Rock around the clock: An agent-based model of low- and high-frequency trading

Abstract

Abstract. We build an agent-based model to study how the interplay between low-and highfrequency trading affects asset price dynamics. Our main goal is to investigate whether highfrequency trading exacerbates market volatility and generates flash crashes. In the model, lowfrequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of highfrequency traders, i.e., their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.

Innovation, growth and financial markets

“…Baumol also cross-checked this conclusion with available estimates of private and social return to innovation elaborated by previous work on the subject and concluded that the ratio between the private and social rate of return is around 1/5. More recently, Iacopetta et al (2014) arrived a similar estimate of about 83 % of uncompensated external benefits.…”

“…Although there are many ways in which the managers can extract private benefits, two of these seem to be more frequent (see, e.g., Khanna 2000;Morck et al 2005;Campbell and Keys 2002;Choi and Cowing 1999): Empire building benefits, whereby the manager derives private benefits from investing beyond what shareholders' value maximization calls for, and resource diversion, where the manager siphons resources of the firm. Iacopetta et al (2014) integrated these governance frictions in a Schumpeterian model where growth is driven both by the foundation of new firms that offer new intermediate products and by investments of incumbent firms in the quality of existing intermediate products. The discipline on management is solved internally through a package compensation scheme whereby the shareholders try to align the managers' interest by offering them a stake of the firm.…”

“…Opponents of these types of policies maintain that these have forestalled competition and inhibited entrepreneurship. Iacopetta et al (2014) studied the consequences of these policies. In particular, they considered the moral hazard actions of managers in situations in which they can engage in resource diversion or undertake empire building actions.…”

“…In a classic work, Manne (1965) identified three ways in which the market for corporate control can solve managerial abuses or inefficiencies: proxy fights, friendly mergers, and hostile takeovers. We then integrate issues of managerial disciplines within a Schumpeterian model, following closely Iacopetta et al (2014). In this section, we also discuss the difficulties of following the propagation mechanism of a shock, such as the one presented by Gaffeo and Massimo (2016), when firms are different in size and there are strong asymmetries in the configuration of the network that links them.…”

Extracting information from the limit order book: New measures to evaluate equity data flow

“…Finance literature employs several avenues when studying HFTs activity. Agents-based simulation is one of the most widely used method (Leal, Napoletano, Roventini, & Fagiolo, 2016;Paddrik et al, 2012;Yang et al, 2012), which requires detailed trading data from each trader to simulate traders' behaviors. Similarly, Brogaard (2010) and Kirilenko et al (2017) rely on financial data sets containing specific trader information that allow to distinguish between HFTs and non-HFTs.…”