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.

摘要

很难预测首次公开募股（IPO）的未来收益，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票价格，因此我们发现验证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票价格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并优化交易策略，从而为金融科技做出贡献。