{"title":"IPOs AND EVALUATION OF THE BORSA ISTANBUL IPO INDEX","authors":"İlknur Ülkü ARMAĞAN","doi":"10.21121/eab.1362952","DOIUrl":null,"url":null,"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.","PeriodicalId":43307,"journal":{"name":"EGE ACADEMIC REVIEW","volume":"334 1","pages":"0"},"PeriodicalIF":0.5000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EGE ACADEMIC REVIEW","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21121/eab.1362952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
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.
本研究强调了资本市场的重要性,并深入探讨了ipo(首次公开募股)为公司和投资者提供的机会和风险。资本市场为国内外投资者提供了一个进行长期投资的渠道,从而促进了经济增长。首次公开募股使资本分配和小投资者的参与得以扩大,最终支持经济稳定。然而,仅为参与IPO而开设新账户可能会限制市场交易,阻碍长期投资。在结构性监管和全球一体化努力的推动下,土耳其的金融市场迅速发展。然而,金融危机和大流行病事件造成了市场波动。ipo在将企业引入金融市场、将资金转化为投资方面发挥着至关重要的作用。因此,各种项目已经启动,以增加上市公司的数量,促进资本市场的发展。在分析部分,研究了Borsa Istanbul IPO指数(XHARZ)的表现,并进行了预测。使用传统的ARIMA (p,d,q)模型和基于人工智能的XGBoost模型进行预测,然后比较它们的性能。分析结果表明,基于机器学习的XGBoost模型提供了最好的预测性能。