This granularity permits the analysis of sequential transactions that often occur within milliseconds of each other, making it possible to construct a complex system containing multiple feedback loops that depict the impacts of a stock not only on itself but also on the other 29 stocks in the network. We use individual stock sub-second transactions data, which are the most granular data available at the time of the crash. The Hawkes excitation matrix measures how the influence these 30 stocks have on each other and themselves and how these influences may evolve over time as traders learn about and react to the ever-changing environment. The DJIA companies are large, blue chip firms chosen to represent the bulk of US economic activity. We focus on the 30 stocks that make up the Dow Jones Industrial Average (DJIA). We explore the nature of the Flash Crash because its short length (36 min from the start through recovery) severely limits the number and resultant impacts of external factors, such as market intervention by government or stock exchange regulators that might affect the behavior of the market during its drawdown and subsequent recovery.
In addition to the Flash Crash, recent crashes include Black Monday (October 19, 1987), the Dotcom Bubble Burst (March 10, 2000), the 2008 Financial Crisis (September 29, 2008), and the 2020 Pandemic (February 19, 2020). The analyses provide insights into the development of market design initiatives, trading strategies, and risk management methods that incorporate an intra-day, endogenous perspective.
The purpose of this paper is (1) to model the price movements exhibited by individual stocks during the 2010 Flash Crash using a Hawkes process excitation matrix and (2) to interpret its entries in the context of complex networks and crowd behavior from the Granger causality and Adaptive Market Hypothesis perspectives. Although this phenomenon may provide transactional benefits, it also enhances the fragility of the system through common risk exposure via the ownership of similar assets, liquidity shocks, and macroeconomic shocks in general, making these risks difficult to manage because of network complexity and market crashes. As Glasserman and Young ( 2016) point out, interconnectedness is a defining characteristic of global and domestic modern financial systems. Taken together, a sequence of price changes involving a broad group of stocks in a relatively short time period that results in a meaningful cumulative decrease in their paper values is referred to as a market crash. Prices, however, often exhibit rapid fluctuations that at times are characterized by large and often unexpected changes.
A trade is consummated when a price is agreed upon. Their primary function is to provide a straightforward and convenient mechanism to transfer ownership of an asset claim in a formal trading venue that is governed by rules of trade. Stock markets are a key component of an economy based on the principles of capitalism.
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