Stock Market Bubble Research Articles

Government debt and stock bubbles in China

Herein, we used Vector Autoregressive and Dynamic Stochastic General Equilibrium models to examine the relation between central government debt and stock bubbles in China. The empirical findings indicate a considerable negative correlation between central government debt and stock bubbles. The model's results also illustrate that the liquidity substitution effect mainly drives a negative linkage. When commercial banks hold government debt, the effect establishes a connection between central government debt and asset bubbles through commercial banks' balance sheets. Further analysis indicates that countercyclical fiscal policies impact the negative correlation between government debt and stock bubbles. The findings suggest that governments should regulate debt levels to manage asset bubbles.

Like a moth to a flame: Do stock market bubbles exacerbate credit risks of peer‐to‐peer lending?

AbstractWe establish a causal link between stock market bubbles and credit risks from peer‐to‐peer lending. Employing a fuzzy regression discontinuity design based on retail investors' disproportional increase in stock market participation when the Shanghai Stock Exchange composite index exceeds 3500, we find that both the default rate and the degree of delinquency rise disproportionately for loans borrowed above the 3500 threshold. This effect is more pronounced among loans of lower quality and when borrowers are more overconfident and less risk averse. Overall, our results suggest that FinTech developments could amplify financial risks and induce contagion across markets.

Artificial intelligence as a core of the new industrial revolution: prospects and limitations

The purpose of the article is to define prospects and limitations of artificial intelligence as a core of in the new industrial revolution. The definition of the concept of AI in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the concept of AI. Based on the analysis conducted and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) energy consumption of AI; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.

Stock Market Bubble- Investigate Cause and Effect of Stock Market Bubbles and Examine How Bubbles can be Identified and Prevented

Stock Market Bubble- Investigate Cause and Effect of Stock Market Bubbles and Examine How Bubbles can be Identified and Prevented

Institutional investor cliques and stock price efficiency: Evidence from China

We investigate the impact of coordinating groups of institutional investors (cliques) on stock price efficiency in China. Employing the Louvain algorithm, we identify institutional investor cliques based on their holding networks and observe strongly correlated trading behaviors among clique members. Our baseline findings document that institutional investor clique ownership impedes stock price efficiency. We also provide a potential mechanism suggesting that this impediment effect arises from reduced information acquisition by clique members. Our additional analyses suggest that the inefficient stock prices induced by institutional cliques may exacerbate stock bubbles and increase the stock price crash risk.

Investor emotions and market bubbles

AbstractAsset pricing bubbles are highly emotional market episodes. Despite this, investor emotions are not part of traditional bubble models. We measure the powerful affects influencing investor decisions during speculative market bubbles directly employing textual analysis of media narratives and domain-specific emotion keyword dictionaries and show how understanding investor emotional dynamics helps explain market behavior. Specifically, we focus on the two Chinese stock market bubbles of 2005–2008 and 2014–2016; there is no evidence of investor learning from experience. Despite Chinese media being censored we show it still has strong explanatory power although the independent English language media can provide an additional perspective. Deeper emotions dominate more superficial feelings in information content.

An innovative approach for optimising CCP default management through agent-based modelling

This paper develops a rigorous model to analyse the market impact of liquidation. Invoking innovative techniques from agent-based simulation, we construct the order book's reaction function to liquidation. A methodology is then developed for establishing the optimal close-out strategy which balances the velocity of positional liquidation with that consequential market reaction function. This model can be used in parallel to more traditional calculations of market volatility to ensure that the overall risk capitalisation of a central clearing counterparty remains robust. In the case of financial markets, agent-based models often seek to account for the so-called stylised facts of financial markets. Stylised facts are simply empirical regularities that appear to be stable across markets and over time. Such facts range from statistical concepts, such as the distribution of returns, to more abstract notions such as stock market bubbles and crashes.

Determining the Ebullient Phases in EFTA Stock Markets

Previous empirical literature supports the idea that stock bubbles have impacts on the efficient allocation of wealth. Researchers targeted various economies in the past using various methods to explore the bubble phenomenon. This study uses the generalized SADF test, which is admitted by empirical literature to be the most successful technique, to explore stock bubbles in three countries included in the European Free Trade Association (EFTA) that have not been studied before. This paper takes the lead and tests for the existence of bubbles in the monthly end index prices of respective countries based on the latest available time series data from January 2001 to September 2019. Based on empirical results, it is concluded that the stock markets of all three countries experienced multiple bubbles during the study period. The case of Iceland is worse, where comparatively more fluctuations in stock prices are seen. To avoid the occurrence of further stock price bubbles in these countries, policy recommendations are provided as well.

Stock market bubbles and the realized volatility of oil price returns

Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. First, we successfully detect major crashes and rallies using the MS-LPPLS-CIs. Having established the relevance of the bubbles indicators, and given the large number of them, we use widely-studied shrinkage (Lasso, elastic net, ridge regression) approaches to estimate our forecasting models. We find that stock market bubbles have predictive value for realized volatility at a short to intermediate forecast horizon. The number of bubble predictors included in the penalized forecasting models tend to increase in the forecast horizon. We obtain our main finding for the various types of stock market bubbles, and for good and bad realized volatilities.

The asymmetric effect of investor herding on the asset price bubble - evidence from the Chinese stock market

ABSTRACT This paper aims at exploring the asymmetric effect of the investor herding behaviour on the price bubble in the Chinese stock market. We first use the backward sup ADF (BSADF) statistic and the time-varying coefficient CCK (TVC-CCK) model to dynamically measure the stock market bubble and the investor herding. Then, we adopt the nonlinear autoregressive distributed lag (NARDL) model to analyse the asymmetric impacts of the positive and negative changes of the herding behaviour on the market bubble under different market conditions from the long and short run. The empirical results show that for the Chinese stock market, the investor herding has different influence mechanisms on the price bubble under different market states, and the effects of the positive and negative herding are asymmetric in the both long term and short term. In the bull market, the continuous rise of the herding plays a fuelling role in the formation of the bubble, and the weakening of the herding can significantly curb its expansion in the long run. However, in the bear market, the aggravation of the herding plays an ‘accelerator’ role in the collapse of the bubble, and the decline of the herding can’t significantly prevent this collapse.

Gold, platinum and the predictability of bubbles in global stock markets

This paper examines the predictability of bubbles across global stock markets and whether or not synchronicity in bubble formation can be predicted via metrics of market risk that are readily available. Utilizing the gold to platinum price ratio (LGP) as an easy to implement risk metric and the Log-Periodic Power Law Singularity (LPPLS) model to detect positive and negative bubble formation at different time scales, we document evidence of synchronized boom and bust cycles of the seven developed equity markets in the G7 bloc. More importantly, our analysis shows that bubbles and their comovements are in fact predictable although the predictive relationship is only detectible via models that account for non-linearities in the data. We find that the gold to platinum price ratio serves as a stronger predictor of deeper downward accelerating price formations followed by a rally. The predictability results for the U.S. also carries over to bubble formation in the remaining stock markets of the G7 bloc, to the extent that the gold to platinum price ratio also helps to explain the synchronicity of bubbles across the G7. Our findings provide a valuable opening for market regulators as the results show that readily available metrics of market risk can be used to model and monitor the occurrence of bubbles in financial markets as well as the connectedness of bubbles across the global markets.

Comparative Analysis of Stock Bubble in S&P 500 Individual Stocks: A Study Using SADF and GSADF Models

Stock bubbles are characterized by unpredictable price surges and subsequent declines, causing significant losses for investors. This study investigates the effectiveness of the Generalized Sup Augmented Dickey–Fuller (GSADF) test in identifying mild explosive patterns and speculative bubbles within individual S&P 500 stocks, as compared to the Sup Augmented Dickey–Fuller (SADF) test. Utilizing real-time monitoring data, this research examines unit roots, stationarity, and the ability to detect multiple structural breaks. The GSADF test consistently outperforms the SADF test in rejecting the null hypothesis, demonstrating greater sensitivity and efficacy in recognizing stock bubbles. Monte Carlo simulations address size distortions in the GSADF test, enhancing accuracy.

Craving money? Evidence from the laboratory and the field.

Continuing to gamble despite harmful consequences has plagued human life in many ways, from loss-chasing in problem gamblers to reckless investing during stock market bubbles. Here, we propose that these anomalies in human behavior can sometimes reflect Pavlovian perturbations on instrumental behavior. To show this, we combined key elements of Pavlovian psychology literature and standard economic theory into a single model. In it, when a gambling cue such as a gaming machine or a financial asset repeatedly delivers a good outcome, the agent may start engaging with the cue even when the expected value is negative. Next, we transported the theoretical framework into an experimental task and found that participants behaved like the agent in our model. Last, we applied the model to the domain of real-world financial trading and discovered an asset-pricing anomaly suggesting that market participants are susceptible to the purported Pavlovian bias.

Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock market

This study proposes innovative methods for determining the starting point and estimating the parameters of the LPPL model for predicting stock market bubbles. These methods address the challenges in determining the starting point of irregularly staged bubbles and the issues of multiple local optima or no solutions in previous prediction algorithms. The proposed LPPL model, incorporating innovative algorithms, is applied to the Chinese A-share market from 2018 to 2021. The starting points of structural bubbles are determined using the difference and grid search methods, and the sequential quadratic programming (SQP) algorithm is used to estimate the parameters of the LPPL model. The study measures and warns about structural bubbles in the A-share market. The results demonstrate that the LPPL model with the innovative algorithm can accurately measure staged and structural bubbles in the A-share market and provide reasonably accurate warnings of bubble bursts, which have been confirmed by the market multiple times.

Research on Investor Sentiment and Its Impact on the Market for Stocks

Both individual and institutional investors are affected by investor sentiment to varying degrees. Based on this, the research theme for this paper is the interaction between investor sentiment and the market for stocks. It begins with defining and quantifying the idea of investor sentiment and then analyzes how investor sentiment and the market for stocks are related, including stock market yield, stock price crash risk, and stock market bubble aspects. It is found that the definition of investor sentiment mostly revolves around the perspectives of "expectation" and "deviation", and the quantitative indicators of investor sentiment are mainly divided into three parts: objective, subjective, and network big data. Investor sentiment is highly predictive of changes in stock market yields in the short term, and high investor sentiment is more likely to lead to stock price crash risk or stock market bubbles. Finally, this paper points out the shortcomings of existing research, and puts forward the future prospects of relevant research conclusions, so as to provide a reference for subsequent scholars to conduct research on investor sentiment and the stock market.

‘Irrational Exuberance’: Behavioural Finance and Émile Zola’s L’Argent

Abstract Émile Zola’s L’Argent has been relatively neglected by critical studies. This interdisciplinary study adds to earlier approaches by examining the ways in which Zola’s novel anticipates strands of contemporary behavioural finance, notably in its analysis of the psychological and social conditions behind ‘irrational exuberance’ and a speculative stock-market bubble. A few avenues are explored, inspired by Robert Shiller’s Irrational Exuberance: precipitating factors, amplification mechanisms, news media, individual investor psychology, herding. The novel reflects contemporary thinking from behavioural finance, but goes further, in its greater human depth, its humour and complexity. Moreover, the novel displays, beyond behavioural finance, a conjunction of financial exuberance with bodily, philosophical, and artistic jouissance, further anticipating modern times.

An Application of the “Recursive Flexible Window” Methodology to Test for Financial Bubbles in a Major Stock Market

Identifying and dating financial bubbles in real time is in the forefront of current empirical research. Their accuracy provides real time useful “warning alerts” to central bankers and fiscal regulators. The complexity of their nonlinear structure and the inherent sudden break mechanisms makes the econometric testing challenging. The new recursive flexible window methodology provided by Phillips, Shi, and Yu (2015) gives consistent results and delivers significant power gains when multiple bubbles occur. It successfully identifies well-known historical episodes of exuberance and collapse. In this paper we look at the Indian stock market indices, the SENSEX, and the NIFTY 50, to see if there is any evidence of a bubble there. We use monthly data for each series, with the Sensex data spanning April 1979 to October 2018 and NIFTY 50 data spanning July 1990 to October 2018. The existence of bubbles in this index will give us some indication of where bubbles are more likely to occur, and therefore provide evidence of potential economic (financial) crises.

“The British Lions Crouched to a Nest of Owls”: The South Sea Bubble through the Lens of the London Press

The South Sea Bubble is one of history's most iconic economic events. While much ink was spilled during the bubble year to make sense of events as they unfolded, commentators were left scrambling for ways to grasp what happened because no one had ever experienced a stock market bubble before. This article focuses on how the London press covered the events that later became known as the South Sea Bubble. A review of every newspaper article in which the company was mentioned during the year of 1720 captures how the movement of the price of the company's stock was interpreted, as well as how it was related to broader social, political, and geopolitical affairs.

Explosive behavior in the Chinese stock market: A sectoral analysis

This paper examines the existence of explosiveness in the Chinese stock market from a sectoral perspective. To that end, the method recently developed by Phillips and Shi (2019), which is an extension of the standard bubble detection technique of Phillips et al. (2015a,b), is used. The empirical results reveal the presence of several explosivity periods in all equity sectors and the aggregate equity market, showing the high propensity of the Chinese stock market to the appearance of episodes of explosiveness. One possible explanation for this frequent explosive dynamics stems from the fact that the Chinese equity market has numerous unique features and is still in an early stage of development. The two main periods of explosiveness common to many Chinese equity sectors correspond to the well-known 2007 and 2015 Chinese stock market bubbles. However, there seems to be some heterogeneity across sectors in terms of the number and duration of explosivity episodes. Furthermore, the significant degree of co-movement of periods of explosiveness in different equity sectors suggests potential contagion effects of explosive dynamics to the whole Chinese equity market, with the consequent detrimental impact on the Chinese real economy.

Sequential monitoring of stock market price changes

In 2016, Eugene Fama mentioned that he wanted a systematic way of identifying and predicting a stock market bubble. This paper develops a statistical method to sequentially monitor the stock market price changes. A new simple boundary function is proposed and asymptotic properties are established. In the empirical application, we successfully detect that the U.S. dot-com bubble occurred around June 1997 using the S&P 500 and Nasdaq indices.