Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models

Mahalakshmi Manian and Parthajit Kayal ()
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Mahalakshmi Manian: Research Scholar
Parthajit Kayal: (corresponding author), Assistant Professor Madras School of Economics, Chennai

Working Papers from Madras School of Economics,Chennai,India

Abstract: This research investigates the phenomenon of economic or financial bubbles within the Indian stock market context, characterized by pronounced asset price inflation exceeding the intrinsic worth of the underlying assets. Leveraging data from the NIFTY 500 index spanning the period 2003 to 2021, the study utilizes the Phillips, Shi, and Yu (PSY) method (Phillips et. al., 2015b), which employs a right-tailed unit root test, to discern the presence of financial bubbles. Subsequently, machine learning algorithms are employed to predict real-time occurrences of such bubbles. Analysis reveals the manifestation of financial bubbles within the Indian stock market notably in the years 2007 and 2017. Moreover, empirical evidence underscores the superior predictive efficacy of Artificial Neural Networks, Random Forest, and Gradient Boosting algorithms vis-à-vis conventional statistical methodologies in forecasting financial bubble occurrences within the Indian stock market. Policymakers should use advanced machine learning techniques for real-time financial bubble detection to improve regulation and mitigate market risks.

Keywords: Financial Bubbles; Machine Learning; K-nearest Neighbour; Random Forest Classifier; Artificial Neural Network; Naïve Bayes (search for similar items in EconPapers)
JEL-codes: C1 C5 G1 G2 G3 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2024-10
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-fdg
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