ABSTRACT This study develops a NK-DSGE model incorporating trend-extrapolative utility specifications to formalize how household wealth preferences drive stock bubble dynamics, as well as the selection of optimal monetary policy. The model features a wealth preference channel through which non-fundamental fluctuations in stock prices affect firm’s market value, relaxing firm’s credit constraints without improving investment efficiency. Besides, this paper finds that the standard tightening Taylor rule will instead promote the continuation of the positive distortion inertia of stock prices, and the forward-looking monetary policy including bubble correction can effectively restrain the stock prices bubble. Under the dual pillar framework of macro-prudential policy, the leaning-against-the-wind monetary policy including the bubble correction is still effective. In addition, this article finds that the selection of optimal monetary policy and the implementation of specific policies need to consider the specific sources of shocks, and be precisely implemented in conjunction with economic goals. The dual-pillar policy framework of the bubble correction rule and the macro-prudential rule can be relatively mild and continue to play a positive role in promoting the economy from the demand side.

Time-varying spillover of multi-scale positive and negative bubbles in stock and oil markets

ABSTRACT Individual investors prefer holding stocks that capture their attention and are salient, due to limited attention and capability constraints. But would rational investors, such as institutional funds, hold stocks with such salient characteristics? We use mutual funds as a representative of institutional investors and propose that they indeed hold salient stocks. However, unlike individual investors, mutual funds time their investment in salient stocks to capitalize on market bubbles – buying overvalued stocks to leverage their influence in further driving up prices, and selling them before the bubble bursts. Mutual funds profit by timing the bubble. This preference for salient stocks aligns with the behaviour and expected outcomes of rational bubble-riding strategies. However, it exacerbates stock market bubbles and leads to significant market volatility. Moreover, this behaviour is more pronounced during periods of high market uncertainty and among institutional investors with greater access to private information.

This study investigates the presence of financial bubbles in the Indian stock market, focusing on the NIFTY 50 and five sectoral indices—NIFTY Auto, NIFTY Bank, NIFTY Financial Services, NIFTY Energy, and NIFTY Pharma—over a 14-year period from August 2011 to March 2025. Utilizing advanced econometric techniques, including Right-Tailed Augmented Dickey-Fuller (RtADF), Rolling ADF (RADF), and Supremum ADF (SADF) tests, the analysis examines 164 monthly return observations to detect speculative bubbles and assesses the impact of sectoral returns on NIFTY 50 returns through multiple linear regression and Granger causality tests. Data, sourced from the National Stock Exchange (NSE) website, reveals that NIFTY Financial Services and NIFTY Auto yielded the highest average returns, while NIFTY Bank exhibited the highest volatility. The results indicate no statistical evidence of speculative bubbles across the selected indices, suggesting that market dynamics were driven by fundamental factors rather than speculative excess, with NIFTY 50 demonstrating the lowest risk and highest risk-adjusted performance. Despite the robust findings, the study acknowledges limitations, including the use of monthly returns instead of daily log prices, exclusion of macroeconomic variables, and a limited sectoral scope. These gaps highlight the need for future research to incorporate high-frequency data, macroeconomic factors, and additional sectors like IT and FMCG for a comprehensive analysis. The absence of bubbles implies a stable market environment, reinforcing investor confidence and reducing risks associated with bubble bursts. However, the dynamic nature of financial markets necessitates ongoing vigilance to monitor potential future speculative episodes, with recommendations for implementing Generalized SADF tests and machine learning models to enhance real-time bubble detection and predictive accuracy for investors and policymakers.

The study conducts an in-depth analysis of the development trends in China’s strategic emerging industries amid the new wave of technological revolution and industrial transformation, with a particular focus on the phenomenon of stock market bubbles. This research constructs a new theoretical framework for real-time monitoring and early warning of bubble risks and systematically investigates these bubbles by using the Kalman filter technique and the CUSUM control chart algorithm. Through empirical analysis, the model successfully identifies the super-exponential bubbles in the stock indices of strategic emerging industries between 2014 and 2015 and between 2019 and 2021. Further research findings suggest that short-term increases in market liquidity may trigger bubble formation, while sustained government policy support and long-term investor confidence in emerging industries play a crucial role in the continued development of these bubbles. The study proposes a series of policy recommendations aimed at mitigating bubble risks and promoting stable and sustainable economic development.

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

This study examines the Enron collapse through an integrated theoretical framework combining the financial saturation paradox with the dynamics of a naturally occurring Ponzi process. The central objective is to evaluate whether endogenous market mechanisms—beyond managerial misconduct—played a decisive role in the emergence and breakdown of the Enron stock bubble. A logistic-growth-based saturation model is formulated, incorporating positive feedback effects and bifurcation thresholds, and applied to Enron’s stock price data from 1996 to 2001. The computations were performed using LogletLab 4 (version 4.1, 2017) and Microsoft® Excel® 2016 MSO (version 2507). The model estimates market saturation ratios (P/Pp) and logistic growth rate (r), treating market potential, initial price, and time as constants. The results indicate that Enron’s share price approached a saturation level of approximately 0.9, signaling a hyper-accelerated, unsustainable growth phase consistent with systemic overheating. This finding supports the hypothesis that a naturally occurring Ponzi dynamic was underway before the firm’s collapse. The analysis further suggests a progression from market-driven expansion to intentional manipulation as the bubble matured, linking theoretical saturation stages with observed price behavior. By integrating behavioral–financial insights with saturation theory and Natural Ponzi dynamics, this work offers an alternative interpretation of the Enron case and provides a conceptual basis for future empirical validation and comparative market studies.

This paper examines Britain’s process of electrification following a disruptive stock market boom and bust in 1882. This is done by noting the companies that raise finance on British stock exchanges, the amounts raised, and the returns earned on that money. It also examines the impact of the Lighting Act of 1882, finding that the Act inhibited investment, but with important exceptions. We find the Act was not a barrier to entrepreneurs alert to the possibilities of electrification. However, the limited British electrical investment after the 1882 crash was more heavily and successfully concentrated on supplying electricity to end users than on developing electrical equipment. When electrification began in earnest after 1888, upon the amendment of the 1882 Lighting Act, there existed only a very weak engineering base to support it, leading to slow, expensive, and unimaginative electrification.

Designing an Early Warning Model to Examine the Effect of OPEC Summit Announcements on Stock Market Bubble Formation: A Logit Regression

Combining unsupervised learning with Restricted Boltzmann Machines and supervised learning with Balanced Random Forest and Feedforward Neural Networks, we propose a warning system for the early detection of stock bubbles by analyzing daily returns and the volatility of a market index. We complement our method by detecting states of high volatility and very low returns, which are market states that immediately follow a stock market’s bubble-bursting point. We trained our detection model using the S&P500 as an empirical case study, using successive samples of well-known crises from 1987 to 2022. Our results achieve area-under-the-curve (AUC) rates of over 70% and false-positive rates of less than 20%. Our model’s generative nature enables the creation of synthetic samples to analyze market periods prone to forming a bubble. The model successfully alerts periods of bubbles and instability in the stock market. Capital markets’ interconnectedness enables the model to be trained with various shocks from other stock markets, providing further detection learning possibilities and improved detection rates. Our work helps investors, regulators, and practitioners in their stock market investment, supervision, and monitoring tasks.

Purpose This study aims to identify and analyze speculative bubbles in the Tunisian stock market from 2004 to 2023 and examine the evolution of return volatility during these periods. Design/methodology/approach The research uses the Supremum Augmented Dickey-Fuller (SADF) and Generalized Supremum Augmented Dickey-Fuller (GSADF) tests, alongside Monte Carlo and bootstrap simulations (Sieve-bootstrap and Wild-bootstrap), to detect speculative bubbles. The Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity model is used to analyze volatility regimes. Findings The study identifies multiple speculative bubbles with varying timing, duration and response to external events. The GSADF test proves more effective than the SADF test for detecting longer, more frequent bubbles. Despite methodological differences, strong correlations among bootstrap techniques improve bubble identification. Bubble periods align with a high-volatility regime (regime 2), emphasizing volatility’s role in bubble formation. Research limitations/implications This study enhances the understanding of speculative bubble formation in emerging markets, highlighting the importance of considering national financial market specifics in bubble analysis. Practical implications The findings offer valuable insights for investors, regulators and policymakers, helping inform decisions and improve financial regulation to foster market stability. Social implications By identifying speculative bubbles, the research helps mitigate economic uncertainty, protects savings and supports financial stability, aiding policymakers in curbing excessive speculation and promoting sustainable economic growth. Originality/value This research contributes to the understanding of speculative bubbles in the underexplored Tunisian stock market, using innovative methodologies for a comprehensive analysis of bubbles and volatility dynamics.

Abstract Financial losses can have persistent effects on the financial system. This article proposes an empirical measure for the duration of these effects, Spillover Persistence. I document that Spillover Persistence is strongly correlated with financial conditions; during banking crises, Spillover Persistence is higher, whereas in the run-up phase of stock market bubbles, it is lower. Lower Spillover Persistence also associates with a more fragile system, for example, a higher probability of future crises, consistent with the volatility paradox. The results emphasize the dynamics of loss spillovers as an important dimension of systemic risk and financial constraints as a key determinant of persistence.

Abstract We examine the relationship between investors’ emotions and GameStop (GME) stock returns during the price bubble of January–February 2021. Analyzing eight basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) from Plutchik's (1980) Wheel of Emotions, we use textual analysis of Reddit posts to find that fear strongly predicts intraday returns and volume order imbalance. The predictive relationship between emotion and returns shifts over time: joy is strongest before the bubble peaks, fear at the peak, and anger after the bubble bursts. These findings highlight the psychological factors influencing trading behavior during stock market bubbles.

Abstract There is causal evidence that mortgage credit expansions increase house prices. Does an expansion of margin lending increase stock prices? Because unconstrained arbitrageurs are more important for pricing stocks than homes, the impact is not obvious. Tests are limited because sizable shocks to margin lending are rare. We examine a major Chinese margin-lending expansion between 2010 and 2015. Institutional holding, regression discontinuity, and event study evidence—exploiting the rollout of margin lending across stocks—shows that arbitrageurs anticipated and bought in advance of a significant causal effect of credit. We develop a model to rationalize our findings. Our estimates suggest that margin debt contributes to stock market fluctuations.

Abstract This article explores the puzzling ups and downs of Tesla, going through one the most volatile stock market swings of the recent past, under conditions of financialised capitalism. We adopt a conjunctural approach, highlighting both the macro and micro dynamics shaping the firm’s financial market trajectory. Among these, meticulously maintained narratives, boosted by social media, attracted dedicated followers, while the rise of new retail trading platforms and excitement around Tesla’s index inclusion helped in producing its stock ‘mementum’. This volatility was further supported by an exceptionally large volume of financial derivatives trading, paralleling a public battle between short sellers and the company’s defenders. The resulting stock market boom enabled Tesla to stabilise its finances, whilst its ‘mercurial’ CEO Elon Musk negotiated the largest executive compensation package in US corporate history, turning him into the world’s richest individual. In conclusion, we argue that Tesla serves as an emblematic case of an increasingly tech-driven financialised capitalism, which scholars could use as a window to study future conjunctures.

While there is a large body of literature on oil uncertainty-equity prices and/or returns nexus, an associated important question of how oil market uncertainty affects stock market bubbles remains unanswered. In this paper, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock markets of the G7 countries. While detecting major crashes and booms in the seven stock markets over the monthly period of February 1973 to May 2020, we also observe similar timing of strong (positive and negative) LPPLS-CIs across the G7, suggesting synchronized boom-bust cycles. Given this, we next apply dynamic heterogeneous coefficients panel databased regressions to analyze the predictive impact of a model-free robust metric of oil price uncertainty on the bubbles indicators. After controlling for the impacts of output growth, inflation, and monetary policy, we find that oil price uncertainty predicts a decrease in all the time scales and countries of the positive bubbles and increases strongly in the medium term for five countries (and weakly the short-term) negative LPPLS-CIs. The aggregate findings continue to hold with the inclusion of investor sentiment indicators. Our results have important implications for both investors and policymakers, as the higher (lower) oil price uncertainty can lead to a crash (recovery) in a bullish (bearish) market.

This paper aims to examine the indirect impact of geopolitical risk on the Vietnamese stock market and stock bubbles (VSB) through its impact on macro factors and commodity prices. Firstly, before testing the influence of geopolitical risk on stock bubbles, a stock bubble existence test using SADF (sup augmented Dickey Fuller test) and GSADF (generalized sup Augmented Dickey Fuller) is done. The tests show that stock bubbles appeared on the Vietnamese stock market in 3 periods: September 2014 to November 2014, June 2017 to May 2018 and March 2021 to March 2022. Secondly, the test on indirect relationship between geopolitical risk and Vietnam's stock market and stock bubbles reveals that geopolitical risk has a significant indirect relationship with the stock market through intermediary factors including macroeconomic factors and natural gas prices, while it has a significant indirect relationship with stock bubbles through the mediation role of oil prices and natural gas prices. More specifically, the indirect relationships are weak but positive, which means that increasing geopolitical tension may cause the VNIndex (representing Vietnam's stock market) to increase and may further exacerbate stock bubbles on the market. Finally, the indirect correlation between geopolitical risk and Vietnam's stock bubble is discussed and a crucial finding is concluded that major geopolitical events often occurred just before and during the stock bubble formation.

The effectiveness of capital mobilization by securities companies in Vietnam during the stock market boom period

Stock fire sale risks and the effect of China connect

Government debt and stock bubbles in China