A Global Taxonomy of Flash Crashes: Cases Demonstrating the Operation and Impact of High-Frequency Traders

Priya Makhija, Elizabeth Chacko and Megha Kukreja

Chapter 14 in Banking Resilience:New Insights on Corporate Governance, Sustainability and Digital Innovation, 2024, pp 481-497 from World Scientific Publishing Co. Pte. Ltd.

Abstract: A flash crash is a sudden decline in the price of one or more assets that are frequently brought on by a trading mistake. A “flash collapse” is when the price of bonds, stocks, or other commodities drops sharply but then quickly rises again. The market will quickly decline, but prices will almost immediately rise again. The US stock market saw a flash crash on May 6, 2010, which was a rare occurrence in the trading system and revealed the market’s susceptibility to high-frequency trading (HFT). Numerous researchers have provided an overview of the incident and the market’s response by highlighting crucial elements like algorithmic trading (AT), the volume and frequency of selling orders in the market, the current market volatility, and the statements that were highlighted as having a significant relationship with flash collapses. This has prompted a number of regulatory and trading organizations to draft new legislation aimed at reducing the likelihood of having such catastrophes in the future. Effective regulation is needed to ensure that traders do not engage in stock manipulation or negatively impact other investors’ ability to buy and sell shares when trading based on transient price variations and trends. One cause of flash crashes is computer programs or algorithms. Therefore, to demonstrate the operation and influence of high-frequency traders, this test case has been constructed to understand how algorithms can impact trading and manipulate the entire market.

Keywords: Corporate Governance, Board Characteristics, Board Structure, Banking Institutions, Historical Literature Review, Financial Crisis, Bank Risk, CEO, Corporate Social Responsibility, CSR, Bank Efficiency, Board Governance, Board Size, Board Independence, Gender Diversity, Blau Index, Risk-adjusted Bank Performance, Tobin's Q, Return-on-Risk-Adjusted-Capital, Sharpe Ratio, Sortino Ratio, Board Diversity, Bank Risk, Bank Performance, Cost Efficiency, Bank Stability, Cultural Openness, Women Directors, Foreign Directors, Directors Educational Level, Islamic Banks, Board Diversity, Gender Diversity, Gender Quota, Emerging Economy, Bank Outcomes, Attendance, Board Effectiveness, Private Banks, Public Sector Banks, India, Corporate Social Responsibility, ESG, Sustainable Finance, Responsible Investing, Socially Responsible Banks, Risk Management, Stakeholder Engagement, Financial Stability, Sustainability, Firm Value, Climate Finance, Policy Uncertainty, Financial Stability, Climate Risk, Bibliometric Analysis, Financial Markets, Financial Assets, Asset Pricing, Capital Flows, Sustainability, Accruals, Earnings Management, Accounting Value, National Culture , Cultural Dimensions , Collective Intelligence , Secrecy, Intelligence Quotient, International Accounting, Covid-19, Pandemic, Impact, Risk, GCC Banks, GCC Islamic Banks, Conventional Banks, Profitability, Capitalization, Resilience, Asset-based Indicators, Bank, China, Density, Diversification, FinTech, Focus, Income-Based Indicators, Kernel, Quantile Regression, Big Data, Economic Capital, Emerging Markets, FinTech, GARCH-M (1; 1), GCC Financial Markets, Liquidity Risk, Reinforcement Machine Learning, Risk Management, Portfolio Management, Liquidity-Adjusted Value at Risk, Risk Metrics, Risk Spillover Effects, Internet Finance, GARCH Models, VaR Models, CoVaR Models, Securities Firms, COVID-19, Systemic Risks, China, Regulatory Reporting, Systemic Risk, Financial Stability, SupTech, RegTech, Financial Reporting, Algorithmic Data Standards, Blockchain, Financial Crisis, BCBS 239, Banks, Gulf Cooperation Council (GCC), Political Connections, Trade-Off Theory, Pecking Order Theory, Capital Structure, Risk, Profitability, Speed Of Adjustment, Generalized Method of Moments (GMM), Flash Crash, High Frequency Traders, Regulatory Framework, Algorithms, Algorithmic Trading (AT), Financial Market, Capital Market, (search for similar items in EconPapers)
JEL-codes: G21 G3 G34 (search for similar items in EconPapers)
Date: 2024
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