Stock price prediction using multiple valuation methods based on artificial neural networks for KOSDAQ IPO companies

IF 1.2 4区经济学 Q3 BUSINESS, FINANCE Investment Analysts Journal Pub Date : 2021-01-02 DOI:10.1080/10293523.2020.1870860

J. Han, Hyun-jung Kim

引用次数: 5

Abstract

ABSTRACT It is difficult to predict future payoffs for initial public offerings (IPOs), since the multiple valuation method used to determine IPOs’ prices provides estimates by reflecting current sentiments in specific market environments. As our model reflects accounting information and stock price, we find that the mean absolute percentage error that verifies the accuracy of IPO stock valuation improves return on investment by 15% to 20%. This can help shareholders and investors accurately estimate stock prices and engage in efficient investment decision-making, while contributing to fintech by applying machine learning to traditional techniques to analyse investment opportunities and optimise trading strategies.

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基于人工神经网络的KOSDAQ上市公司多重估值方法的股价预测

摘要首次公开募股（IPO）的未来收益很难预测，因为用于确定IPO价格的多重估值方法通过反映特定市场环境中的当前情绪来提供估计。由于我们的模型反映了会计信息和股价，我们发现验证IPO股票估值准确性的平均绝对百分比误差将投资回报率提高了15%至20%。这可以帮助股东和投资者准确估计股价并进行有效的投资决策，同时通过将机器学习应用于传统技术来分析投资机会和优化交易策略，为金融科技做出贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Investment Analysts Journal BUSINESS, FINANCE-

CiteScore

1.90

自引率

11.10%

发文量

期刊介绍： The Investment Analysts Journal is an international, peer-reviewed journal, publishing high-quality, original research three times a year. The journal publishes significant new research in finance and investments and seeks to establish a balance between theoretical and empirical studies. Papers written in any areas of finance, investment, accounting and economics will be considered for publication. All contributions are welcome but are subject to an objective selection procedure to ensure that published articles answer the criteria of scientific objectivity, importance and replicability. Readability and good writing style are important. No articles which have been published or are under review elsewhere will be considered. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. All peer review is double blind and submission is via email. Accepted papers will then pass through originality checking software. The editors reserve the right to make the final decision with respect to publication.