Topic
Flash crash
About: Flash crash is a research topic. Over the lifetime, 239 publications have been published within this topic receiving 5343 citations.
Papers
TL;DR: In this paper, the causal effect of algorithmic trading on the New York Stock Exchange's quote dissemination has been analyzed. And the results indicate that AT improves liquidity and enhances the informativeness of quotes.
Abstract: Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes. TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded. Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries. By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce firms’ cost of capital. Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a starting point near zero in the mid-1990s, AT is thought to be responsible for
1,478 citations
Posted Content•
TL;DR: This article used audit-trail data to compare the trading of High Frequency Traders and other traders during the Flash Crash of May 6, 2010 with the three prior trading days, and concluded that the inventories of High-frequency Traders were too small to have caused or prevented the flash crash.
Abstract: We use E-mini S&P 500 futures market audit-trail data to compare the trading of High Frequency Traders and other traders during the Flash Crash of May 6, 2010 with the three prior trading days. On all 4 days, High Frequency Trader’s inventories rarely exceeded 3000 contracts, mean-reverted to 0 with a half-life of approximately two minutes, and had similar correlations with price changes over one second intervals. The Flash Crash was triggered by a 75,000 contract sell program. We conclude that the inventories of High Frequency Traders were too small to have caused or prevented the Flash Crash.
646 citations
TL;DR: In this paper, the authors study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure, and they find that the trading pattern of the most active non-designated high frequency traders did not change when prices fell during the Flash Crash.
Abstract: We study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure. On May 6, 2010, U.S. financial markets experienced a systemic intraday event—the Flash Crash—where a large automated selling program was rapidly executed in the E-mini S&P 500 stock index futures market. Using audit trail transaction-level data for the E-mini on May 6 and the previous three days, we find that the trading pattern of the most active nondesignated intraday intermediaries (classified as High-Frequency Traders) did not change when prices fell during the Flash Crash.
640 citations
TL;DR: In this paper, the authors argue that the flash crash was the result of the new dynamics at play in the current market structure and highlight the role played by order toxicity in affecting liquidity provision, and show that a measure of this toxicity, the volume synchronized probability of informed trading (VPIN), captures the increasing toxicity of the order flow in the hours and days prior to collapse.
Abstract: The “flash crash” of May 6, 2010, was the second-largest point swing (1,010.14 points) and the biggest one-day point decline (998.5 points) in the history of the Dow Jones Industrial Average. For a few minutes, $1 trillion in market value vanished. In this article, the authors argue that the flash crash was the result of the new dynamics at play in the current market structure. They highlight the role played by order toxicity in affecting liquidity provision, and they show that a measure of this toxicity, the volume synchronized probability of informed trading (VPIN), captures the increasing toxicity of the order flow in the hours and days prior to collapse. Because the flash crash might have been avoided had liquidity providers remained in the marketplace, a solution is proposed in the form of a “VPIN contract” that would allow liquidity providers to dynamically monitor and manage their risks.
397 citations
TL;DR: In this paper, the authors study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure, and find that the trading pattern of the most active non-designated high frequency traders (classified as High Frequency Traders) did not change when prices fell during the Flash Crash.
Abstract: We study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure. On May 6, 2010, U.S. financial markets experienced a systemic intraday event, known as the Flash Crash, when a large automated sell program was rapidly executed in the E-mini S&P 500 stock index futures market. Using audit trail transaction-level data for the E-mini on May 6 and the previous three days, we find that the trading pattern of the most active non-designated intraday intermediaries (classified as High Frequency Traders) did not change when prices fell during the Flash Crash.
374 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 13 |
| 2020 | 14 |
| 2019 | 17 |
| 2018 | 16 |
| 2017 | 28 |
| 2016 | 16 |