Fragmentation and Stability of Markets

Ladley, Daniel; Lensberg, Terje; Palczewski, Jan; Sc̣henk-Hoppé, Klaus Reiner

doi:10.2139/ssrn.2304450

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Ladley et al. ( 2015 ) have shown that centralizing markets can lead to higher price volatility and less resilience to shocks because it increases the equilibrium proportion of unskilled traders. Kovaleva and Iori ( 2015 ) have studied the effects of pre-trade quote transparency on market quality in an artificial limit order market where traders react to the imbalance in demand and supply posted in the limit order book.…”

Section: Agent-based Models and Financial Stabilitymentioning

confidence: 99%

Advances in the agent-based modeling of economic and social behavior

Steinbacher¹,

Raddant

Karimi

et al. 2021

SN Bus Econ

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In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

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Section: Agent-based Models and Financial Stabilitymentioning

confidence: 99%

Advances in the agent-based modeling of economic and social behavior

Steinbacher¹,

Raddant

Karimi

et al. 2021

SN Bus Econ

View full text Add to dashboard Cite

show abstract

“…Yeh (2008) uses a genetic programming model to show that greater intelligence improves market efficiency. Ladley et al (2015) use a genetic programming model to investigate the relationship between skill and market fragmentation and show that large numbers of unskilled individuals make the market more susceptible to shocks. Whilst Manahov et al (2014) shows that varying the length of trading strategies impacts trader and market performance.…”

Section: Introductionmentioning

confidence: 99%

The Design and Regulation of High Frequency Traders

Ladley

2019

SSRN Journal

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Central to the ability of a high frequency trader to make money is speed. In order to be first to trading opportunities firms invest in the fastest hardware and the shortest connections between their machines and the markets. This, however, is not enough, algorithms must be short, no more than a few lines of code. As a result there is a trade-off in the design of optimal HFT strategies: being the fastest necessitates being less sophisticated. To understand the effect of this tension a computational model is presented that captures latency, both of code execution and information transmission. Trading algorithms are modelled through genetic programmes with longer programmes allowing more sophisticated decisions at the cost of slower execution times. It is shown that depending on the market composition short fast strategies and slower more sophisticated strategies may both be viable and exploit different trading opportunities. The relative profits of these different approaches vary, however, slow traders benefit from their presence. A suite of regulations are tested to manage the risks associated with high frequency trading, the majority are found to be ineffective, however, constraining the ratio of orders to trades may be promising.

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Fragmentation and Stability of Markets

Cited by 2 publications

References 36 publications

Advances in the agent-based modeling of economic and social behavior

Advances in the agent-based modeling of economic and social behavior

The Design and Regulation of High Frequency Traders

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