Black Swan Research Articles

Break a peg! A study of stablecoin co-instability

Following the increasing adoption of stablecoins, their robustness has become an ongoing concern for investors and regulators alike. This article studies the co-instability of a large panel of stablecoins by identifying their structural breaks and co-occurrences. Moreover, we use Dynamic Time Warping (DTW) and Dynamic Conditional Correlation DCC-GARCH models to assess spillover effects in the wake of four major black swan events in the crypto world. Similar to prior literature, we conclude that algorithmic stablecoins were the major shock receivers of the IRON TITAN and TERRA LUNA crashes, highlighting the importance of allowing for sufficient margins of safety in their design. We also observe that the FTX bankruptcy and the Silicon Valley Bank collapse had repercussions of a different nature and affected more categories of stablecoins, from algorithmic to fiat money-backed. We also highlight how, irrespective of the idiosyncratic or systemic nature of the shocks, well-known custodial stablecoins, as well as the most successful crypto-collateralized tokens, showed resilience during or in the immediate aftermath of these events.

Correction to “Authenticity and high performance: Nonmarket social, public and media strategies of foreign SMEs during a black swan event”

Correction to “Authenticity and high performance: Nonmarket social, public and media strategies of foreign SMEs during a black swan event”

Assessing the current health effects of climate change in the Netherlands

Abstract The National Institute for Public Health and the Environment (RIVM) has assessed the current effects of climate change on health as part of a national sector-wide process towards updating the Dutch National Adaptation Strategy. Besides health, this process includes assessing the risks of climate change for water management, agriculture, the built environment, nature and other sectors. It will provide a basis for policy, for prioritising the most urgent risks and for long-term risk monitoring. Six climate-related health topics were assessed for this update: heat, air quality, mental health, UV radiation, pollen allergies and infectious diseases. To allow comparison of the impact and likelihood of the climate-related risks across sectors, a format for factsheets on climate risks had been provided. Following this format, for each climate-related health topic we considered the climate threat, secondary effects, exposure, vulnerability, adaptive capacity, impact and cascading effects. The magnitude of the health impacts was categorised per topic as low (< 10,000 people affected; 0 - 10 seriously injured/dead), medium (10,000 - 100,000 people affected; 10 - 100 seriously injured/dead) or high (>100,000 people affected; >100 seriously injured/dead). The likelihood of these impacts was then described as well as possible black swan events or tipping points. Finally, the policy context was outlined, including possible maladaptation, lock-ins, and equity aspects, followed by quality assurance considerations. For all topics, negative health impacts are already occurring. However, the magnitude of the impacts cannot yet be accurately quantified. In this presentation, we will discuss the results of this assessment and explore the pros and cons of being part of a sector-wide process.

Review of Causes, Prediction, and Prevention of Systemic Risk

In financial market, people always ignore or underestimate the potential risk of systemic risk until black swan events emerged as an irretrievable crisis. In order to analyze and prevent from crisis that generated by systemic risk, this paper collates and presents detailed review of the causes, prediction, and mitigation of systemic risk by gathering and sifting current theoretical and empirical research. It explores the complexity and interdependence of financial systems and emphasizes how systemic risk is created via endogenous risk generation via the investment decisions of financial institutions and regulatory and market failures. Moreover, it underlines the strong demand of through legislative changes, the importance of enhanced market discipline, and well-crafted government interventions. Deliberative risk governance is presented as a practical and necessary strategy when it comes to addressing stakeholders in the risk management process to achieve legitimacy and consensus. The research ends by advocating for a comprehensive strategy for risk management to strengthen the resilience of the global financial system and reduce the probability of future potential crisis.

Impact of Black Swan Events on Ethereum blockchain ERC20 token transaction networks

The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens are programmable and interoperable tokens, enabling various applications and token economies. Transaction graphs, representing the flow of the value between wallets within the Ethereum network, have played a crucial role in understanding the system’s dynamics, such as token transfers and the behavior of traders. Here, we explore the evolution of daily transaction graphs of ERC20 token transactions, which sheds light on the trader’s behavior during the Black Swan Events – 2018 crypto crash and the COVID-19 pandemic. By using the tools from network science and differential geometry, we analyze 0.98 billion of ERC20 token transaction data from November 2015 to January 2023. Our analysis reveals an increase in diverse interaction among the traders and a greater adoption of ERC20 tokens in a maturing Ethereum ERC20 financial ecosystem after the Crypto Crash 2018 and the COVID-19 pandemic. Before the crash and the COVID-19 pandemic, most traders interacted with other traders in an isolated or restricted manner, with each trader focusing solely on either buying or selling activities. However, after the crash and during the pandemic, most traders diversely interacted among themselves by participating in both buying and selling activities. In addition, we observe no significant negative impact of the COVID-19 pandemic on user behavior in the financial ecosystem.

Employee perspectives on effective crisis leadership skills in small and medium-sized enterprises

ABSTRACT Building on the 4Cs and five phases of crisis management and Hersey and Blanchard’s situational leadership model, this research aims to identify employees’ perspectives and expectations on crisis leadership characteristics and skills to lead organisations and decrease the disruptive effects of a black swan event. This exploratory research interviewed 43 small and medium-sized enterprise employees from wholesale and retail trade sectors in Taiwan and the U.S. and studied their expectations of effective crisis leadership. Using an inductive thematic analysis, this research identified eight themes for leaders to navigate and lead through crises and make positive changes. Personal qualities and abilities to excel are the top two themes mentioned by most of the interviewees. A follower-centred situational leadership that demonstrates positive personal qualities, focuses on employee safety, and assures job security and transparency can effectively lead the employees and organisation to overcome a crisis. The findings could be used for organisation leaders’ and HRD professionals’ practice as change agents and for future research in HRD theory and leadership.

Demystifying the dynamic relationship between news sentiment index and ESG stocks: Evidence from time-frequency wavelet analysis

Using the Time-Frequency Wavelet Analysis, this study investigates the dynamic connectedness between the News Sentiment Index (NSI) and ESG leader indices of developed countries. The empirical findings clearly show nontrivial dynamic comovements between NSI and the ESG indices and the existence of the ESG market volatility in different frequency scales. We also report some discrepancies in cross-linkage patterns among different countries. In particular, such associations for some countries (e.g., Australia and the United Kingdom) are negative, with the leading role of the ESG index against market risk due to high fluctuations in sentiments. These findings suggest that ESG stocks possess potential hedging and diversifying features and safe-haven attributes against market risk driven by negative sentiment during the outbreak of Black Swan events. Our empirical results have insightful implications for investors, portfolio managers, and policymakers.

Authenticity and high performance: Nonmarket social, public and media strategies of foreign SMEs during a black swan event

AbstractNonmarket (NM) strategy is an emerging concept, similar to the notion of NM social, public and media strategy (SPMS). Previous literature has predominantly focused on larger corporations, often overlooking smaller enterprises. Additionally, much of the existing research has been centred on developed markets, leaving a gap in qualitative research. The current analysis seeks to elucidate how foreign small and medium‐sized enterprises (FSMEs) in transitional markets, such as Shanghai, adopt specific NM SPMS to mitigate the liability of foreignness (LOF) during a pandemic. The study utilizes a CGT approach, employing semi‐structured interviews with 27 participants, including foreign entrepreneurs and digital marketing specialists. Additionally, qualitative data was analysed comprehensively using Atlas.ti 9 software. The qualitative methodology implementation resulted in the creation of the authenticity and high performance framework from where specific strategies and general implications are presented. The study highlights two principal themes: (1) The need for increased flexibility and speed in social media message communication and (2) Customized FSME strategies through digital marketing—a never‐going‐back market digitalization. The second theme is subdivided into (a) Tailored public relations for FSMEs. (b) An increased need for internal cohesion—fostering a stable mindset and positive rapport among staff. (c) An increased need for trialling new target customers. (d) Creating a relatable story—showing a particular vulnerability can be viral and (e) Maintaining a strong reputation for operational performance.

Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?

The coronavirus disease (COVID-19) has led to a persistent increase in the volatility of the credit market and triggered a series of financial distress and bankruptcy. To investigate whether there are differences in loan default determinants before and during COVID-19 and to identify the most effective predictors of loan default during COVID-19, this study employs machine learning methods to establish a comprehensive loan default prediction model for Peer-to-peer (P2P) lending based on four perspectives: loan characteristics, credit transaction history, personal information, and macroeconomic environment. The results show that the EXtreme Gradient Boosting (XGBoost) outperforms the other models and that credit transaction history plays a vital role in forecasting loan default over the two periods. We also find discrepancies between the effects of consumer price index, purchasing manager’ index, and the number of bidders on loan default before and during the pandemic. Our study contributes to related research fields on loan default prediction by identifying loan default determinants that are more applicable to unstable periods and investigating the impact of COVID-19 on default predictions. Meanwhile, our findings can provide P2P lending investors, platforms, and policymakers with practical implications to reduce uncertainty and losses that result from similar black swan events.

Exploring the influence of Silicon Valley Bank default and U.S. financial stress on sectoral interactions and effective hedging strategies

ABSTRACT The SVB implosion could trigger a domino effect, shaking confidence in U.S. banks. This research estimates the response of good and bad cumulative abnormal returns (G-CARs, B-CARs) on the event day, pre-event and post-event periods, using financial stress, oil, geopolitical and equity uncertainty as controls. We also examine the moderating impact of the SVB implosion and U.S. financial stress indices (US-FSI) on the good and bad dynamic conditional correlations between 132 U.S. sectoral pairings. Additionally, we assess the best portfolio distribution to determine if short positions in specific U.S. sectors offer better protection against SVB’s long-term volatility using the DCC-GARCH-t copulas. Overall findings suggest that B-CARs are more adversely affected than G-CARs. Therefore, investors need to consider bearish and bullish market conditions before devising hedging strategies. The Financials, Health Care, Industrial, Materials, Oil and Gas, Real Estate and Transportation sectors of U.S. experienced significant losses due to higher B-CARs around the SVB default event. Furthermore, the SVB default’s moderating impact on the U.S. sectoral stock returns is more pronounced during bearish market conditions compared to bullish ones following a Black Swan event. This suggests a differential moderating impact of SVB on good and bad conditional connectedness and a reduction in diversification benefits.

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction.

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

Surviving black swan: competitive intelligence and frugal innovation as panaceas to SME value creation during crisis

Surviving black swan: competitive intelligence and frugal innovation as panaceas to SME value creation during crisis

Understanding uncertainty shocks and the role of black swans

Uncertainty shocks can trigger business cycles, bank runs and asset price fluctuations. But how do those shocks translate into changes in beliefs and actions? What makes people suddenly become uncertain? This paper provides an explanation for subjective uncertainty shocks, estimated on real-time GDP data, that fits puzzling features of professional forecasts. The key ingredients are that agents do not know the true model governing the distribution of macroeconomic outcomes, but they believe that distribution to be potentially skewed. When agents estimate higher probabilities of extreme tail events, their uncertainty rises. Our estimates reveal that learning about probabilities of tail events – black swans – explains much of the fluctuations in subjective uncertainty.

Emerging from Adversity: Higher Education is No Stranger to ‘Black Swan’ Events Resulting in Educational Enhancements

Emerging from Adversity: Higher Education is No Stranger to ‘Black Swan’ Events Resulting in Educational Enhancements

Gross anatomical study on bones of pectoral girdle in black swan (Cygnus atratus)

Gross anatomical study on bones of pectoral girdle in black swan (Cygnus atratus)

Sex Identification of Black Swan (Cygnus atratus): Possible at the First Day of Hatching

Sex Identification of Black Swan (Cygnus atratus): Possible at the First Day of Hatching

The Need for Localized, Socio-economic Policy Measures for Controlling a Pandemic: An Empirical Study of COVID-19 in India

We examine the localized nature of COVID-19 pandemic spread in India. Through Gamma curve-fitting we observe that the infection patterns exhibit substantial variation across locations. Areas with larger male population and higher economic activity witness more cases. Economically-deprived districts experience higher mortality after controlling for the infections. Mobility in spatially contiguous locations is a significant determinant of new infections. Our study emphasizes the role of socioeconomic factors in explaining the variation across districts. The findings support the need for locally-specific policy, better medical infrastructure in socio-economically vulnerable localities and social-distancing measures in controlling the spread.

A glance at the COVID-19 epidemic control system in Taiwan: implications for organizational crisis management and leadership in black swan events

Abstract The COVID-19 pandemic caused substantial impacts and uncertainty in the world. For human resource leaders compelled into new roles in light of the pandemic, this paper used an exemplary case – Taiwan – to (1) discuss lessons and takeaways from the Taiwanese government’s and organizations’ response to the pandemic and (2) identify the structure of a pandemic control model and effective leadership behaviours at the national level that may be adopted in business settings to decrease the disruptive effects of a crisis. While efficient subsystems in organizational governance, policy, leadership, and culture are essential to combat a crisis, the evidence from Taiwan’s case in these contexts highlights the importance of various infrastructures and communication pathways in crisis management. The study aims to advance the understanding of effective responses and leadership skills in times of crisis and contribute to human resource literature and policy development in business settings. Human resource professionals and leaders play an important role as change agents to reduce the disruptive effects of a black swan event and can benefit from this study to be prepared for future crises. Implications for international human resource interventions in crisis management and leadership and recommendations for future research are provided.

On unpredictable events in risk analysis

The concept of unpredictable events is commonly referred to in the context of risk analysis. Different meanings are attributed to this concept in the literature, many of which link this type of event to the concept of black swans, tail events, and so-called “unknown unknowns”. In the present paper, we review and discuss potential conditions for labeling an event (or outcome/consequence) as unpredictable in a risk analysis setting. The proposed conditions relate to whether the available knowledge supports i) the specification of an event or outcome in the risk assessment, ii) an adequate uncertainty characterization for the event or outcome, and iii) the identification of precursor signals and warnings. These conditions are discussed in light of a contemporary perspective on risk, where the aspects of time and knowledge are given particular focus. The incorporation of these aspects and their dynamics allows for clear distinctions to be made between short-term and long-term unpredictability, as well as between ex ante and ex post perspectives in unpredictability assessments. A main conclusion is that the level of unpredictability associated with an event (or its consequences/outcomes) can vary depending on the temporal perspective and on the degree to which the conditions are met.

The impact of SRDI policy for SMEs on corporate innovation under the COVID-19: evidence from China

ABSTRACT This study focuses on small and medium-sized enterprises (SMEs) in China, and examines whether and how the SRDI (Specialized, Refinement, Differential and Innovation) policy support impacts SMEs’ innovation under the COVID-19. We find that the R&D investment and innovation outputs in SMEs supported by the SRDI policy gets significantly increased, which results from the increase in government subsidies and the implementation of digital transformation. Furthermore, the positive impact of the policy on SMEs’ innovation is more significant after the outbreak of the COVID-19. It shows that resource support from government and digital transformation can help reduce the external adverse impact of the COVID-19 on SMEs’ innovation and enhance the risk-taking and sustainable development capabilities of SMEs when the Black Swan incident occurs.