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.

Algorithmic Trading and the Market for Liquidity

Abstract: We examine the role of algorithmic traders (AT) in liquidity supply and demand in the 30 DAX stocks on the Deutsche Boerse in January 2008. AT represent 52% of market order volume and 64% of nonmarketable limit order volume. AT more actively monitor market liquidity than human traders. AT consume liquidity when it is cheap, i.e., when the bid-ask quotes are narrow, and supply liquidity when it is expensive. When spreads are narrow AT are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. AT react more quickly to events and even more so when spreads are wide.

Noise as Information for Illiquidity

Abstract: We propose a broad measure of liquidity for the overall financial market by exploiting its connection with the amount of arbitrage capital in the market and the potential impact on price deviations in US Treasurys. When arbitrage capital is abundant, we expect the arbitrage forces to smooth out the Treasury yield curve and keep the dispersion low. During market crises, the shortage of arbitrage capital leaves the yields to move more freely relative to the curve, resulting in more “noise.” As such, noise in the Treasury market can be informative and we expect this information about liquidity to reflect the broad market conditions because of the central importance of the Treasury market and its low intrinsic noise – high liquidity and low credit risk. Indeed, we find that our “noise” measure captures episodes of liquidity crises of different origins and magnitudes and is also related to other known liquidity proxies. Moreover, using it as a priced risk factor helps explain cross-sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market.

Automated Trading System in an Electronic Trading Exchange

Abstract: An electronic exchange system network includes a trader site having an automated trading system capable of submitting orders and/or quotes to an exchange site. The automated trading system determines whether an order or quote should be submitted based on, for example, the current market price of an option and theoretical buy and sell prices. The theoretical buy and sell prices are derived from, among other things, the current market price of the security underlying the option. The theoretical buy and sell prices are calculated when underlying factors that contribute to the theoretical prices change. Computation times of the theoretical prices may be reduced by using precalculated values and/or using interpolation and extrapolation. Other techniques may be used in addition or in the alternative to speed automatic decision-making. In addition, a system of checks may be conducted to ensure accurate and safe automated trading. The automated trading system may be capable of automatically submitting orders in connection with the underlying security in order to hedge part of the delta risk associated with the automated option trades.