K. (2015). Exchange rate forecasting using ARIMA. Neural Network and Fuzzy Neuron, Journal of Stock Forex Trading,4, 155
  • Bonaccorso, G., (2017). Machine Learning algorithms. Birmingham: Packt Publishing
  • Box, G. E. P., Jenkins, G. M., Ljung, G. M. (2016). Time series analysis: Forecasting and control. 5th Edition, NJ: John Wiley and Sons
  • Box, G, E, P., Jenkins, G, M. (1970). Time series analysis forecasting and control. San Francisco: Holden Day Inc
  • Borsa İstanbul, Veriler, Geçmişe dönük veri satışı. Borsa İstanbul Tarihsel ve Referans Veri Platformu. Erişim: 01.09.2023. https://datastore.borsaistanbul.com/
  • Borsa İstanbul, Endeksler, Halka arz endeksi. Erişim: 09.09.2023. https://borsaistanbul.com/tr/sayfa/166/bist-halka-arz-endeksi
  • Breiman L., (2001). Random forests. Machine Learning, 45(1):5-32
  • Brooks, C., (2014). Introductory econometrics for finance. 3rd Edition, NY: Cambridge University Press
  • Chen, Z., Li, C., Sun, W. (2020). Bitcoin price prediction using Machine Learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395
  • 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
  • Clarke B, Fokoue E, Zhang H. (2009). Tree-based classifiers. Principles and Theory for Data Mining and Machine Learning. 1st Edition, London NY: Springer Dordrecht Heidelberg, p.262.
  • Desai, V. S., Bharati, R. (1998). A comparison of Linear Regression and Neural Network Methods for predicting excess returns on large stocks. Annals of Operations Research, 78(0), 127-163
  • Emekli, G. (1995). Yararlanma bakımından Türkiye topraklarının bölünüşü ve zamanla gösterdiği değişmeler. Ege Coğrafya Dergisi, 8, 225-236
  • Fischer, T., Krauss, C. (2018). Deep Learning with Long Short-term Memory Networks for financial market predictions. European Journal of Operational Research, Vol. 270, 654-669
  • Fu, X., Du, J., Guo, Y., Liu, M., Dong, T., Duan, X. (2018). A Machine Learning framework for stock selection. Online: https://doi.org/10.48550/arXiv.1806.01743
  • Gündüz, H., Çataltepe, Z., Yaslan, Y. (2017). Derin Sinir Ağları ile borsa yönü tahmini. 25th Signal Processing and Communications Applications Conference (SIU)
  • Hanias, M., Thalassinos, E. L., Curtis, P. (2012). Time series prediction with Neural Networks for the Athens Stock Exchange indicator. European Research Studies Journal, 15(2), 23- 31
  • Ibbotson, R. G., Ritter, J. R. (1995). Handbooks in OR & MS. Chapter 30, Initial Public Offerings Investopedia, Initial public offering (IPO): What it is and how it Works. Erişim: 01.09.2023. https://www.investopedia.com/terms/i/ipo.asp
  • Kamuyu Aydınlatma Platformu, Endeksler, BIST Halka Arz. Erişim: 10.09.2023. https://www.kap.org.tr/tr/Endeksler Kilimci, Z. H. (2020). Borsa tahmini için Derin Topluluk Modelleri (DTM) ile finansal duygu analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakülte Dergisi. 35(2), 635- 650
  • Krauss, C., Do, X. A., Huck, N. (2017). Deep Neural Networks, Gradient-boosted Trees, Random Forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702
  • Kurtaran Ç.M. (2016). Firmaların ilk halka arz sonrası faaliyet performanslarının değerlendirilmesi: Borsa İstanbul örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 12 (27), 267-282
  • Kutlu, B., Badur, B. (2009). Yapay sinir ağları ile borsa endeksi tahmini. Yönetim, Yıl:20,63, 25-4
  • Loughran, T, Ritter, J. R., Rydqvist K. (1994), Initial public offerings: International insights, Pacific-Basin Finance Journal, Vol.2, Issues 2–3,165-199
  • Maciel, L., Ballini, R. (2010). Neural Networks applied to stock market forecasting: An empirical analysis. Journal of the Brazilian Neural Network Society, 8(1), 3-22.
  • Mitchell, R.; Frank, E. (2017). Accelerating the XGBoost Algorithm Using GPU Computing, PeerJ Computer Science, Vol.3, 127-164
  • Olah, C., (2015). Understanding LSTM networks. Colah’s blog, Online: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • Oncharen, P., Vateekul, P. (2018). Deep Learning for stock market prediction using event embedding and technical indicators. 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), 19-24
  • Özdemir, Ö. (2008). Zaman serisi modellemesinde yapay sinir ağlarının kullanımı ve bir uygulama. Anadolu Üniversitesi, Fen Bilimleri Enstitüsü, İstatistik Anabilim Dalı, Yüksek Lisans Tezi, https://earsiv.anadolu.edu.tr/xmlui/bitstream/handle/11421/5537/547648.pdf?sequence=1
  • Pabuçcu, H. (2019), Borsa endeksi hareketlerinin tahmini: Trend belirleyici veri. Selçuk Üniversitesi Sosyal Bilimler Yüksekokulu Dergisi, 22 (1), 246-256
  • Pešterac, A. (2020). The importance of initial public offering for capital market development in developing countries. Economic Themes, 58(1), 97-115
  • Ritter, J.R. (1991). The long-run performance of initial public offerings. The Journal of Finance, 46, 3-27
  • Ruiz Gazen, A, Villa N. (2007). Storms prediction: Logistic regression vs. random forest for unbalanced data. Case Studies in Business, Industry, and Government. Statistics (CSBIGS), 1(2):91-101
  • Sermaye Piyasası Kurulu, (2022). Yatırımcı bilgilendirme kitapçıkları, Halka arz https://spk.gov.tr/data/61e34f9a1b41c61270320792/10-Halka%20Arz.pdf
  • Siami Namini, S., Namin, A. S. (2018). Forecasting economics and financial time series: ARIMA vs. LSTM. Texas Tech University, Department of Agricultural and Applied Economics, https://arxiv.org/ftp/arxiv/papers/1803/1803.06386.pdf
  • Smith, J., Johnson, J., (2020). Ortalama Mutlak Yüzde Hata (MAPE) yöntemi ile tahmin değerlendirmesi. Veri Analizi ve Tahminde İleri Yöntemler Dergisi, 25(3), 345-36
  • Telli, Ş. Coşkun, M. (2016). Forecasting the BIST 100 Index using Artificial Neural Networks with consideration of the economic calendar. International Review of Economics And Management, 4 (3), 26-46
  • Thawornwong, S., Enke, D., Dagli, C. (2003). Neural Networks as a decision maker for stock trading: A technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313-325
  • Tkac, M., Verner, R. (2016). Artificial Neural Networks in business: Two decades of research, Applied Soft Computing, 38, 788-804
  • Tsay, R.S. (2010). Analysis of financial time series, 3rd Edition, NY: John Wiley and Sons Türkiye Cumhuriyet Merkez Bankası, İstatistikler, Gösterge niteliğindeki Merkez Bankası kurları. Erişim: 01.09.2023. https://www.tcmb.gov.tr/wps/wcm/connect/tr/tcmb+tr/main+menu/istatistikler/doviz+kurlari/gosterge+niteligindeki+merkez+bankasi+kurlarii
  • Wang, J., Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective. Neural Networks. Neurocomputing, 156, 68–78
  • Weibo, L., Zidong, W., Xiaohui, L., Nianyin, Z., Yurong, L., Fuad, A. (2016). A survey of Deep Neural Network Architectures and their applications, Neurocomputing, 234
  • Velankar, S., Valecha, S., Maji, S. (2018). Bitcoin price prediction using Machine Learning, 20th International Conference on Advanced Communication Technology (ICACT), IEEE, 144-147
  • Veri Analiz Platformu, Yatırımcı istatistikleri, Portföy dilimi bazında yatırımcı sayıları. Erişim: 09.09.2023. https://www.vap.org.tr/portfoy-dilimi-bazinda-yatirimci-sayilari Vergipedia, (2020). Türk şirketlerinin halka arz performansı yeterli mi. Erişim: 09.09.2023. https://vergipedia.com/makale/turk-sirketlerinin-halka-arz-performansi-yeterli-mi/
  • Yıldırım, D., Dursun A. (2016). Borsa İstanbul’daki İlk Halka Arzlarda İlk Gün Düşük Fiyat Anomalisi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(1), 189-202
  • Yunpeng, L., Di, H., Junpeng, B., Yong, Q. (2017). Multi-step ahead time series forecasting for different data patterns based on LSTM Recurrent Neural Network, 14th Web Information Systems and Applications Conference (WISA), IEEE; 05-310

IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX

Year 2023, Volume: 23 Issue: 4, 695 - 708, 22.10.2023
https://doi.org/10.21121/eab.1362952

Abstract

This study emphasizes the importance of capital markets and delves into the opportunities and risks that IPOs (Initial Public Offerings) offer for companies and investors. Capital markets provide an avenue for both domestic and foreign investors to make long-term investments, thereby contributing to economic growth. IPOs enable the broadening of capital distribution and the participation of small investors, ultimately supporting economic stability. However, opening new accounts solely for IPO participation may limit market transactions and hinder long-term investments. Financial markets in Turkey have rapidly developed, driven by structural regulations and efforts towards global integration. Nevertheless, financial crises and pandemic events have caused fluctuations in the market. IPOs play a crucial role in bringing companies into financial markets and converting funds into investments. Therefore, various projects have been initiated to increase the number of publicly traded companies and promote capital market development. In the analytical part of the study, the performance of the Borsa Istanbul IPO Index (XHARZ) is examined, and forecasting is conducted. Traditional ARIMA (p,d,q) models and artificial intelligence-based XGBoost models are used for predictions, followed by a comparison of their performance. The results of the analysis show that the machine learning based XGBoost Model provides the best forecasting performance.

References

  • Avcı, S.B., Akdoğu, E., Şimşir, Ş.A. (2020). Borsa İstanbul ilk halka arz piyasa dinamikleri ve düşük fiyatlama. Center of Excellence in Finance Araştırma Raporu. Sabancı Üniversitesi. 1-24
  • Aygören, H., Sarıtaş, H. ve Morallı T. (2012). İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4 (1), 73-88
  • Babu, A.S., Reddy, S.K. (2015). Exchange rate forecasting using ARIMA. Neural Network and Fuzzy Neuron, Journal of Stock Forex Trading,4, 155
  • Bonaccorso, G., (2017). Machine Learning algorithms. Birmingham: Packt Publishing
  • Box, G. E. P., Jenkins, G. M., Ljung, G. M. (2016). Time series analysis: Forecasting and control. 5th Edition, NJ: John Wiley and Sons
  • Box, G, E, P., Jenkins, G, M. (1970). Time series analysis forecasting and control. San Francisco: Holden Day Inc
  • Borsa İstanbul, Veriler, Geçmişe dönük veri satışı. Borsa İstanbul Tarihsel ve Referans Veri Platformu. Erişim: 01.09.2023. https://datastore.borsaistanbul.com/
  • Borsa İstanbul, Endeksler, Halka arz endeksi. Erişim: 09.09.2023. https://borsaistanbul.com/tr/sayfa/166/bist-halka-arz-endeksi
  • Breiman L., (2001). Random forests. Machine Learning, 45(1):5-32
  • Brooks, C., (2014). Introductory econometrics for finance. 3rd Edition, NY: Cambridge University Press
  • Chen, Z., Li, C., Sun, W. (2020). Bitcoin price prediction using Machine Learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395
  • 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
  • Clarke B, Fokoue E, Zhang H. (2009). Tree-based classifiers. Principles and Theory for Data Mining and Machine Learning. 1st Edition, London NY: Springer Dordrecht Heidelberg, p.262.
  • Desai, V. S., Bharati, R. (1998). A comparison of Linear Regression and Neural Network Methods for predicting excess returns on large stocks. Annals of Operations Research, 78(0), 127-163
  • Emekli, G. (1995). Yararlanma bakımından Türkiye topraklarının bölünüşü ve zamanla gösterdiği değişmeler. Ege Coğrafya Dergisi, 8, 225-236
  • Fischer, T., Krauss, C. (2018). Deep Learning with Long Short-term Memory Networks for financial market predictions. European Journal of Operational Research, Vol. 270, 654-669
  • Fu, X., Du, J., Guo, Y., Liu, M., Dong, T., Duan, X. (2018). A Machine Learning framework for stock selection. Online: https://doi.org/10.48550/arXiv.1806.01743
  • Gündüz, H., Çataltepe, Z., Yaslan, Y. (2017). Derin Sinir Ağları ile borsa yönü tahmini. 25th Signal Processing and Communications Applications Conference (SIU)
  • Hanias, M., Thalassinos, E. L., Curtis, P. (2012). Time series prediction with Neural Networks for the Athens Stock Exchange indicator. European Research Studies Journal, 15(2), 23- 31
  • Ibbotson, R. G., Ritter, J. R. (1995). Handbooks in OR & MS. Chapter 30, Initial Public Offerings Investopedia, Initial public offering (IPO): What it is and how it Works. Erişim: 01.09.2023. https://www.investopedia.com/terms/i/ipo.asp
  • Kamuyu Aydınlatma Platformu, Endeksler, BIST Halka Arz. Erişim: 10.09.2023. https://www.kap.org.tr/tr/Endeksler Kilimci, Z. H. (2020). Borsa tahmini için Derin Topluluk Modelleri (DTM) ile finansal duygu analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakülte Dergisi. 35(2), 635- 650
  • Krauss, C., Do, X. A., Huck, N. (2017). Deep Neural Networks, Gradient-boosted Trees, Random Forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702
  • Kurtaran Ç.M. (2016). Firmaların ilk halka arz sonrası faaliyet performanslarının değerlendirilmesi: Borsa İstanbul örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 12 (27), 267-282
  • Kutlu, B., Badur, B. (2009). Yapay sinir ağları ile borsa endeksi tahmini. Yönetim, Yıl:20,63, 25-4
  • Loughran, T, Ritter, J. R., Rydqvist K. (1994), Initial public offerings: International insights, Pacific-Basin Finance Journal, Vol.2, Issues 2–3,165-199
  • Maciel, L., Ballini, R. (2010). Neural Networks applied to stock market forecasting: An empirical analysis. Journal of the Brazilian Neural Network Society, 8(1), 3-22.
  • Mitchell, R.; Frank, E. (2017). Accelerating the XGBoost Algorithm Using GPU Computing, PeerJ Computer Science, Vol.3, 127-164
  • Olah, C., (2015). Understanding LSTM networks. Colah’s blog, Online: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • Oncharen, P., Vateekul, P. (2018). Deep Learning for stock market prediction using event embedding and technical indicators. 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), 19-24
  • Özdemir, Ö. (2008). Zaman serisi modellemesinde yapay sinir ağlarının kullanımı ve bir uygulama. Anadolu Üniversitesi, Fen Bilimleri Enstitüsü, İstatistik Anabilim Dalı, Yüksek Lisans Tezi, https://earsiv.anadolu.edu.tr/xmlui/bitstream/handle/11421/5537/547648.pdf?sequence=1
  • Pabuçcu, H. (2019), Borsa endeksi hareketlerinin tahmini: Trend belirleyici veri. Selçuk Üniversitesi Sosyal Bilimler Yüksekokulu Dergisi, 22 (1), 246-256
  • Pešterac, A. (2020). The importance of initial public offering for capital market development in developing countries. Economic Themes, 58(1), 97-115
  • Ritter, J.R. (1991). The long-run performance of initial public offerings. The Journal of Finance, 46, 3-27
  • Ruiz Gazen, A, Villa N. (2007). Storms prediction: Logistic regression vs. random forest for unbalanced data. Case Studies in Business, Industry, and Government. Statistics (CSBIGS), 1(2):91-101
  • Sermaye Piyasası Kurulu, (2022). Yatırımcı bilgilendirme kitapçıkları, Halka arz https://spk.gov.tr/data/61e34f9a1b41c61270320792/10-Halka%20Arz.pdf
  • Siami Namini, S., Namin, A. S. (2018). Forecasting economics and financial time series: ARIMA vs. LSTM. Texas Tech University, Department of Agricultural and Applied Economics, https://arxiv.org/ftp/arxiv/papers/1803/1803.06386.pdf
  • Smith, J., Johnson, J., (2020). Ortalama Mutlak Yüzde Hata (MAPE) yöntemi ile tahmin değerlendirmesi. Veri Analizi ve Tahminde İleri Yöntemler Dergisi, 25(3), 345-36
  • Telli, Ş. Coşkun, M. (2016). Forecasting the BIST 100 Index using Artificial Neural Networks with consideration of the economic calendar. International Review of Economics And Management, 4 (3), 26-46
  • Thawornwong, S., Enke, D., Dagli, C. (2003). Neural Networks as a decision maker for stock trading: A technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313-325
  • Tkac, M., Verner, R. (2016). Artificial Neural Networks in business: Two decades of research, Applied Soft Computing, 38, 788-804
  • Tsay, R.S. (2010). Analysis of financial time series, 3rd Edition, NY: John Wiley and Sons Türkiye Cumhuriyet Merkez Bankası, İstatistikler, Gösterge niteliğindeki Merkez Bankası kurları. Erişim: 01.09.2023. https://www.tcmb.gov.tr/wps/wcm/connect/tr/tcmb+tr/main+menu/istatistikler/doviz+kurlari/gosterge+niteligindeki+merkez+bankasi+kurlarii
  • Wang, J., Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective. Neural Networks. Neurocomputing, 156, 68–78
  • Weibo, L., Zidong, W., Xiaohui, L., Nianyin, Z., Yurong, L., Fuad, A. (2016). A survey of Deep Neural Network Architectures and their applications, Neurocomputing, 234
  • Velankar, S., Valecha, S., Maji, S. (2018). Bitcoin price prediction using Machine Learning, 20th International Conference on Advanced Communication Technology (ICACT), IEEE, 144-147
  • Veri Analiz Platformu, Yatırımcı istatistikleri, Portföy dilimi bazında yatırımcı sayıları. Erişim: 09.09.2023. https://www.vap.org.tr/portfoy-dilimi-bazinda-yatirimci-sayilari Vergipedia, (2020). Türk şirketlerinin halka arz performansı yeterli mi. Erişim: 09.09.2023. https://vergipedia.com/makale/turk-sirketlerinin-halka-arz-performansi-yeterli-mi/
  • Yıldırım, D., Dursun A. (2016). Borsa İstanbul’daki İlk Halka Arzlarda İlk Gün Düşük Fiyat Anomalisi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(1), 189-202
  • Yunpeng, L., Di, H., Junpeng, B., Yong, Q. (2017). Multi-step ahead time series forecasting for different data patterns based on LSTM Recurrent Neural Network, 14th Web Information Systems and Applications Conference (WISA), IEEE; 05-310
There are 47 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Research Article
Authors

İlknur Ülkü Armağan 0000-0003-0542-0007

Early Pub Date October 16, 2023
Publication Date October 22, 2023
Acceptance Date September 22, 2023
Published in Issue Year 2023 Volume: 23 Issue: 4

Cite

APA Armağan, İ. Ü. (2023). IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX. Ege Academic Review, 23(4), 695-708. https://doi.org/10.21121/eab.1362952
AMA Armağan İÜ. IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX. ear. October 2023;23(4):695-708. doi:10.21121/eab.1362952
Chicago Armağan, İlknur Ülkü. “IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX”. Ege Academic Review 23, no. 4 (October 2023): 695-708. https://doi.org/10.21121/eab.1362952.
EndNote Armağan İÜ (October 1, 2023) IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX. Ege Academic Review 23 4 695–708.
IEEE İ. Ü. Armağan, “IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX”, ear, vol. 23, no. 4, pp. 695–708, 2023, doi: 10.21121/eab.1362952.
ISNAD Armağan, İlknur Ülkü. “IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX”. Ege Academic Review 23/4 (October 2023), 695-708. https://doi.org/10.21121/eab.1362952.
JAMA Armağan İÜ. IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX. ear. 2023;23:695–708.
MLA Armağan, İlknur Ülkü. “IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX”. Ege Academic Review, vol. 23, no. 4, 2023, pp. 695-08, doi:10.21121/eab.1362952.
Vancouver Armağan İÜ. IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX. ear. 2023;23(4):695-708.