Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

An agent based model of the E-Mini S&P 500 applied to flash crash analysis

Abstract: We propose a zero-intelligence agent-based model of the E-Mini S&P 500 futures market, which allows for a close examination of the market microstructure. Several classes of agents are characterized by their order speed and order placement within the limit order book. These agents' orders populate the simulated market in a way consistent with real world participation rates. By modeling separate trading classes the simulation is able to capture interactions between classes, which are essential to recreating market phenomenon. The simulated market is validated against empirically observed characteristics of price returns and volatility. We therefore conclude that our agent based simulation model can accurately capture the key characteristics of the nearest months E-Mini S&P 500 futures market. Additionally, to illustrate the applicability of the simulation, experiments were run, which confirm the leading hypothesis for the cause of the May 6th 2010 Flash Crash.

Trading and hedging in S&P 500 spot and futures markets using genetic programming

Abstract: In this study, genetic programming, an optimization technique based on the principles of natural evolution, was used to generate trading and hedging rules in Standard & Poor’s 500 spot and futures markets. I adopted a realistic trading process that included reasonable transaction costs, obtainable execution prices, and all the unique features of futures trading. The results suggested that the spot market was quite efficient with most genetically generated trading rules duplicating the buy-and-hold strategy. Most of the trading activities of these trading programs were in the futures market, where transaction costs were substantially lower. The out-of-sample performance of these trading rules varied from year to year, indicating that genetic programming could not consistently find outperforming technical trading rules. Some evidence was found for the superior market-timing abilities of these rules. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:911–942, 2000

Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE

Abstract: We study pre-trade transparency by looking at the introduction of NYSE's OpenBook service that provides limit order book information to traders off the exchange floor. We find that traders attempt to manage limit order exposure: They submit smaller orders and cancel orders faster. Specialists' participation rate and the depth they add to the quote decline. Liquidity increases in that the price impact of orders declines, and we find some improvement in the informational efficiency of prices. These results suggest that an increase in pre-trade transparency affects investors' trading strategies and can improve certain dimensions of market quality.

How Algorithms Interact: Goffman's 'Interaction Order' in Automated Trading

Abstract: In a talk in 2013, Karin Knorr Cetina referred to ‘the interaction order of algorithms’, a phrase that implicitly invokes Erving Goffman's ‘interaction order’. This paper explores the application o...