Price Bubbles Research Articles

Predictive Modeling for Asset Bubble Detection in Financial Markets

This paper presents a comprehensive approach to predicting and detecting asset bubbles in financial markets, utilizing advanced analytics, machine learning models, and deep learning techniques. The study focuses on the S&P 500 as a representative indicator of the U.S. stock market, aiming to develop a robust methodology for identifying early signs of asset bubbles and providing actionable insights for investors, financial institutions, and policymakers.

Does every cloud (bubble) have a silver lining? An investigation of ESG financial markets

This paper investigates the presence of financial bubbles in the environmentally friendly investments captured by the ESG markets. By using the log-periodic power law singularity framework, we identified several periods of positive and negative bubbles in the short, medium, and long term. Moreover, we examined the relationship between ESG attention sentiment and financial bubbles. We found an asymmetric effect of ESG sentiment on financial bubbles, i.e., increasing positive and decreasing negative bubbles. Our empirical results provide valuable insights into the stability of environmentally friendly markets, which help risk managers and policymakers respond appropriately to financial and social bubbles.

Soccer Bubble: Is There a Speculative Bubble in the Price of International Soccer Players?

An exponentially increasing trend of players’ registration prices has taken place since 2012, and we test the hypothesis that this may be a speculative bubble, using a structural break test (Chow test) on a data set of international soccer players' registration prices, that ranges from 2007 to 2019. The test confirms the presence of an abrupt change in the trend of these prices since the year 2012, both for the whole data set and for subgroups based on the types of competition considered (Champions League, Europa League, and residual) and on the age of the players. We surmise that a possible root at the base of this trend is the need for soccer teams to cope with the Fair Play regulation, which has taken place exactly in the same years, and caused an excess of players’ trading as a means to comply with the obligation for teams to balance their books over time.

Прогнозування рецесій у реальному часі (на прикладі США)

This article presents the principles and results of real time forecasting of recessions in the US economy using macroeconomic forecasting models, which has, as the model’s output, a single indicator of general economic activity that can be monitored monthly or at least quarterly: GDP-Based Recession Indicator Index; 2) Real-time Sahm Rule Recession Indicator; 3) Smoothed U.S. Recession Probabilities; 4) Composite Leading Indicator Index; and 5) Yield Curves Inversion Model. Usually, these models are used in practice by government regulators and business to make their decisions in real time, as they are simple to apply and are updated regularly. However, these models have no sufficient theoretical grounding, therefore it is difficult to define the best model to apply in practice, especially in the case of missed or false signals about impending recession or in the case of various forecasting results obtained from different models. Besides, this paper presents a US economy forecast made using the author’s CMI-model of macroeconomic dynamics, which has all advantages of the above mentioned models, but also has theoretical grounding for its single index of general economic activity that can be updated monthly. According to the CMI-model we may expect a new US recession (in accordance with official NBER methodology of a business cycle dating) at the end of second quarter of 2024. At the same period, we can expect a persistent growth in unemployment. As to the financial sector of US economy, its dynamics for the next few months will depend on Federal Reserve (Fed) policy. If Fed starts to decrease Federal Fund rate, one can expect new and significant absolute values (financial bubbles) for key financial indexes. If Fed is not able to reduce Federal Fund rate due to high inflation, financial indexes will possibly hold their high levels in average (or even increase as a result of expectations for the Federal Fund rate reduction or as a result of positive news from individual economic sectors or companies). The development of the financial bubble may continue until statistic data will be able to confirm the start of a new recession that would initiate a huge financial crisis. Obviously, the possible reduction of Federal Fund rate will increase the power of the expected financial crisis. In any case, such a financial crisis, initiated by a new recession, will probably occur in second half of 2024, since the statistics, which reflect the past state of the economy, will continue generating ambiguous signals as to the possibility of a new recession. And only closer to the end of 2024 unambiguous statistical data about the state of economy at the middle of the year will appear. However, the official dating of the recession in NBER terms will be probably done even later. In this case it will be the middle of 2025 or even later. Usually, the deeper the recession is, the easier and earlier it can be dated. To my mind, even if Fed reduces the Federal Fund rate, it may fail to help avoid new recession. Moreover, a significant level of inflation is unlikely to allow a relatively rapid reduction of the discount rate or any other aggressive boost for the economy.

Monetary policy uncertainty and the price bubbles in energy markets

We examine the relationship between the monetary policy uncertainty (MPU) and the price bubbles in U.S. oil futures, including WTI crude oil future, heating oil future, and gasoline future. The Log Periodic Power Law Singularity (LPPLS) model is firstly used to analyze and validate the price bubbles of U.S. oil futures. We find that (1) the timing of local peaks of MPU closely aligns with the occurrence of price bubbles in U.S. oil futures, (2) MPU is significantly correlated with crude oil future price bubble but not with heating oil future (or gasoline future) price bubble, and MPU significantly amplify the magnitude of price bubble risk for crude oil future, (3) MPU is an effective set of factors for machine-learning based identification of all three U.S. oil future price bubbles in the non-linear model, even though the impact of MPU on heating oil (or gasoline) future price bubble is not significant in logit regression as the linear model. Collectively, our findings highlight the significance of MPU in relation to price bubbles in U.S. oil futures, offering new insights for investors and policymakers to explore the (non)-linear relationship between MPU and price bubbles in energy markets.

WHERE IS CREDIT IN THE PRICE SPECIE FLOW?

Standard models of the price specie flow do not consider credit. Yet Hume and preceding authors were reacting to the implosion of Law’s financial bubble. We delineate the anti-credit thesis contained within the evolution of eighteenth-century balance of payments analyses. A string of eighteenth-century authors argued over whether the balance of payments constituted a binding constraint on credit creation. As part of their analysis they considered how changes in the money supply might alter output, prices, employment, capital, and population. How new money entered the economy was often critical. We start with Law and then consider Melon, Gervaise, Vanderlint, Cantillon, Montesquieu, Hume, Steuart, Forbonnais, and Smith. In closing we pay particular attention to the idea that Hume and Smith effectively displaced preceding, often “mercantilist,” analyses of credit and the balance of payments.

The Bubble Metropolis: Manhattan Island Crises in Contemporary Science Fiction

As “the island at the center of the world,” Manhattan has inspired countless writers and has served as a spatial archetype in science fiction’s world-building. From the interdisciplinary perspective of literature and economics, this article discusses the crisis imagination of the “bubble metropolis” in five contemporary Manhattan-related science fiction novels including Cities in Flight (1970), The Blister (1975), Terminal World (2010), Zone One (2011), and New York 2140 (2017). The spatial variety of Manhattan Island in these science fiction novels is closely combined with its economic condition. The characters, plots, and spatial imagery of these novels gather to reflect the different stages of the operation of a bubble economy, illustrating a historical cycle of capitalism that can never be escaped. Manhattan Island has long been the symbol of the world’s rush for wealth. The fear of economic recession, environmental degradation, and class conflict have formed the special geographical features of the island in the future. The crisis imagination of the “bubble metropolis” also seeks to stimulate critical thinking on economic ethics, urban design, and high technology, calling for social justice and public welfare.

How is the ECB’s quantitative easing transmitted to the financial markets?

PurposeOver the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.Design/methodology/approachFirst, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).FindingsThe results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.Practical implicationsThe findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.Originality/valueTo the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

Energy transition and housing market bubbles: Evidence from prefecture cities in China

Governments are committing to realizing the energy transition to create a sustainable and resilient energy future, while less is known about the real effects of the energy transition on various sectors of the economy, particularly the tertiary industry. This paper thus investigates the relationship between the energy transition and housing market bubbles at the level of prefecture cities in China. We find that an increase in one standard deviation of the energy transition decreases approximately 39% of the average housing bubbles index. Furthermore, the digital economy and land transfer income of real estate enterprises could be underlying economic channels to significantly explain the impacts of the energy transition on housing market bubbles. Finally, we identify that green finance including green loans and green bonds enhances this negative effect on housing market bubbles. Our main result remains valid in a battery of robustness tests. This study provides important practical implications for policy makers, enterprises, and investors.

Assessing whether China will experience the lost decade of Japan

ABSTRACT Japan’s lost decade has long puzzled economists, specifically as to what brought it about. As China’s current economic conditions resemble Japan’s in the 1980s, this has prompted concern China might suffer a similar fate. This paper identifies that the response of the Japanese central bank toward the Plaza Accord, which is unfettered lending was the key factor of lost decades. By contrasting Sino-US and Japan-US trade wars, this paper argues that China will not fall foul of the same situation of accepting a new Plaza Accord. By comparing China’s bubble economy now with Japan’s bubble economy in the 1980s, this paper argues that the fallout from a burst Chinese bubble is expected to be less severe than Japan’s early 1990s. Furthermore, by analyzing the two countries’ respective debt problems and aging populations, it is concluded that these issues pose no significant threat to Chinese economic growth in the short term.

Rational Investing or Speculative Fever?: SPACs, Robinhood, and Digital Assets—Securities Markets or Casinos?

This article focuses a recurring theme – speculation in the financial markets. The 2010-2020 decade set the stage for a new round of speculative activity starting in 2021. In the article that follows I reflect on a new wave of speculation and three current examples of speculative activity. The article concludes that regulators should be cautious about over-regulation of SPACs and gamified trading. The article also supports the regulation of digital assets (crypto currencies and NFTs) as securities.

Market Manipulation of Cryptocurrencies: Evidence from Social Media and Transaction Data

The cryptocurrency market cap has experienced a great increase in recent years. However, large price fluctuations demonstrate the need for governance structures and identify whether there are market manipulations. In this article, we conduct three analyses—social media data analysis, blockchain data analysis, and price bubble analysis—to investigate whether market manipulation exists on Bitcoin, Ethereum, and Dogecoin platforms. Social media data analysis aims to find the reasons for price fluctuations. Blockchain data analysis is used to find detailed behavior of the manipulators. Price bubble analysis is used to investigate the relation between price fluctuation and manipulators’ behavior. By using the three analyses, we show that market manipulation exists on Bitcoin, Ethereum, and Dogecoin. However, market manipulation of Bitcoin is limited, and for most of Bitcoin’s price fluctuations, we found other explanations. The price for Ethereum is the most sensitive to technical updates. Technical companies/teams usually hype some new concepts (e.g., ICO, DeFi), which causes a price spike. The price of Dogecoin has a high correlation with Elon Musk’s X (formerly known as Twitter) activity, showing that influential individuals have the ability to manipulate its prices. In addition, the poor monetary liquidity of Dogecoin allows some users to manipulate its price.

Detection and forecasting of housing price bubbles in Indonesia, Malaysia, and Singapore

Detection and forecasting of housing price bubbles in Indonesia, Malaysia, and Singapore

The price effect of temporary short-selling bans: Theory and evidence

We develop a model of temporary short-selling bans by extending the infinite-horizon model of Scheinkman and Xiong (2003). Our model predicts that a temporary short-selling ban leads to a speculative bubble that is the highest at the beginning of the ban and gradually converges to zero. Examining the 2008 short-selling ban in the U.S., we find evidence consistent with the model’s predictions. The innovation of our empirical design is to use the financial segments of non-banned stocks as a control group for the banned financial stocks. Our results are robust across different test specifications and different samples of stocks.

Evaluating the Influence of AI on Market Values in Finance: Distinguishing Between Authentic Growth and Speculative Hype

This article ventures into the intricate realm where the distinctions between authentic growth and speculative bubbles from impact of AI (Miller, 2003), celebrated and critiqued as a buzzword, emerges as a transformative element reshaping entire industries, workflows, and fundamentally altering market valuations (Perri, 2023). The promise of efficiency, innovation, and competitive superiority attributed to AI's integration into business models beckons a deeper investigation into its potential to redefine the competitive landscape. However, this promising horizon is not without its perils and scrutiny. A predominant debate as we navigate up to the year 2023 revolves around whether the market's enthusiastic reception of AI capabilities signifies a realistic reassessment of potential based on tangible fundamentals or if it's a manifestation of speculative exuberance, detached from any solid grounding (Baltrusaitis, 2023). This exploration addresses some implications for investment strategies (GuoRong Hu*, Hui Liu , 2020), regulatory frameworks, and potentially influencing the direction of future technological advancements. It endeavors to dissect AI's multifaceted roles in contemporary business ecosystems, scrutinizing its impacts on corporate valuations and attempting to demarcate the fine line separating real growth from speculative froth. Through a detailed examination, this paper illuminates how AI technologies foster operational efficiencies, drive innovation, and unlock unprecedented insights, all the while carefully navigating the surrounding hype to assess overvaluation risks (Svetlova, 2022) and emerging pitfalls. The journey through the AI landscape reveals a spectrum of inspiring successes and cautionary tales, guiding the current nuanced discussion toward clarifying AI's impact on capital markets and corporate valuations understanding for investors, business leaders, and technology aficionados aiming to make informed decisions (Tania Babina , Anastassia Fedyk , Alex He, James Hodson, 2024).

Asset bubbles and wealth inequality

AbstractWe examine the causal link between asset bubbles and wealth inequality in a two‐agent macroeconomic model. Bubbles influence wealth inequality through two channels: altering the debt–asset ratio and fuelling speculation. When bubbles grow, they can temporarily decrease wealth inequality if asset prices rise faster than debt. However, when they burst, wealth inequality increases as the debt–asset ratio rises. Steady‐state wealth inequality is unaffected by bubbles if household types share symmetric speculative timing. Although macroprudential policy, communication, and leaning against the wind can reduce negative bubble effects on aggregate utility, they have a limited effect on wealth inequality.

Cryptoart: ethical challenges of the NFT revolution

The digital transformation of the art world has become a revolution for the sector. Cryptoart, based on non-fungible tokens (NFT), is attracting the attention of artists, collectors and enthusiasts for its ability to tokenise any element that can be sold as art in the digital market. In this way, it is able to become a scarce resource and an economic asset by encapsulating the market value of a piece of digital art, which may or may not have a reference in the real world. This study will delve into the ethical aspects underlying what is known as the NFT Revolution, particularly impacts related to the abuse or destruction of cultural heritage, speculation and the generation of economic bubbles and environmental unsustainability. To this end, this research has been carried out within the framework of a hermeneutic-critical proposal for analysing, understanding and prescribing cryptoart and its processes. This, methodology, typical of the human and social sciences, critically analyses the current context of the digital transformation of art through the study and interpretation of bibliographical sources and case studies in order to reconstruct the keys and conditions of possibility that guide its development in a fair and responsible way.

Detection of financial bubbles using a log‐periodic power law singularity (LPPLS) model

AbstractThis article provides a systematic review of the theoretical and empirical academic literature on the development and extension of the log‐periodic power law singularity (LPPLS) model, which is also known as the Johansen–Ledoit–Sornette (JLS) model or log‐periodic power law (LPPL) model. Developed at the interface of financial economics, behavioral finance and statistical physics, the LPPLS model provides a flexible and quantitative framework for detecting financial bubbles and crashes by capturing two salient empirical characteristics of price trajectories in speculative bubble regimes: the faster‐than‐exponential growth of price leading to unsustainable growth ending with a finite crash‐time and the accelerating log‐periodic oscillations. We also demonstrate the LPPLS model by detecting the recent bubble status of the S&P 500 index between April 2020 and December 2022, during which the S&P 500 index reaches its all‐time peak at the end of 2021. We find that the strong corrections of the S&P 500 index starting from January 2022 stem from the increasingly systemic instability of the stock market itself, while the well‐known external shocks, such as the decades‐high inflation, aggressive monetary policy tightening by the Federal Reserve, and the impact of the Russia/Ukraine war, may serve as sparks.This article is categorized under: Applications of Computational Statistics > Computational Finance Algorithms and Computational Methods > Computational Complexity Statistical Models > Nonlinear Models

Market- and future-level sentiment and futures returns in Chinese agricultural futures markets

This study examines market and future-level sentiment in the Chinese agricultural futures market, distinguishing between contagious and idiosyncratic sentiment. Our analysis reveals that agricultural future-level sentiment is significantly affected by market-level sentiment and domestic stock market sentiment, particularly during bullish market conditions, while global stock market sentiment has minimal impact. Furthermore, we find that both market and future-level sentiment strongly and positively influence futures returns, with contagious and idiosyncratic sentiment exerting favorable influences, particularly in bullish markets. Our findings provide clear evidence of sentiment's impact on agricultural futures returns, offering valuable insights for regulators to identify potential market bubbles and implement effective sentiment regulation measures.

Um ESTUDO DAS FINANÇAS COMPORTAMENTAIS ASSOCIADAS AS BOLHAS E CRISES

This article aims to identify the relationship between behavioral biases and speculative crises and bubbles. For this purpose, a systematic literature review was carried out, analyzing the publications made between 1960 and 2020, which were included in the Web of Science and Scopus databases. In conducting the research, the protocol by Tranfield, Denyer and Smart (2003) was adopted. And to carry out the analysis, the following softwares was used: Microsoft Excel®, VOSviewer, Iramuteq and RStudio. The main contributions that this article brought to the literature were to present a portfolio of theoretical approaches on the subject, as well as to identify the main authors and works in the area.