Financial Market Crashes Research Articles

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.

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.

Judge me on my losers: Do robo‐advisors outperform human investors during the COVID‐19 financial market crash?

As financial institutions increasingly offer robo‐advisors (RAs) as a service, the empirical evidence on their performance, particularly during market downturns, remains limited. We study the impact of using RAs on investment performance during the 2020 financial crisis caused by the COVID‐19 global pandemic. We obtain daily portfolio and transaction data of investors on an online investment platform. Besides making investment decisions by themselves, investors can also leverage an RA system offered by the platform. We match RA users with other investors who did not use the RA with similar investor and portfolio characteristics before the market crash, then compare their portfolio returns after the crash. We find that RA users experienced significantly fewer losses during the market downturn, and this performance advantage came from the RA‐managed assets (rather than assets managed by RA users themselves). We further show that the RA system adjusted its portfolios to hold less risky funds, whereas human investors stayed with their status quo and did not reduce the risk of their portfolios, which partly accounted for the performance discrepancy. Importantly, the RA’s superior performance was not simply a continuation of its performance in a normal market; in fact, we find RA users to have similar portfolio returns as non‐RA investors during normal markets, and the benefits of RA's risk‐reduction trading strategy only manifested during the financial crisis. Our work offers an empirical assessment of RAs’ performance under different market conditions and advances the understanding of algorithmic decision making in financial markets.

Impact of COVID-19 on the Behaviour of Islamic and Conventional Investors: Evidence from the Indonesia Stock Market Crash 2020

Purpose — The aim of this paper is to investigate the influence of the COVID-19 outbreak on Indonesia’s conventional and Islamic stock markets through the lens of behavioural finance in the digital age. Design/Methodology/Approach — The analysis in this paper is focused on the short-run and long-run impact of variables associated with COVID-19—such as the number of COVID-19 cases and mortality, the Google Search Volume (GSV) for the search query associated with COVID-19, and the panic index related to COVID-19—on the returns of the LQ45 Conventional Index and Jakarta Islamic Index (JII), using the Autoregressive Distributed Lag (ARDL) model. Findings — In the short run, increasing mortality and GSV significantly decreases the returns on LQ45 and JII. By contrast, the returns of LQ45 and JII are unaffected by an increase in the number of cases or the panic index. In the long run, only the panic index affects the LQ45 returns. Originality/Value — This article makes three contributions to the literature. First, it compares the COVID-19 outbreak’s impact on conventional and Islamic stock markets. Second, it discusses the short-run dynamics and long-run impact of the COVID-19 outbreak on stock returns. Third, it provides an explanation of the empirical relationship between the COVID-19 outbreak and the stock market using a behavioural finance viewpoint. Practical Implications — Digital behavioural science-based policies are needed to prevent or lessen financial market crashes during future crises. ErratumArticle classification: Erratum from: ISRA International Journal of Islamic Finance Vol. 15 No. 3 (2023)Publisher: ISRA Research Management Centre, INCEIF University Shiddiqi, F.A. & Susamto, A.A. (2023), ‘Impact of COVID-19 on the Behaviour of Islamic and Conventional Investors: Evidence from the Indonesia Stock Market Crash 2020’, ISRA International Journal of Islamic Finance, Vol. 15 No. 3, pp. 142–159. https://doi.org/10.55188/ijif.v15i3.615It has been brought to our attention that an error in the affiliation of one of the authors has been inadvertently occurred during the publication process. The affiliation of author Faris Azzam Shiddiqi on page 142 should be changed from Graduate School Yogyakarta, Universitas Gadjah Mada, Yogyakarta, Indonesia to instead read: The Graduate School, Universitas Gadjah Mada, Yogyakarta, Indonesia. ISRA Research Management Centre, INCEIF University sincerely apologises to the authors for any inconvenience caused.This change has been effected in the online version of the paper. Citation:(2023), ‘Erratum’, ISRA International Journal of Islamic Finance, Vol. 15 No. 3, https://doi.org/10.55188/ijif.v15i3.615

Friends During Hard Times: Evidence from the Great Depression

Abstract Using a novel data set of over 3,500 public and private firms, we construct the network of executive and director connections prior to the 1929 financial market crash. We find that more connected firms have 17% higher 10-year survival rates. Consistent with a working capital channel, the results are strongest for small, private, cash-poor firms, and firms located in counties with high bank suspension rates. Moreover, connections to cash-rich firms that increase accounts receivable matter the most. Our results suggest that network connections can play a stabilizing role during a financial crisis by easing the flow of capital to constrained firms.

Financial System, Audit and Corporate Governance During and After the Financial Crisis

A financial system is a set of institutions, such as banks, insurance companies, and stock exchanges, that permit the exchange of funds. Financial systems exist on firm, regional, and global levels. Borrowers, lenders, and investors exchange current funds to finance projects, either for consumption or productive investments, and to pursue a return on their financial assets. The financial system also includes sets of rules and practices that borrowers and lenders use to decide which projects get financed, who finances projects, and terms of financial deals. The concept of "internal control" conveys a difficult and complex process. Different countries have different interpretations of the internal control system, and in accordance with their tradition use different methods, techniques and philosophies for its implementation.The financial system of our economy, although it must be acknowledged that it is not very modern and developed, is still quite complex. In support of a complete system with all components, there is a need for harmonized investment in many directions at once. Financial crises and stock market crashes have clearly demonstrated the impact of investors’ sentiment on asset pricing and stock markets’ efficiency. Herd behavior, which is behavioral similarity based on individuals’ interaction that leads to convergence of action and correlated trading (Hirshleifer and Teoh, 2003), is one of the most important behavioral biases that is more likely to occur during periods of market stress when individual investors prefer to follow the market consensus, being reluctant to follow their own knowledge or beliefs (Christie and Huang, 1995). Management on all levels must have full confidence in the integrity and independence of the audit process.Financial system, audit and corporate governance (CG) has come to the forefront of academic research due to the vital role it plays in the overall health of economic systems. The wave of U.S. corporate fraud in the 1990s was attributed to deficiencies in corporate governance. The recent 2008-2009 global financial crisis, triggered by the unprecedented failure of Lehman Brothers and the subprime mortgage problems, renewed interest on the role of corporate governance in the financial sector. The main purpose of this scientific paper is to study financial situation, audit and governance and strategies for optimizing investment, concretelly in the albanian financial environment. Keywords: Financial crisis, corporate governance, emerging economies etc. DOI: 10.7176/RJFA/14-10-01 Publication date: May 31 st 2023

Forecasting extreme events in collective dynamics: An analytic signal approach to detecting discrete scale invariance

A challenging problem in physics concerns the possibility of forecasting rare but extreme phenomena such as large earthquakes, financial market crashes and material rupture. A promising line of research involves the early detection of precursory log-periodic oscillations to help forecast extreme events in collective phenomena where discrete scale invariance plays an important role. Here we investigate two distinct approaches towards the general problem of how to detect log-periodic oscillations in arbitrary time series without prior knowledge of the location of the moveable singularity. We first show that the problem has a definite solution in Fourier space; however, the technique involved requires an unrealistically large signal-to-noise ratio. Then we show that the quadrature signal obtained via analytic continuation onto the imaginary axis, using the Hilbert transform, necessarily retains the log-periodicities found in the original signal. This finding allows the development of a new method of detecting log-periodic oscillations that relies on calculation of the instantaneous phase of the analytic signal. We then illustrate the method by applying it to the stock market crash of 1987 and explore the important role played by positive feedback mechanisms in relation to economic bubble formation, financial crashes, log-periodic dynamics and analytic behavior. Finally, we discuss the relevance of these findings for parametric rather than nonparametric estimation of critical times.

Wealth redistribution in bubbles and crashes

What are the social-economic consequences of financial market bubbles and crashes? Using novel comprehensive administrative data from China, we document a substantial increase in inequality of wealth held in equity by Chinese households in the 2014–15 bubble-crash episode: the largest 0.5% households in the equity market gain, while the bottom 85% lose, 250B RMB through active trading in this period, or 30% of either group's initial equity wealth. In comparison, the return differential between the top and bottom household groups in 2012–14, a period of a relatively calm market, is on the order of 1 to 3%. We examine several possible explanations for these findings and discuss their broader implications.

The COVID‐19 risk in the Chinese option market

The COVID‐19 pandemic has increased fear of a financial market crash in China. We use an implied volatility slope measure, which proxies the cost of option protection against and therefore trader's fear of crash risk, using the Shanghai Shenzhen CSI 300 Index options. We show that this measure is positively related to new cases and deaths of the pandemic during the COVID‐19 outbreak in China. Option traders are willing to pay more for hedging downside tail risk as the pandemic worsens, and are no longer as concerned by news of cases and deaths after the lift of the lockdown.

ASIAN STOCK MARKET FINANCIAL CATASTROPHE: PROPORTIONAL ANALYSIS OF SOUTH-EAST ASIAN STOCK MARKETS

This study investigates financial crisis of 2007 in Asian stock markets. This study is aimed at studying the impact of this crisis on East and South Asian Stock Markets. The outcomes of this research would provide a better understanding of the stock market crashes or financial crisis and the possible implications of these scenarios. The study also adds valuable contribution to the existing literature on financial market crashes and may encourage further researches. The years on which the research is focused on are from 2003 to 2012 while the indexes are of 52 different countries on Panel analysis basis. It can be said in the end that the financial crisis does influence the economy of any country. Financial crisis in the global scenario impact the stock markets and other financial institutions all over the world. East Asian market was more resilient then the South Asian market in the financial crisis in the late 2000s. The governments of South Asian countries should implement the strategies and policies focused on the policies of East Asian countries to overcome the financial crisis in future..

Big data, news diversity and financial market crash

A vast quantity of high-dimensional, unstructured textual news data is produced every day, more than two decades after the launch of the global Internet. These big data have a significant influence on the way that decisions are made in business and finance, due to the cost, scalability, and transparency benefits that they bring. However, limited studies have fully exploited big data to analyze changes in news diversity or to predict financial market movements, specifically stock market crashes. Based on modern methods of textual analysis, this paper investigates the relationship between news diversity and financial market crashes by applying the change-point detection approach. The empirical analysis shows that (1) big data is a relatively new and useful tool for assessing financial market movements, (2) there is a relationship between news diversity and financial market movements. News diversity tends to decline when the market falls and volatility soars, and increases when the market is on an upward trend and in recovery, and (3) the multiple structural breaks detected improve the ability to forecast stock price movements. Therefore, changes to news diversity, embedded in big data, can be a useful indicator of financial market crashes and recoveries.

The Value of Registered Investment Advisors during the COVID-19 Financial Market Crash - Evidence from 13F and Twitter

The ongoing public health crisis associated with the COVID-19 pandemic has had a significant impact on business activities, government at large, financial markets, and individual households. This study is the first to our knowledge that seeks to examine the 2020 financial market crisis using manually downloaded data, which combined 13F filing with ADV forms with tweets from Registered Investment Advisors. We find that even during extremely negative return days, financial advisors communicate frequently and positively with their clients. Clients working with financial advisors tend to sell less and buy more during a market downturn as compared with self-discretionary clients.

Forecasting price of financial market crash via a new nonlinear potential GARCH model

Financial market crash is one of the most extreme manifestations of financial market instability. It is of great significance to study its underlying structure and its forecasting methods. From the perspective of Econophysics, in addition to the influence of factors such as fluctuation on the financial price crash, we consider that the financial price crash originates from the change of the underlying structure described by the nonlinear potential function in the financial system. So we first propose a new nonlinear potential GARCH model by introducing nonlinear potential function and combining with GARCH model. In order to only analyze the effects of potential functions and eliminate the effects of fluctuations on prices, we combine the realized volatility and propose the likelihood function estimations of the proposed model and the benchmark GARCH and ARCH models. Then, based on the 5-minute high-frequency data of CSI300 and WTI crude oil, the out of sample forecasting with rolling time window and the SPA test method with Bootstrap characteristics are calculated. Finally, Akaike information criterion and Bayesian information criterion are employed to test the Goodness of fit of three models for in-sample and out-of-sample. The dynamic forecasting performance of the proposed new nonlinear potential GARCH model is compared with that of the benchmark GARCH and ARCH models. The main empirical results show that the proposed new nonlinear potential GARCH model is better than the benchmark GARCH and ARCH models for forecasting the returns and prices in the financial price crash for the given data. In addition, the potential well of the underlying nonlinear potential function changes obviously in the financial price crash.

ESG ratings and financial performance of exchange-traded funds during the COVID-19 pandemic

ABSTRACT With the advent of the COVID-19 pandemic, the world has experienced economic and social fragility, which calls for alternative approaches to navigate towards sustainable outcomes. While recent studies show that responsible investments (RI) are resilient during the economic downturn caused by crises such as COVID-19, there has been little exploration into exchange-traded funds (ETFs). Using ANOVA and multivariate regression models, we analyze the differences and relationship between the financial returns of ETFs and their Eco-fund ratings during the COVID-19 pandemic-related financial market crash. Our results indicate that higher levels of the sustainability performance of ETFs do not safeguard investments from financial losses during a severe market downturn. These results contribute to the research by exposing weaknesses of current sustainability scores and rating methods to provide an initial analysis of RI during the COVID-19 pandemic

The Mechanism behind the Bursting of Financial Bubbles and Market Crashes

This article proposes to deliver an algorithm to envisage the distribution of the critical points of bubbles, may it be a financial bubble or an asset bubble. The study comprehensively examines the use of Log periodic Power law in various articles from renowned authors from the first paper that was published by Didier Sornette in 1996 to the present day. The paper scrutinizes the prerogatives and robustness of the LPPL for large market falls and the anti-bubbles that build in a market. The LPPL fit has been attempted to fit into various crashes in different stock markets that were predicted previously to establish the smooth working of the model.

Unraveling Hidden Queer Pattern Through Fractal Analysis

The behavioral finance studies are ought to be the trading strategies as the investor’s and other financial market participants’ decision-making and judgments are driven by the information available and the information leaked in the market. As a result, the information, which is floated in the market, results in an aggressive buy and sell behavior and can impair the process of fair price discovery and cause a downturn in the event of downside herding leads to a financial market crash, which causes great damage to the economy and affects its financial health.

GOLD: HEDGE, SAFE HAVEN OR DIVERSIFIER FOR INDONESIAN CAPITAL MARKET

This study scrunitize whether gold can act as hedge, diversifier and safe haven in Indonesian Capital Market. Data employed in this study are monthly Jakarta Composite Index and monthly international gold spot price during January 1999 to September 2013. Using Generalized Autocorrelation Conditional Heteroscedasticity (GARCH), the results show that gold can not act as hedge and diversifier in Indonesian Capital Market because both instruments have comovement. Furthermore, gold can not act as robust safe haven in Indonesian Capital Market. These findings suggest that it is better to investors whose already hold Indonesian stocks as their portfolio to exclude gold as their part of portfolio and divert their portfolio to other instrument with risk free characteristic such as Indonesia sovereign debt and Indonesia sovereign Sukuk during financial market crash.

Credit Pit Detection in Subordinate Securities: A French Perspective

The purpose of this research is to prepare a predictive model for identifying credit crisis using an artificial neural network. The paper also aims to find out the driver and driven relationship between various financial instruments like CDS, FRA, IRS, and the Volatility index (VCAC) and government securities for France. The model, thus, is directed towards finding a threshold for credit pit events and linking various events corresponding to that dates where the threshold is breached to validate the accuracy and usefulness of the model. From the research, it is found that for France, the CDS-FRA-VCAC model derives the threshold for VCAC to indicate the probability of credit crisis or financial market crash. It is also found that sovereign bonds have a huge impact on France economy including various derivatives. This is probably why the Eurozone debt crisis impacted France much more than the 2008 financial crash.

Wealth Redistribution in Bubbles and Crashes

What are the social-economic consequences of financial market bubbles and crashes? Using novel comprehensive administrative data from China, we document a substantial increase in inequality of wealth held in risky assets by Chinese households in the 2014-15 bubble-crash episode: the largest 0.5% households in the equity market gain, while the bottom 85% lose, 250B RMB through active trading in this period, or 30% of either group’s initial equity wealth. In comparison, the return differential between the top and bottom household groups in 2012-14, a period of a relatively calm market, is an order of magnitude smaller. We examine a number of possible explanations for these findings and discuss their broader implications.

A Machine-Learning-Based Early Warning System Boosted by Topological Data Analysis

We propose a novel early warning system for detecting financial market crashes that utilizes the information extracted from the shape of financial market movement. Our system incorporates Topological Data Analysis (TDA), a new set of data analytics techniques specialised in profiling the shape of data, into a more traditional machine learning framework. Incorporating TDA leads to substantial improvements in timely detecting the onset of a sharp market decline. Our framework is both able to generate new features and also unlock more value from existing factors. Our results illustrate the importance of understanding the shape of financial market data and suggest that incorporating TDA into a machine learning framework could be beneficial in a number of financial market settings.