Financial Crash Research Articles

Neville Kirk, A Nation in Crisis: Division, Conflict, Capitalism in the United Kingdom ; Neville Kirk, British Society and Its Three Crises: From the 1970s Globalisation to the Financial Crash of 2007–8 and the Onset of Brexit in 2016

Neville Kirk, <i>A Nation in Crisis: Division, Conflict, Capitalism in the United Kingdom</i> ; Neville Kirk, <i>British Society and Its Three Crises: From the 1970s Globalisation to the Financial Crash of 2007–8 and the Onset of Brexit in 2016</i>

The Impact of Managers' Self-interested Behavior on the Firm's Strategic Decision-making

Based on agency theory, this paper investigates the impact of managerial self-interest, especially managerial temporal myopia, on corporate strategic decision-making. This myopic behavior can lead to investment myopia, which in turn affects investment decisions. Moreover, it may result in financial distress and stock price crashes, ultimately affecting financing decisions. Firstly, it explores how managers exploit information asymmetry to advance their personal interests, consequently undermining the long-term value of the company. Next, the study examines the specific manifestations and causes of managerial temporal myopia, explaining how short-term behaviors in investment, financing, and debt management affect corporate decision-making. Finally, the paper proposes strategies to mitigate managerial self-interest, including curbing the tendency towards short-term gains, enhancing information disclosure, and restricting managerial expansion without adequate supervision. This analysis aims to provide valuable guidance for corporate governance, helping enterprises to make scientifically sound strategic decisions while considering managerial self-interest. The objective is to avoid long-term losses caused by myopic behavior.

Health impact assessment in two planning projects in England: reflections on normative effectiveness

BackgroundThis article reports on research commissioned by (what was at the time) Public Health England (PHE). The objective is to reflect on the normative effectiveness of Health Impact Assessment (HIA) applied to two planning projects; (a) the new town at Cranbrook, Devon (HIA prepared in 2007), and (b) the regeneration of the Marsh farm area in Luton (HIA prepared in 2009). In this context, the focus is on the contribution of HIA to actions that are intended to lead to good or improved health and wellbeing.MethodsNormative HIA effectiveness criteria derived from a literature review were used to guide the analysis. The two included HIA cases were previously identified as good practice examples with regards to procedure and report quality. Semi-structured interviews with public health, planning and other actors originally involved in the HIAs were conducted in 2021. This was followed up by web-searches for evidence on actual developments in 2023.ResultsInterviews indicated that normative effectiveness initially appeared to be high, but that the longer-term effects of the financial crash of 2008 reduced this. Delays in initially anticipated timelines and HIA actors moving elsewhere or retiring meant that HIAs were not followed-up and connections between developments and the HIAs were no longer made. However, web-based searches conducted in 2023 found that key HIA suggestions were eventually implemented, albeit with delay. There is also evidence for improved IMD (index of multiple deprivation) rankings in the Marsh farm regeneration case.ConclusionA mismatch is observed with regards to HIA exercises appearing to be largely ‘forgotten’ after over a decade of their publication, but recommendations still being implemented, possibly as a result of ‘institutional memory’. Making monitoring and follow-up of HIA binding rather than advisory would allow for direct linkages to be made.

A multi-jurisdictional perspective: To what extent can cryptocurrency be regulated? And if so, who should regulate cryptocurrency?

This article addresses the need for effective cryptocurrency regulation considering its volatile nature, recent investment surges, and subsequent financial crashes. It explores various approaches to cryptocurrency regulation, examining different jurisdictions’ approaches towards cryptocurrency’s classification as a security, commodity, or property. For countries to efficiently regulate the global threat that cryptocurrency has the potential to be, regulatory methods that are currently being used on a domestic scale need to be addressed and evaluated for international effectiveness. The article begins by defining cryptocurrency and reviewing the recent events that have intensified the regulatory race, such as crypto crazes and crypto crashes. It analyses different regulatory approaches, highlighting which countries view cryptocurrency as a security, commodity, or property, and examines the necessity for international harmonisation. It then examines the introduction of Central Bank Digital Currencies, evaluating it as the potential step forward and identifying the impact it could have.

MODELLING STOCK PRICES OF A BANK WITH EXTREME VALUE DISTRIBUTIONS

The study investigates the application of Extreme Value Theory in modelling stock prices, aiming to capture the tail behaviour and extreme movements that conventional distributions often fail to represent accurately. The use of Extreme Value Theory has gained considerable attention in the field of finance due to its ability to model rare events, such as financial crises or market crashes. By incorporating Extreme Value Theory, researchers aim to improve risk management, portfolio optimization, and pricing of financial derivatives. In this study, the Log-normal, Weibull, Gamma, and Normal distributions were used to model the stock price closing data, with a specific focus on extreme value distributions. Both graphical explorations and goodness-of-fit criteria were considered together to evaluate the suitability of these distributions. When assessing the data, it was observed that the Weibull distribution provided the best fit for the given stock price closing data.

Lost futures: COVID-19 as a crisis of employability for arts and humanities students in the UK

ABSTRACT That COVID-19 impacted the ability of Higher Education providers to furnish students with practical learning experiences is in no doubt. Between 2020 and 21, national and internationally imposed restrictions on in-person activities incurred a dramatic shift towards online learning systems across the globe and the de-prioritisation or adaptation of many employability-based offers for university students. Arguing that these changes disproportionately impacted employability provision for students registered on professionalisation-based Arts and Humanities courses we use the results of 27 semi-structured interviews, conducted with students registered on one of six UK-based museums, curatorial or heritage courses between 2020 and 2021, to explore how COVID-19 manifested as a crisis of employability for these students. Focusing particularly on Mark Fisher’s [2014. Ghosts of My Life: Writings on Depression, Hauntology & Lost Futures. Winchester: Zero Books] reflections on the psychological and affective impacts of previous international crises (e.g. the 2008 financial crash), we use our analysis to highlight the ways in which employability was felt by our participants during this period through the effects of stagnation, loss and a resistant hauntological presence. Such findings, we argue, are not specific to COVID-19 alone, but are conditional to a more general response to the employability crisis that we suggest has relevance to university educators beyond the UK. Indeed, it is our contention that understanding such global systemic shocks from a student perspective is essential if we are to improve our responses to such crises in the future, and we use this article to urge further reflections on the enmeshment between employability and crisis going forward.

Class mobilisation and class consciousness in the British strike wave of 2022–23

For a brief period, the British strike wave of 2022-23, which broke out during the inflationary surge and subsequent ‘cost of living crisis’, both reflected and enabled the articulation of a heightened level of class consciousness, given expression by the left of the trade union movement. The widespread collapse in living standards interacted with the longerterm weakening of consumption-based identities in the wake of the financial crash, and the manifest profiteering of big business, to create a moment where it was possible for union leaderships to describe the strike wave and the plight of working people in terms of a class struggle. The longer-term legacy of this moment is yet to become clear.

Early warning signals for stock market crashes: empirical and analytical insights utilizing nonlinear methods

This study introduces a comprehensive framework grounded in recurrence analysis, a tool of nonlinear dynamics, to detect potential early warning signals (EWS) for imminent phase transitions in financial systems, with the primary goal of anticipating severe financial crashes. We first conduct a simulation experiment to demonstrate that the indicators based on multiplex recurrence networks (MRNs), namely the average mutual information and the average edge overlap, can indicate state transitions in complex systems. Subsequently, we consider the constituent stocks of the China’s and the U.S. stock markets as empirical subjects, and establish MRNs based on multidimensional returns to monitor the nonlinear dynamics of market through the corresponding the indicators and topological structures. Empirical findings indicate that the primary indicators of MRNs offer valuable insights into significant financial events or periods of extreme instability. Notably, average mutual information demonstrates promise as an effective EWS for forecasting forthcoming financial crashes. An in-depth discussion and elucidation of the theoretical underpinnings for employing indicators of MRNs as EWS, the differences in indicator effectiveness, and the possible reasons for variations in the performance of the EWS across the two markets are provided. This paper contributes to the ongoing discourse on early warning extreme market volatility, emphasizing the applicability of recurrence analysis in predicting financial crashes.

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&amp;P 500 index between April 2020 and December 2022, during which the S&amp;P 500 index reaches its all‐time peak at the end of 2021. We find that the strong corrections of the S&amp;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 &gt; Computational Finance Algorithms and Computational Methods &gt; Computational Complexity Statistical Models &gt; Nonlinear Models

Log-periodic oscillations as real-time signatures of hierarchical dynamics in proteins.

The time-dependent relaxation of a dynamical system may exhibit a power-law behavior that is superimposed by log-periodic oscillations. D. Sornette [Phys. Rep. 297, 239 (1998)] showed that this behavior can be explained by a discrete scale invariance of the system, which is associated with discrete and equidistant timescales on a logarithmic scale. Examples include such diverse fields as financial crashes, random diffusion, and quantum topological materials. Recent time-resolved experiments and molecular dynamics simulations suggest that discrete scale invariance may also apply to hierarchical dynamics in proteins, where several fast local conformational changes are a prerequisite for a slow global transition to occur. Employing entropy-based timescale analysis and Markov state modeling to a simple one-dimensional hierarchical model and biomolecular simulation data, it is found that hierarchical systems quite generally give rise to logarithmically spaced discrete timescales. By introducing a one-dimensional reaction coordinate that collectively accounts for the hierarchically coupled degrees of freedom, the free energy landscape exhibits a characteristic staircase shape with two metastable end states, which causes the log-periodic time evolution of the system. The period of the log-oscillations reflects the effective roughness of the energy landscape and can, in simple cases, be interpreted in terms of the barriers of the staircase landscape.

The nexus between bank efficiency and leverage

AbstractIn this paper, we shed light on the impact of leverage on efficiency by introducing a new banking efficiency indicator that includes efficiency and stability conditions. This indicator relies on the leverage as a proxy of a bank's risk and stability. Banks' leverage played an important role in the last financial crash as well as in the Basel III regulatory rules. The results of the econometric investigation using a large sample of American commercial banks show that profit efficiency indicators including leverage are better predictors of future profits than current indicators, including other measures of bank risk. This is particularly evident for the period during the 2007–2009 financial crisis. Our findings have important policy implications, particularly in light of the recently implemented optimal leverage ratio.

After Neoliberalism: Economic Theory and Policy in the Polycrisis1

AbstractMainstream economic theory and policy have struggled to address (and in some cases have made worse) the multiple economic crises since 2008—global financial crash, austerity, stalled productivity, wage stagnation, rising inequality, inflation, climate and environmental breakdown. At the root of this failure is the ‘ontological individualism’ which underpins neoclassical economic theory: the belief that individual households and firms are sovereign actors. It proposes in its place a premise of ‘ontological institutionism’—the view that economic behaviour is primarily influenced by the institutional structures and rules within which it occurs. Commonplace in other social sciences, this view radically changes economic analysis and policy prescription. Based on an explicit ethical definition of policy objectives, the article offers an ‘institutionally pluralist’ view proposing different kinds of institutions for five different spheres of economic life. Arguing that economic policy should be seen as a process of institutional design (not simply making markets more efficient), it offers some illustrative policy proposals in key fields, from climate change to business investment.

Leveraging Behavioral Economics to Decision Architecture

Modern economic theory has been dominated by neoclassical economics since the 1970s. Like any theory, neoclassical economics also has a series of assumptions underpinning it. These assumptions have been criticized and challenged on various grounds in the past several decades. The most serious assumption is the assumption of rational economic agents who aim to maximize their utility by analyzing the costs and benefits of every decision. The presence of “homo economicus” is very rare in our society. In fact, it is practically and psychologically impossible to be rational even most of the times. Empirical and experimental research heavily corroborates this fact. Despite such massive evidence, nearly all economic models and public policies use this distorting assumption in analysis and research. The result has been catastrophic, as can be seen in the Global Financial Crisis of 2008, the Dot-Com bust and numerous financial market crashes before that. Furthermore, the theory of rationality also reduces the effectiveness of government policies in achieving their objectives. Behavioral economics has emerged to address these fallacies of modern neoclassical economics and complement it to improve economic theory, methodologies and forecasting. Behavioral economics has mainly two branchesmacro theory which aims to design macro-economic models incorporating behavioral concepts and micro theory which explains the various biases that individuals, groups and institutions exhibit in economic and even social decision-making. This research is focused on micro-behavioral economic theory. While a colossal magnitude of research has been done in identifying, testing and explaining the plethora of behavioral biases, there is a dearth of research on systematically reducing these biases. This research aims to fill this gap by conducting research on private and public sector employees of 30 to 40 years age and testing for a significant difference in level of biases revealed between the two groups of employees. The research found that there is a significant level of difference in the rational decision-making behavior of the two groups of employees. It also discusses the applications of the findings and the further scope for research.

Institutional Failures as Structural Determinants of Suicide: The Opioid Epidemic and the Great Recession in the United States.

We investigate recent trends in U.S. suicide mortality using a "structural determinants of health" framework. We access restricted-use multiple cause of death files to track suicide rates among U.S. Black, White, American Indian/Alaska Native, and Latino/a men and women between 1990 and 2017. We examine suicide deaths separately by poisonings and nonpoisonings to illustrate that (1) women's suicide rates from poisonings track strongly with increases in prescription drug availability and (2) nonpoisoning suicide rates among all adult Americans track strongly with worsening economic conditions coinciding with the financial crash and Great Recession. These findings suggest that institutional failures elevated U.S. suicide risk between 1990 and 2017 by increasing access to more lethal means of self-harm and by increasing both exposure and vulnerability to economic downturns. Together, these results support calls to scale up to focus on the structural determinants of U.S. suicide.

Post-catastrophic Irelands in contemporary fiction

ABSTRACT This article examines the relationship between neoliberal late capitalism and the climate crisis in Irish writing. It focuses on the imagined post-climate change Ireland of Danny Denton’s novel The Earlie King and the Kid in Yellow (2018), which shows a feudal society clinging to a Dublin sunk below rising waters. This analysis posits that The Earlie King employs “weird” methodologies to imagine an Ireland so wracked by environmental destruction that the transactional structures of capitalism which catalysed this destruction are rendered inoperable by it. It draws upon Mark Fisher’s work on “eerie” and “weird” modes of writing, and the partially overlapping theorisation of the “New Weird” fiction movement by participating authors Steph Swainston and China Miéville. Other fictional Irelands ravaged by the aftermaths of crisis are proliferating, in texts such as Sara Davis-Goff’s Last Ones Left Alive (2018), Oisín Fagan’s Nobber (2019) and David Mitchell’s The Bone Clocks (2014). Collectively, these texts display a direct inheritance of the 2008–9 financial crash. Luke Gibbons writes that Irish modernists “did not have to await the twentieth century to undergo the shock of modernity.” This is echoed today, in post-crash Irish writers’ lived experience of the depth of suffering which follows capitalist collapse.

После неолиберализма и его провала: ноономика как возможность

The 2008 global financial crash and its ongoing spiraling consequences mark the failure of neoliberalism as a strategy of exit from the crisis following the collapse of the post-World War II international Bretton Woods Keynesian framework. Technological progress per se cannot solve the strategic impasse as the problems in productivity growth demonstrate. Modernization itself has to overcome the obstacles posed by a fundamental crisis of late modernity in the epoch of advanced capitalist decline noonomy provides an open field of research of the relations between scientific-technological progress and political economy in the 21st century. It focus on the progress of integration of science into a cognition based social production without exploitation. A comparative study with the ‘General Intellect” concept advanced by Karl Marx in Grundrisse and the related debates on this topic during the last decades could be fruitful. In this context, noonomy provides new insights and possibilities for a re-elaboration of a socialist strategy of exit from the global crisis.

What is a financial crash?

What is a financial crash?

Third place social infrastructure, after and in crisis: insights from a local case study

Following the 2008 global financial crisis, Digital Fabrication Laboratories (Fab Labs) have become a common feature of the urban landscape in cities throughout Europe. An emerging body of literature suggests that Fab Labs go beyond providing access to digital fabrication tools, and function as ‘third places’ as they enhance social connectedness. Drawing on a case study of a Fab Lab in the English city of Coventry, this article utilises the concept of ‘austerity urbanism’ to understand the changing nature of third places in England since the 2008 global financial crash. In doing so, we argue that a confluence of austerity urbanism and digital advancements has influenced both the emergence of new third places (such as Fab Labs) while simultaneously undermining long-established third places (such as libraries). As a result, vital aspects of social infrastructure are being shaped and reshaped in the contemporary era. The article reflects on what these changes mean for individual and community well-being.

Predicting stock market crashes on the African stock markets: evidence from log-periodic power law model

PurposeThis study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.Design/methodology/approachOver the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.FindingsBy minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.Practical implicationsThe adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.Originality/valueThis study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

Deep learning-based solution for smart contract vulnerabilities detection

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.