利用机器学习模型研究羊群行为

IF 1.9 Q2 BUSINESS, FINANCE Review of Behavioral Finance Pub Date : 2023-11-01 DOI:10.1108/rbf-05-2023-0121
Muhammad Asim, Muhammad Yar Khan, Khuram Shafi
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引用次数: 0

摘要

本研究旨在调查羊群行为在英国股票市场的存在,特别强调有关经济的新闻情绪。作者之所以关注新闻情绪,是因为在当前的数字时代,投资者会根据新闻和媒体平台预测的当前趋势做出决策。设计/方法/方法对于实证建模,作者使用机器学习模型来调查2006年至2021年期间英国股市中羊群行为的存在。运用支持向量回归、单层神经网络和多层神经网络模型对英国股票市场的羊群行为进行了预测。作者使用所有模型估计放牧系数,并将结果与线性回归模型进行比较。研究结果显示,在不同的时间制度下,羊群行为在英国股票市场的有力证据。此外,当作者将经济不确定性新闻情绪纳入模型时,结果显示出显着的改善。支持向量回归、单层感知器和多层感知器模型的结果显示了2007-08年全球金融危机和2019冠状病毒病期间英国股市羊群行为的证据。此外,作者将这些发现与线性回归进行了比较,线性回归没有提供除COVID - 19外所有制度中羊群行为的证据。研究结果也为个人投资者和政策制定者构建有效的投资组合和避免市场崩溃提供了深刻的见解。在现有的羊群行为文献中,关于经济不确定性的新闻情绪尚未被使用。然而,在当前这个时代,这个参数在市场异常的背景下是相当关键的,因此需要进行调查。此外,当使用不同的方法时,文献展示了关于羊群行为存在的不同结果。在这种情况下,机器学习模型的使用在羊群文献中是相当罕见的。机器学习模型非常健壮,并提供准确的结果。因此,本研究采用单层感知机模型、多层感知机模型和支持向量回归模型三种不同的模型来研究英国股票市场的羊群行为。并对各模型的计算结果进行了比较分析。该研究揭示了经济不确定性新闻情绪对羊群行为预测的重要性。
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Investigation of herding behavior using machine learning models
Purpose The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms. Design/methodology/approach For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model. Findings The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively. Originality/value In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.
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来源期刊
Review of Behavioral Finance
Review of Behavioral Finance BUSINESS, FINANCE-
CiteScore
4.70
自引率
5.00%
发文量
44
期刊介绍: Review of Behavioral Finance publishes high quality original peer-reviewed articles in the area of behavioural finance. The RBF focus is on Behavioural Finance but with a very broad lens looking at how the behavioural attributes of the decision makers influence the financial structure of a company, investors’ portfolios, and the functioning of financial markets. High quality empirical, experimental and/or theoretical research articles as well as well executed literature review articles are considered for publication in the journal.
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