Stock Market Bubble Research Articles

Diagnosing stock market bubbles with advanced ADF tests

A full-fledged empirical test of the latest methodology in the field of recursive procedures for identifying and dating market bubbles - the GSADF test - has been performed. The use of previous iterations of recursive tests did not allow to accurately determine the presence of a market bubble at the early stages of its formation. The results of this study prove that the GSADF test using a sliding window significantly improves the discriminatory ability of recursive tests, at the early stages of formation it allowed to detect such episodes of stock market collapse as the Japanese economic bubble of 1986-1991 and the dot-com bubble in the USA in 1995-2001. Testing based on current market data demonstrates the formation of a market bubble in the NASDAQ stock index since August 2020.

Stock bubbles under sudden public crises: A perspective from the excessive financialization of firms

This paper examines the crowding-out effect of the sudden onset of COVID-19 on the share price bubbles of over-financialized firms, and identifies the mechanisms responsible for this effect. Our findings suggest that COVID-19 has a significant crowding-out effect on the share price bubbles of over-financialized firms, which can be attributed to its impact on subjective investor sentiment and objective stock liquidity. The crowding-out effect is heterogeneous between different ownership, debt structure, and COVID-19 pandemic shocks. Our research suggests that the virtual economy may face a higher risk of the bubble bursting under the sudden public shock.

An Investigation on Real Estate Market Dynamics and Bubble Formation Modeling

Abstract A speculative bubble arises when the market price of a financial asset diverges from its fundamental value, resulting in abrupt and disproportionate hikes that cannot be rationally explained by its underlying intrinsic value. This behavior is often followed by market crashes, as observed during historical asset bubbles such as the Dutch Tulip Mania (1634-1637), the Mississippi bubble (1719-1720), the South Sea bubble (1720), the stock-market bubble of the US (1921-1929), the Japanese stock market and real estate bubbles (1986-1991), and the recent US housing bubble and the stock market crash (2002-2006). In the housing market, speculative bubbles can be attributed to excessive public expectations of future price increases, leading to overpricing and eventually diminishing demand and instability of inflated home prices (Case and Shiller, 2003). The cycle of a housing market bubble involves individuals continuing to purchase houses despite awareness of overpricing, with the expectation of compensation through further price increases. This perception of continuous price increases leads to increased demand, which further drives up prices. However, house prices cannot increase indefinitely, and the steady increase eventually becomes unstable, resulting in a downward adjustment that may occur at a much faster pace than price increases. The existence of speculative bubbles in stock and housing markets has led academics to investigate their potential role in financial crises.

Correlation Analysis of Stock Markets in the Belt and Road Regions

As a regional cooperation framework, the Belt and Road Initiative (BRI) aims to spur economic development by investing in infrastructure and improving connectivity between the countries in Asia, Europe, and Africa located along the Silk Road Economic Belt and the 21st Century Maritime Silk Road. Many studies have documented that BRI-related investment projects reduce trade costs, increase trade and investment flows, bring benefits to trading parties, and improve connectivity between countries along the Belt and Road. Financial integration is a critical aspect of intangible connectivity that is fundamental to and benefits from increased trade and investment flows between countries. This study examines the pairwise correlations between 39 stock markets in the Belt and Road regions from 2008 to 2018, focusing on the correlations between China and the other markets. We find that the correlation between two markets in the same region is greater than those in different regions. China has a higher correlation with markets in Southeast Asia and North and Central Asia than in other regions. The correlations fluctuate over time, but their relative rankings among market pairs remain stable. In the first half of 2015, there was an unusually low correlation between China and all other markets, most likely due to China’s stock market bubble. Our systematic examination of stock market correlations in the Belt and Road regions sheds new light on the temporal and cross-sectional dynamics in these understudied financial markets. The findings suggest significant potential for financial integration in the Belt and Road regions.

A bubble identification mechanism: Evidence from the Chinese stock market

AbstractThis paper provides a bubble date‐stamping mechanism using the agent‐based computational finance method. The key steps of the bubble date‐stamping mechanism are the construction of the simulated financial market, the computation of the characteristic indexes, and the thresholds of the Price Band in the simulated financial market. The present study adopts the mechanism to identify the bubbles of sample stocks in the Chinese stock market from April 2003 to December 2019. The findings show that the bubbles are primarily distributed in 2006–2008, 2009–2012 and 2014–2018, respectively. Furthermore, we analyse the bubble strength and the price fluctuation during the above three periods. In addition to the bubble date‐stamping mechanism, the paper also studies the factors that drive the bubbles in the Chinese stock market from both macro and micro perspectives.

Literature Review on Detecting Bubbles in Stock Market

Stock market bubbles are times when valuation dynamics are momentarily explosive. Stock bubbles can be interpreted as a deviation of stock prices from their fundamental value. LPPLS is one of model to calculate bubble, which assuming that an asset's observed price trend during a bubble era differs from its intrinsic fundamental value. This research is qualitative study or descriptive research to review the LPPLS as a model to calculate bubbles in stock. This study uses secondary data sourced from articles which published in Scopus Indexed International Journal. The conclusion of the research that the LPPLS model can calculate, predict and identify accurately bubbles related historical event such as the black Monday, dot com, and subprime crisis periods. The LPPLS model has the advantage, including construct end of bubble signals accurately.

Predictions of Tesla Stock Price based on Linear Regression, SVM, Random Forest, LSTM and ARIMA

The stock market of a country is an important financial market. A booming stock market promotes the effective use of social capital, the prudent deployment of economic resources, and the expansion of the country's macroeconomics. Making more informed decisions as an investor is made possible by the development of trustworthy equity market models. A trading model allows market participants to select corporations that pay the highest dividend payments while lowering the risks associated with investing. However, batch processing methodologies make stock market research more challenging as a result of the strong connection between stock prices. The advent of technological achievements like universal digitization has elevated share market forecasting into a highly advanced age. Through the research and comparison of several methodologies, this article tries to discover the most accurate approach for predicting Tesla stock closing prices. Predictions are made using statistical approaches such as ARIMA and machine learning methods such as SVM , Linear Regression, Random Forests, and LSTM. Following a thorough examination of all approaches, it was discovered that the accuracy of machine learning methods in predicting stocks is higher than that of statistical methods and integrated algorithm technologies like Random Forest have excellent anti-interference and anti-overfitting characteristics, which are more suitable for evaluating high-volatility stocks like Tesla.

Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

Behavioral asset pricing under expected feedback mode

We develop an enhanced DSSW model of behavior asset pricing by introducing the expected feedback mode. Through numerical simulation, it has been theoretically proved that risky asset price is jointly determined by trend extrapolated effect of expected feedback traders and the creating space effect of noise traders, and that price fluctuation depends on the expected feedback coefficient. As expected feedback traders' expectation return has been realized and the above two effects are obviously imbalance, their confidence will deteriorate prior to price collapse, and eventually achieve self-fulfilling expectation of price reversal in the process of price momentum. This model sheds lights on financial anomalies of trade size clustering and the formation of stock bubble, besides it provides a new perspective for avoiding the “bewitching” trading and reducing market volatility.

Photovoltaic Companies on the Warsaw Stock Exchange—Another Speculative Bubble or a Sign of the Times?

Renewable energy sources are an attractive alternative to fossil fuels for several reasons. Firstly, these are ecological arguments, mainly reducing greenhouse gas emissions. Secondly, there are legal issues, particularly the obligations of the European Community countries in the field of climate as part of the implementation of the European Green Deal and the joint achievement of 40% of energy from renewable sources by 2030. Thirdly, these are international issues, primarily regarding reducing dependence on uncertain oil or gas markets. And finally, they may be economic reasons, such as diversification of energy supplies and associated costs, as well as opportunities for profits on the capital market. In Poland, over the last decade, a certain kind of boom in photovoltaics has been visible, both in terms of the number of companies dealing with solar collectors, as well as the annual increase in new capacity, or the level of installed capacity. Also, on the Warsaw Stock Exchange, photovoltaic companies have introduced much confusion in the tier of quotations in recent years. Solar energy has become a kind of gateway for companies to increase their results, stock exchange quotations, or acquire new customers. It is not surprising that more and more investors want to invest their money in this segment. Given the above, this article attempts to answer the question: Is there a risk of a stock market bubble among photovoltaic companies in the near future? For this purpose, we used the financial indicators of photovoltaic companies listed on the Warsaw Stock Exchange, and with the help of the Taxonomic Measure of Attractiveness of Investments, we created rankings of the investment attractiveness of these companies in 2017-2019. The leaders include companies listed on the main market as well as in the alternative trading system of the Warsaw Stock Exchange. It should be borne in mind that regardless of the undertaken diversification and analytical activities, the risk of an investment bubble has been and will remain an indispensable element in the functioning of every capital market in the world.

Identifying stock market bubbles and evaluation with momentum index and CCI

Stock markets ought to be efficient while stock prices reflect all available information fairly and equitably. This bubble majorly forms when market participants inflate stock prices above the stock value based on some valuation system. Identifying the formation of these bubbles becomes imperative to provide information for the investors and these bubbles need to be validated as well, hence this paper contemplates to identify the existence of stock market bubbles between 2017 and 2022 with respect to the BSE Sensex. It also aims to analyse, if the momentum index and consumer confidence index reflect the market bubbles along with performance when the bubbles crashed. The E-views platform for the data analysis and Dickey-fuller, Augmented Dickey-Fuller, Rolling ADF, and Supremum ADF tests were deployed for identification of bubbles. It is found that a major market bubble occurred between January-May 2020. Apart from identifying the bubble that occurred previously, this paper also intends to alert investors of future bubbles following the same pattern of indices. By analysing contributing factors leading to a bubble, investors can be better prepared for the next bubble and can adjust their strategies accordingly to minimize losses. Identification of stock market bubbles and validating them with momentum and consumer confidence indices just doesn’t suffice the investor requirements. Further the factors leading to the formation of these bubbles, nature of these bubbles also attention. Bubbles happen for commodity prices, crypto-currencies and often might be of different types and might reflect entirely different behaviour, in that case how far can momentum and CC indices serve as leading indicators need to be explored as well.

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.

Collective Effervescence in Retail Investors: An Exploratory Inquiry to Understand the Meme Stock Bubble

Collective Effervescence in Retail Investors: An Exploratory Inquiry to Understand the Meme Stock Bubble

Estimation on Rational Speculative Bubbles in Stock Market by Using Generalised Johansen-Ledoit-Sornette Model

This paper discusses the generalized Johansen-Ledoit-Sornette (GJLS) model in determining and predicting the size of rational speculative bubbles in stock market. In this study, the GJLS model is used as an approach to detect the presence and prediction of rational speculative bubble sizes of the stock market. The stock markets chosen in this study are Hang Seng, Nikkei 225 and S\&P 500. This is because China, Japan and United States are countries that pioneers the world economy. This paper presents step by step on finding the linear and non-linear parameters in the generalized Johansen-Ledoit-Sornette model using the Ordinary Least Squares method for the linear parameters and formulas for the non-linear parameters. There are 7 parameters that need to be identified which are A,B and C for linear parameter and ω, φ, β and t for the non-linear parameter. Then, the size of speculative rational bubbles is determined and predict using the GJLS model. The size of speculative rational bubbles for future cycle is forecasted. Then, the effect of the presence of rational speculative bubbles towards the economy is discussed.

The Green Stock Market Bubble

One may contend that there are some clear similarities between the market for sustainable assets now and the situation of tech stocks right before the collapse of the dot-com bubble. In fact, before 2000, the risk of the new technology was primarily idiosyncratic because of the limited scale of production and the low likelihood of a widespread adoption. Subsequently, with an increasing probability of adoption, also, the old economy was affected by the new technology and, hence, the wealth of the representative agent. As a result, systematic risk increased, which depressed stock prices, because it pushed up the discount rates in the new and old economies. Similarly, given the high probability of a large-scale adoption of the new “green technology,” it is likely that there is not only a bubble forming in green energy stocks, but the boom is affecting the stock market as a whole. I apply a recently developed recursive testing procedure and dating algorithm that is useful in detecting multiple bubble events. Using S&P 500 stock market data, price-dividend ratios, I identify the well-known historical speculative bubbles and find an explosive movement in today’s market starting in June 2021, which can be associated with the new “green technology.” I find that an explosive movement in green stocks started roughly a year before it was migrating to the whole stock market. I argue that this is a good bubble because it will enable businesses to invest cheaply in green energy, hastening the transition away from fossil fuels and assisting in the fight against climate change.

US monetary policy and BRICS stock market bubbles

We use the multi-scale Log-Periodic Power Law Singularity (LPPLS) confidence indicator approach to detect both positive and negative bubbles at short-, medium- and long-run for the stock markets of the BRICS countries. Then, we utilize impulse responses obtained from the local projection method (LPM) framework to capture the effect of US monetary policy shocks on the BRICS bloc equity market. The effect of these shocks on the bubble indicators for each country is limited, with a strong positive impact observed under the medium-term negative bubble indicator of Brazil, China and South Africa. Given the findings, associated policy implications are discussed.

Trading behaviours during stock market bubbles: evidence from Vietnam

ABSTRACT We study investors’ trading behaviours during bubbles on the Vietnam stock market. Vietnam offers a great setting due to its high susceptibility to bubbles and large heterogeneity among various groups of investors. We document evidence of trading explosions during two bubbles identified in our sample period, followed by reversions in the aftermath. Financial sectors make a significant contribution to this phenomenon in both episodes. We also find that foreign and sophisticated investors increase their holdings when the bubbles form and start selling well before the bursts, suggesting their superior ability to local individuals in timing the market.

The mirror of history: How to statistically identify stock market bubble bursts

This paper proposes a method for detecting bubble phases and the timing of bursts in global stock markets. The study identifies 27 bubbles in 29 global stock market indices over the last century. Using transformations and change-point detection on index returns, we discover that every major bubble has the same two-phase pattern indeed. Although the mechanisms or causes of each bubble are complex and unique, they all follow the same pattern. Thus, our findings suggest that history has a tendency to repeat itself.

Investor Emotions and the Psychodynamics of Asset Pricing Bubbles: A Chinese Perspective

This paper explores the powerful emotions unleashed during asset pricing bubbles. Adopting a psychoanalytic perspective, we develop a five-stage path-dependent model of such financial crises and test this empirically on the Chinese 2005–2008 and 2014–2016 stock market bubbles. Results are consistent with our underlying theory and demonstrate how investors experience a range of highly charged emotions directly related with different market states during such episodes. Our evidence suggests that if we wish properly to understand and explain such destructive events, we also need to recognize the fundamental role investor unconscious fantasies and market psychodynamic processes play in their etiology.

Developing a Regime-Switching Present Value Model: Switching Fundamentals and Bubbles

We develop a present-value model where the fundamental and non-fundamental components switch between distinct regimes. The non-fundamental component is specified as a periodically collapsing bubble of Balke and Wohar [Balke, N. S., & Wohar, M. E. (2009). Market fundamentals versus rational bubbles in stock prices: A Bayesian perspective. Journal of Applied Econometrics, 24(1), 35–75. https://doi.org/10.1002/jae.1025]. The fundamental component is constructed as in van Binsbergen and Koijen [van Binsbergen, J. H., & Koijen, R. S. J. (2010). Predictive regressions: A present-value approach. The Journal of Finance, 65(4), 1439–1471. https://doi.org/10.1111/j.1540-6261.2010.01575.x], by treating the expectations of market fundamentals as latent variables. Unlike existing methods, e.g. [Zhu, X. (2015). Tug-of-war: Time-varying predictability of stock returns and dividend growth. Review of Finance, 19(6), 2317–2358. https://doi.org/10.1093/rof/rfu047; Choi, K. H., Kim, C., & Park, C. (2017). Regime shifts in price-dividend ratios and expected stock returns: A present-value approach. Journal of Money, Credit and Banking, 49(2–3), 417–441. https://doi.org/10.1111/jmcb.12384; Chan, J. C., & Santi, C. (2021). Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach. Journal of Economic Dynamics and Control, 127, 1–26], ours requires no unnecessary approximations, accommodates flexible forms of regime-switching, and the resulting present-value formula is internally consistent.