Prudential supervisory disclosure (PSD) with supervisory technology (SupTech): lessons from a FinTech crisis

The U.S. financial markets faced an unprecedented rapid decline and recovery on May 6, 2010, known as the May 6 flash crash. Roughly one trillion $ market value in less than thirty minutes vanished with the biggest one-day point decline in the history of the DJIA at the time. Since the market events took place in electronic markets, and algorithmic trading and high-frequency trading, parts of FinTech, played significant roles, we handle the May 6 flash crash from the FinTech, SupTech, and financial supervision perspectives. With the flashback method, we analyzed the reactions of market participants, media, and two financial supervisors, the SEC, and the CFTC, to the market crash. We find that the technological imbalance between financial markets or institutions and their supervisors drove the markets in uncertainty, hence in a fear and panic environment. Since the imbalance has not diminished yet, the same risks still exist. As a remedy, we introduce a new concept and model with a well-functioning SupTech system to cope with the May 6 type FinTech crises.

Stock Returns and Roughness Extreme Variations: A New Model for Monitoring 2008 Market Crash and 2015 Flash Crash

We use Student’s t-copula to study the extreme variations in the bivariate kinematic time series of log–return and log–roughness of the S&P 500 index during two market crashes, the financial crisis in 2008 and the flash crash on Monday August 24, 2015. The stable and small values of the tail dependence index observed for some months preceding the market crash of 2008 indicate that the joint distribution of daily return and roughness was close to a normal one. The volatility of the tail and degree of freedom indices as determined by Student’s t-copula falls down substantially after the stock market crash of 2008. The number of degrees of freedom in the empirically observed distributions falls while the tail coefficient of the copula increases, indicating the long memory effect of the market crash of 2008. A significant change in the tail and degree of freedom indices associated with the intraday price of S&P 500 index is observed before, during, and after the flash crash on August 24, 2015. The long memory effect of the stock market flash crash of August 2015 is indicated by the number of degrees of freedom in the empirically observed distributions fall while the tail coefficient of the joint distribution increases after the flash crash. The small and stable value of degrees of freedom preceding the flash crash provides evidence that the joint distribution for intraday data of return and roughness is heavy-tailed. Time-varying long-range dependence in mean and volatility as well as the Chow and Bai-Perron tests indicate non-stability of the stock market in this period.

You Need Three Butterflies to Cause a Hurricane

The aim of this study is verifying the impact of high volatility, scarce liquidity and stop-loss orders on abnormal events like the May 6, 2010 Flash Crash. The paper assumes those three factors to be the main drivers, proposes a mathematical model based upon them and analyses audit trail data to verify whether those factors actually were at the origin of that event. It uses the concept of 'run', an uninterrupted sequence of trades all occurring in the same direction and compares volatility, liquidity and occurrence of stop-loss orders over the analysis period. The results found provide suggestive evidence that a combination of the three factors contributed to the crash. Each of them, taken individually, does not usually lead to extreme behaviours. Even two factors together may not disturb the orderly functioning of the markets but the combination of volatility, scarce liquidity and stop-loss orders may lead to a crisis.

Understanding the Flash Crash – state of the art

Purpose The purpose of this paper is to provide the current state of knowledge about the Flash Crash. It has been one of the remarkable events of the decade and its causes are still a matter of debate. Design/methodology/approach This paper reviews the literature since the early days to most recent findings, and critically compares the most important hypotheses about the possible causes of the crisis. Findings Among the causes of the Flash Crash, the literature has propsed the following: a large selling program triggering the sales wave, small but not negligible delays suffered by the exchange computers, the micro-structure of the financial markets, the price fall leading to margin cover and forced sales, some types of feedback loops leading to downward price spiral, stop-loss orders coupled with scarce liquidity that triggered price reduction. On its turn leading to further stop-loss activation, the use of Intermarket Sweep Orders, that is, orders that sacrificed search for the best price to speed of execution, and dumb algorithms. Originality/value The results of the previous section are condensed in a set of policy implications and recommendations.