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Algorithmic Trading and the Market for Liquidity [PDF]
Journal of Financial and Quantitative Analysis, 2012 AbstractWe examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders.
Ryan Riordan, Terrence Hendershott
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The Anatomy of Trading Algorithms
SSRN Electronic Journal, 2019We study the anatomy of four widely used institutional trading algorithms representing $675 billion in demand from 961 institutions. Parent orders generate hundreds of child orders which strategically employ price, time-in-force, and display priority rules to navigate the tradeoff between trading and minimizing transaction costs.
Sunil Wahal, Tyler Beason
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Algorithmic Trading in Practice
2018The use of computer algorithms in securities trading, or algorithmic trading, has become a central factor in modern financial markets. The desire for cost and time savings within the trading industry spurred buy side as well as sell side institutions to implement algorithmic services along the entire securities trading value chain.
Peter Gomber, Kai Zimmermann
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Algorithmic Trading of Portfolios
SSRN Electronic Journal, 2017We develop a portfolio trading strategy that aims to achieve optimal performance against arrival prices by minimizing market impact and risk. We first lay the groundwork by formulating a single-security trading strategy and then generalize the framework to the portfolio trading context.
Kapil Phadnis, Sanghyun Park
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Optimal Trading Stops and Algorithmic Trading
SSRN Electronic Journal, 2014Trading stops are often used by traders to risk manage their positions. In this note, we show how to derive optimal trading stops for generic algorithmic trading strategies when the P&L of the position is modelled by a Markov modulated diffusion. Optimal stop levels are derived by maximising the expected discounted utility of the P&L.
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2014
This chapter introduces readers to algorithmic trading. We provide a description of the electronic trading environment and discuss issues required to make proper algorithmic trading decisions. We present and critique the major theories of algorithmic trading, and provide further insight into where change may continue to expand.
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This chapter introduces readers to algorithmic trading. We provide a description of the electronic trading environment and discuss issues required to make proper algorithmic trading decisions. We present and critique the major theories of algorithmic trading, and provide further insight into where change may continue to expand.
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2015 IEEE 13th International Symposium on Intelligent Systems and Informatics (SISY), 2015
Although many of the world's markets have rebounded since the crash of 2008, it is believed a major correction is overdue. Some even claim that the markets are rigged in favor of those who employ high speed fiber network connections with the exchanges to front run trades.
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Although many of the world's markets have rebounded since the crash of 2008, it is believed a major correction is overdue. Some even claim that the markets are rigged in favor of those who employ high speed fiber network connections with the exchanges to front run trades.
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Algorithmic Trading and Market Quality
SSRN Electronic Journal, 2020A unique data set from NASDAQ OMX Nordic allows a deep analysis of trader types’ activity and provides evidence on the roles played in the trading ecosystem. We specifically investigate the impact of algorithmic traders on market quality relative to the activities of other market participants under various conditions.
John Paul Broussard +4 more
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Human Bias in Algorithmic Trading
SSRN Electronic Journal, 2013This paper documents a stark periodicity in intraday volume and in the number of trades. We find activity in both variables spikes by about 20% at regular intervals of 5 or 10 minutes throughout the trading day. We argue that this activity is the result of algorithmic trading influenced by human traders/programmers’ behavioral bias to transact on round
Andrei Nikiforov +3 more
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Electronic Markets and Trading Algorithms
SSRN Electronic Journal, 2011Academics attempt to understand the consequences of fragmentation, electronic markets and trading algorithms. Practitioners, by necessity, devise ever-improving trading algorithms to achieve their trading objectives. This paper is a bridge of sorts. I use the structural approach developed in decades of research on price formation to illuminate modern ...
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