Market Collapse Research Articles

Sowing the Seeds of Financial Crises: Endogenous Asset Creation and Adverse Selection

Abstract What sows the seeds of financial crises, and what policies can help avoid them? I model the interaction between the ex-ante production of assets and ex-post adverse selection in financial markets. Positive shocks that increase market prices exacerbate the production of low-quality assets and can increase the likelihood of a financial market collapse. The interest rate and the liquidity premium are endogenous and depend on the functioning of financial markets as well as the total supply of assets (private and public). Optimal policy balances the economy's liquidity needs ex-post with the production incentives ex-ante, and it can be implemented with three instruments: government bonds, asset purchase programs, and transaction taxes. Public liquidity improves incentives but implies a higher deadweight loss than private market interventions. Optimal policy does not rule out private market collapses but mitigates the fluctuations in total liquidity.

Past anthropogenic impacts revealed by trace elements in cave guano

Natural and human-induced toxic elements can accumulate in the environment, posing significant risks to human health and ecosystems. This study explores cave bat guano, an unconventional and relatively under-researched environmental repository, to reveal historical pollution trends and sources. Through trace elements analysis of a 1.5-m-thick guano deposit from Zidită Cave (Romania), we track changes in mining and metallurgy from 1000 CE–2012. We identified substantial pollution primarily from porphyry copper and Au–Ag–Te mines, but also impacts from usage of leaded gasoline and agricultural practices. Our record shows disruptions caused by the Bubonic plague around 1250 CE and a major surge ∼ 1500 CE. After the decline triggered by the European silver market collapse in 1525 CE, our study reveals a brief mining revival. This resurgence was followed by a continuous decline lasting until the early 1800s, driven by socio-economic upheavals and recurrent outbreaks of the bubonic plagues. The Industrial Revolution sparked prolonged growth that lasted until 1989 CE, only briefly interrupted by the Great Depression and World War II. Consequently, cave bat guano proves to be a critical resource for understanding spatial pollution patterns, both locally and regionally, and for identifying specific pollution sources.

Silicon Valley Bank Collapse: Causes & Consequences

This paper discusses the concept of bank bankruptcy and highlights the recent failure of Silicon Valley Bank (SVB) as a significant case similar to the Lehman Brothers' collapse in 2008. Lehman's failure was attributed to the U.S. housing market collapse and internal risk management issues. SVB's failure, occurring in 2023, resulted from both internal factors (improper decisions, high-risk strategies, and poor financial management) and external factors (economic recession, Federal Reserve interest rate hikes, regulatory challenges, and changes in the tech industry). The bankruptcy of SVB has far-reaching economic impacts on depositors, borrowers, entrepreneurs, investors, and the banking industry. It triggers a crisis of confidence, liquidity pressure, and systemic risks. The Federal Reserve implements emergency measures to address the fallout, reflecting on regulatory approaches to mitigate threats to the banking system. The comparison with Lehman Brothers underscores similarities in improper development strategies, asset structures, risk management, and regulatory oversight, leading to systemic risks and undermining financial market confidence.

Manias, panics, & land: The property bubbles of the great Chinese crash of the 1880s

Historians researching China’s debt crisis of the early 1880s have largely focused on its stock market collapse of 1882-83. Yet the crisis was more than this—it was also a real estate crash. More precisely, the crisis entailed the bursting of two asset price bubbles in urban property: one in Hong Kong (1881), the other in Shanghai (1883). These boom-busts have long remained under-appreciated in both Chinese and global history. This article thus draws on what patchwork data exist, qualitative sources, economic theory, and global historical comparison to reconstruct their histories for the first time. That the crash was a property debacle in these globalising ports, not just a share selloff, suggests that it was no bout of speculation divorced from economic fundamentals, as some posit. Instead, the 1880s crash was a crisis that triggered a major depression, one inextricably bound up with late-Qing China’s integration into the global economy.

The Driving Factors of Stock Price Crash Risk

Factors that increase the likelihood of a stock market collapse are examined in this study via a review of the empirical literature. We focus on the main drivers relating to the potential for a decline in stock prices, including market factors, variables unique to the company, and those industry- and country-wide factors. Our findings suggest a multi-faceted phenomenon linked to the potential fall of stock prices. Market parameters, including liquidity, market capitalization, and volatility, influence the likelihood of a stock price fall.Similarly, firm-specific factors, such as leverage, leverage ratio, and firm size, significantly impact stock price crash risk. Finally, macroeconomic factors, such as GDP growth, inflation rate, and exchange rate, are also important factors that might cause a stock market meltdown. In conclusion, the danger of a fall in stock prices is influenced by various circumstances and is a complicated and multi-faceted phenomenon.

Exploring Volatility: Evolution, Advancements, Trends, and Applications

Volatility is a fundamental notion in financial markets, influencing investment decisions, risk management techniques, and market dynamics. This paper provides a thorough overview of the historical evolution and practical implications of volatility, focusing on important works and key advancements in the field. The overview begins with early conceptions of volatility and the necessity for measurement prompted by market collapses, then progresses to advanced quantitative models and computer tools. The study includes key innovations such as the Black-Scholes model, which revolutionized options pricing and pioneered the concept of implied volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models introduced frameworks for modeling time-varying volatility, paving the way for greater forecasting accuracy. Advancements in computing techniques have made it easier to analyze high-frequency data and estimate realized volatility, providing timely insights into market trends. The review also investigates contemporary trends, such as the use of machine learning algorithms and the issues provided by cryptocurrency marketplaces. Furthermore, the article examines the various characteristics and metrics of volatility, emphasizing its multidimensional nature and diverse uses in risk management, portfolio optimization, derivative pricing, and market analysis. Practical examples show how investors, traders, and financial professionals may use volatility to navigate complex market settings and make sound judgments. Finally, the study highlights the enduring significance of volatility in financial markets and highlights the need for continuing research and analysis to improve our understanding of market behavior. Acknowledging the complexities of volatility prepares market participants with valuable understandings to manage risks effectively and capitalize on market opportunities, thus contributing to financial stability and optimal portfolio performance.

How ESG Disclosure Affects the Stock Price Crash Risk

This study delves into the relationship between ESG (environmental, social, and governance) factors and the possibility of a stock market collapse. While ESG factors have become a pivotal consideration in assessing a company's sustainable and ethical business practices, the dynamics between ESG scores and the stock market collapse risk remain underexplored. Drawing upon a comprehensive dataset from 2009-2020, our research offers an enriched understanding of these factors' interplay. Initial findings suggest a correlation between higher ESG scores and reduced stock price collapse risk. The paper contributes to the broader discourse in three keyways. Firstly, it expands the understanding of ESG's economic consequences, emphasizing its potential protective role against significant stock price downturns. Secondly, by incorporating the perspective of sustainable business practices, the research contributes to the existing literature on the factors that determine of stock price collapse risk. Finally, it underscores the practical implications of promoting ESG in mitigating financial risks, informing both corporate strategies and regulatory frameworks.

The great crypto crash in September 2018: why did the cryptocurrency market collapse?

The cryptocurrency crash on the 5th of September, 2018, resulted in price decreases in 95 of the 100 leading digital currencies. We obtained millisecond data of some of the more prominent cryptocurrencies–bitcoin, ethereum, ripple, bitcoin cash and eos–and some of the smaller cryptocurrencies–neo, nem, omg, tezos and lisk–that were most affected in the crash and investigated what caused the digital market to collapse. We find that the behaviour of the more prominent cryptocurrencies and bitcoin, in particular, was the dominant factor behind the crash. We also find that smaller cryptocurrencies followed the behaviour of the larger ones in the crash. Furthermore, our empirical findings show that the trading behaviour of cryptocurrency traders (CTs) did not trigger the digital market crash. We propose the introduction of a single-cryptocurrency circuit breaker most prominent largest cryptocurrency–bitcoin–that will halt trading during market disruptions.

Risk analysis of China's financial market collapse based on cloud computing and machine learning algorithms

AbstractIn recent years, with the rapid advancement of hardware and software algorithms, information technology has penetrated every aspect of our lives, creating waves of digital transformation in enterprises represented by four underlying technologies, namely: ‘artificial intelligence, big data, cloud computing, and blockchain’. As a result, digital technology has emerged as the primary force behind industrial transformation and upgrading, as well as high‐quality development. Using the data of Shanghai and Shenzhen A‐share listed companies from 2007 to 2020, this paper empirically examines the impact of the digital transformation of listed companies on their stock price crash risk and its mechanism using a two‐way fixed effect model with time and individual effects. It can be seen from the research results that, overall, enterprise digital transformation can significantly reduce the risk of a stock price crash. Further analysis reveals that this risk‐inhibiting effect is more apparent for private enterprises and small and medium‐sized enterprises. In terms of impact mechanism, digital transformation can reduce the risk of a stock price crash by improving the quality of enterprise information internally, while the more analysts pay attention to an issue, the more obvious the role of digital transformation in reducing the risk of a stock price crash for enterprises externally. However, it is worth noting that if the stock price of an enterprise crashes, blindly implementing digital transformation may increase the crash risk. The paper illustrates the practical importance of digital technology from the perspective of enterprises in stabilizing the crash risk of stock prices and improving the performance of the capital market. Meanwhile, it contributes to theoretical research on the impact of digital transformation on microeconomic entities and provides practical knowledge and evidence for enterprises to accelerate digital transformation.

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.

The Dynamic Impact of COVID-19 Pandemic on U.S Stock Market Returns

The global stock market experienced significant detrimental effect during the COVID-19 pandemic era. In the United States, the pandemic had considerable influence on the stock market. Notably, the pandemic negatively impacted the stock market from March, 2020, before there was a recovery from Dec, 2020. During this period, the pandemic significantly increased risks and uncertainties in the U.S. stock market. Particularly, the S&P 500 index during that period exhibits the dynamic significance of coronavirus on the stock market. Price movements in the financial markets resulted from price changes in the S&P 500, which led to a market meltdown. The COVID-19 index raised volatility, which sparked frenetic trading on the stock market. Notably, the epidermic flare-up had caused the S&P 500 index to lose 34% of its value as of August 2020. Also, the index's value fell by 86% as a result of the lengthy market collapse. However, the timely, obligatory, and successful anti-epidemic measures taken by governments led to a positive performance of stocks. As a result, the COVID-19 contagion’s impact on stock returns in the U.S. transformed from negative to positive performance.

Fundamental and speculative components of the cryptocurrency pricing dynamics

The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.

Was tun, wenn der (Gas-)Markt kollabiert?

Abstract A failure of the price mechanism in the gas market cannot be ruled out. The article explains how a market collapse can occur and what effects even the possibility of such a collapse can have. It explains the essential elements that an economic framework should have in the shadow of a possible market collapse.

Strategic organizational resilience during and after the COVID-19: Case study of Western Balkans

The COVID-19 pandemic has affected businesses globally. It swept the world like a wave, changing and adapting economies around the world. However, many businesses have shown remarkable resilience in the face of this crisis, adapting to changing circumstances and finding new ways to serve their customers. To build resilience, people need to understand organizational change. In this study, the authors only explained the organizational aspects of resilience in the Western Balkans. They emphasized that there is a lack of research investigating resilience in the context of venture failure due to the inability to respond to the demands of crisis conditions. Therefore, they study the association between corporate responses that we consider particularly important during this crisis, in relation to labor practices, to determine whether companies experienced less-negative responses to the crisis during the market collapse. The aim of these studies is to highlight the importance of the need to improve resilience after the COVID-19 crisis and to determine strategies for achieving it. For this purpose, extensive literature was used, which represents the theoretical framework of the research. Based on their research, the winners in the changing business environment will be unbridled firms that respond to challenges and create new opportunities.

The Globalization of Cashless Transactions Using Blockchain Technology to Preventing Money Laundering and The Changing Trends in The Cryptocurrency Market: A Learning Experience of Polish and EU Laws

Abstract Summary The COVID-19 pandemic experience has driven us to rely on technology so much on the development and expansion of technology, including cashless transactions. Criminal groups may become more interested in electronic payments and virtual currencies because of increased traffic in these areas. When comparing the second half of the 2019 to the first half of the 2020, the number of fraudulent card transactions increased by 11.4%. New developments in virtual currency trading regulations, including the digital finance package, which includes, among other things, a draught regulation from the European Parliament and an amended act to combat money laundering and terrorist financing, among others. For the reasons stated above, these regulations may lead to a virtual currency market collapse and the withdrawal of investors and the siphoning of money into Asian markets as a result. The current regulations are a manifestation of total regulation and do not encourage technological advancement.

Tradition, leadership and innovation in public health education. Commemorating the 100th anniversary of ESPM.

Health is a global concern. No one country or health system can tackle public health problems on their own. Climate change, enironmental degradation, conflict, human trafficking and migration, substance addictions, the uncontrolled corporate health damage caused by multinational corporations, economic catastrophe, global market collapse- all these require concerted public health action from local to global level. Schools of public health need to be equipped to educate, train and research problems of the health of the public and planet in the 21st century. The need for actions by schools of public health has never been more vital.

Transition Dynamics in Equilibrium Search

We study a dynamic equilibrium search model where sellers differ in their urgency to liquidate an asset. Buyers strategically make price offers without knowing a given seller’s urgency. We study liquidity and price dynamics on the transition path after an unexpected shock. Generically, the transition includes a phase where all buyers offer the same price, causing a market collapse; however, price dispersion resumes in finite time, leading to a recovery where both types make sales. We show that prices and liquidity can overshoot before converging to the steady state. When relaxed sellers randomly become desperate, dampening oscillations can occur. (JEL C73, D11, D83)

Study on the Pakistan stock market using a new stock crisis prediction method.

A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the prediction of stock crises by using deep learning algorithms such as Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Root Mean Square Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are implemented for assessing the performance of the models. The HFS-based GRU technique outperformed the HFS-based LSTM method to anticipate the stock crisis. To complete the task, the experiments used Pakistan datasets. The researchers can look at additional technical factors to forecast when a crisis would occur in the future. With a new optimizer, the GRU approach may be improved and fine-tuned even more.

Information flow in first-order potts model phase transition

Phase transitions abound in nature and society, and, from species extinction to stock market collapse, their prediction is of widespread importance. In earlier work we showed that Global Transfer Entropy, a general measure of information flow, was found to peaks away from the transition on the disordered side for the Ising model, a canonical second order transition. Here we show that (a) global transfer entropy also peaks on the disordered side of the transition of finite first order transitions, such as ecology dynamics on coral reefs, which have latent heat and no correlation length divergence, and (b) analysis of information flow across state boundaries unifies both transition orders. We obtain the first information-theoretic result for the high-order Potts model and the first demonstration of early warning of a first order transition. The unexpected earlier finding that global transfer entropy peaks on the disordered side of a transition is also found for finite first order systems, albeit not in the thermodynamic limit. By noting that the interface length of clusters in each phase is the dominant region of information flow, we unify the information theoretic behaviour of first and second order transitions.

Freedom As and Against Democracy

Freedom As and Against Democracy