利用机器学习模型检测和预测印度股市的金融泡沫

IF 1 Q3 MANAGEMENT

IIMB Management Review Pub Date : 2025-09-01 DOI:10.1016/j.iimb.2025.100591

Mahalakshmi Manian, Parthajit Kayal

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引用次数: 0

摘要

本研究使用NIFTY 500指数数据（2003-2021年）研究了印度股市的金融泡沫，其中资产价格超过其内在价值。Phillips, Shi, and Yu （PSY）方法通过右尾单位根检验检测泡沫，揭示了2007年和2017年的显著现象。机器学习算法，包括人工神经网络、随机森林和梯度增强，在实时预测泡沫方面优于传统方法。这些发现强调了先进的机器学习技术对政策制定者的潜力，可以通过改进金融泡沫的检测和管理来加强市场监管和降低风险。

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Detecting and forecasting financial bubbles in the Indian stock market using machine learning models

This study examines financial bubbles in the Indian stock market, where asset prices exceed their intrinsic value, using NIFTY 500 index data (2003–2021). The Phillips, Shi, and Yu (PSY) method detects bubbles through a right-tailed unit root test, revealing notable occurrences in 2007 and 2017. Machine learning algorithms, including Artificial Neural Networks, Random Forest, and Gradient Boosting, outperform traditional methods in predicting bubbles in real time. These findings emphasise the potential of advanced machine learning techniques for policymakers to enhance market regulation and mitigate risks through improved detection and management of financial bubbles.

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来源期刊

IIMB Management Review MANAGEMENT-

CiteScore

3.20

自引率

5.90%

发文量

审稿时长

68 days

期刊介绍： IIMB Management Review (IMR) is a quarterly journal brought out by the Indian Institute of Management Bangalore. Addressed to management practitioners, researchers and academics, IMR aims to engage rigorously with practices, concepts and ideas in the field of management, with an emphasis on providing managerial insights, in a reader friendly format. To this end IMR invites manuscripts that provide novel managerial insights in any of the core business functions. The manuscript should be rigorous, that is, the findings should be supported by either empirical data or a well-justified theoretical model, and well written. While these two requirements are necessary for acceptance, they do not guarantee acceptance. The sole criterion for publication is contribution to the extant management literature.Although all manuscripts are welcome, our special emphasis is on papers that focus on emerging economies throughout the world. Such papers may either improve our understanding of markets in such economies through novel analyses or build models by taking into account the special characteristics of such economies to provide guidance to managers.