机器学习洞察:探究事前信息的可变重要性

IF 1.9 Q2 BUSINESS, FINANCE Managerial Finance Pub Date : 2024-08-27 DOI:10.1108/mf-12-2023-0765
Ali Albada, Eimad Eldin Abusham, Chui Zi Ong, Khalid Al Qatiti
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引用次数: 0

摘要

目的对首次公开募股(IPO)初始回报的实证研究通常严重依赖线性回归模型。然而,这些模型由于容易受到离群值的影响而效率低下,而离群值是 IPO 数据中经常出现的情况。本研究引入了一种被称为随机森林的机器学习方法,以解决线性回归可能难以解决的问题。设计/方法/途径本研究的样本包括 2004 年至 2021 年的 352 个固定价格 IPO。本研究的一个独特之处是采用了随机森林方法。与其他方法相比,本研究对随机森林的准确性进行了评估。研究结果变量重要性测量结果表明,投资者需求、投资者之间的意见分歧和发行价格是预测 IPO 初始回报的最关键因素。据作者所知,这项研究是马来西亚文献中应用随机森林方法解决传统线性回归模型限制的开创性工作之一。这是通过考虑更广泛的因素并承认异常值的影响而实现的。此外,本研究通过对最能反映发行公司质量的事前信息进行排序和识别,为马来西亚文献增添了价值。这一贡献有助于潜在投资者的决策过程,并为发行公司提供了向 IPO 市场宣传其价值和质量的有效手段。
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Machine learning insights: probing the variable importance of ex-ante information

Purpose

Empirical examinations of initial public offering (IPO) initial returns often rely heavily on linear regression models. However, these models can prove inefficient owing to their susceptibility to outliers, a common occurrence in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve.

Design/methodology/approach

The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its application of the random forest method. The accuracy of random forest in comparison to other methods is evaluated. The findings indicate that the random forest model significantly outperforms other methods in all of the evaluated aspects.

Findings

The variable importance measure indicates that investors’ demand, divergence of opinion among investors and offer price are the most crucial predictors of IPO initial returns. These determinants hold particular significance due to the widespread use of the fixed-price method in Malaysia, as this method amplifies the information asymmetry in the IPO market.

Originality/value

To the best of the authors’ knowledge, this study is among the pioneering works in Malaysian literature to apply the random forest method to address the constraints of conventional linear regression models. This is achieved by considering a more extensive array of factors and acknowledging the influence of outliers. Additionally, this study adds value to Malaysian literature by ranking and identifying the ex-ante information that best signals the issuing firm’s quality. This contribution facilitates prospective investors’ decision-making processes and provides issuing firms with effective means to communicate their value and quality to the IPO market.

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来源期刊
Managerial Finance
Managerial Finance BUSINESS, FINANCE-
CiteScore
3.30
自引率
12.50%
发文量
103
期刊介绍: Managerial Finance provides an international forum for the publication of high quality and topical research in the area of finance, such as corporate finance, financial management, financial markets and institutions, international finance, banking, insurance and risk management, real estate and financial education. Theoretical and empirical research is welcome as well as cross-disciplinary work, such as papers investigating the relationship of finance with other sectors.
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