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

Mahalakshmi Manian & Parthajit Kayal

IIMB Management Review2025https://doi.org/10.1016/j.iimb.2025.100591article
ABDC C
Weight
0.44

What the paper says

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.

3 citations

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https://doi.org/https://doi.org/10.1016/j.iimb.2025.100591

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@article{mahalakshmi2025,
  title        = {{Detecting and forecasting financial bubbles in the Indian stock market using machine learning models}},
  author       = {Mahalakshmi Manian & Parthajit Kayal},
  journal      = {IIMB Management Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.iimb.2025.100591},
}

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

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.