Tick size and market quality: Simulations based on agent‐based artificial stock markets

Abstract

Summary This paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

“…Maryyam (2016) studied the impact of firms' performance on stock returns and found that ROA, leverage, EPS, book value, and price‐to‐book ratio provide a unique contribution to a statistically significant predictor of stock prices. Recently, Yang, Zhang, and Ye (2020) investigated the effects of the tick size in an agent‐based order‐driven artificial stock market. They found that the tick size system has a significant impact on the quality of the markets.…”

Sticky Stock Market Analysts

“…The Frankfurt Artificial Stock Market (Hein et al 2012) also takes into account a realistic stock exchange mechanism, different communication structures between the agents, and different investment philosophies of the agents. Recently, for example, information asymmetries (Krichene and El-Aroui 2018), memory length and confidence level (Bertella et al 2014), risk preference (Chen and Huang 2008), tick size systems (Yang et al 2020), and different types of stocks (Ponta and Cincotti 2018) have been taken into account in artificial stock markets. Artificial stock markets have the significant advantage that extreme events (crashes) can be observed more frequently and can be better analyzed than on real stock markets.…”

“…In order to successfully design active investment strategies such as market timing, stock picking, or index picking, forecasts of future stock market developments are indispensable. New forecasting methods are constantly being discussed: econometric models (Goyal et al 2021;Chen and Vincent 2016;Welch and Goyal 2008), artificial neural networks (Rajab and Sharma 2019;Atsalakis and Valavanis 2009), artificial intelligence (Mallikarjuna and Rao 2019), capital market simulations with multi-agent models (Yang et al 2020;Krichene and El-Aroui 2018;Arthur et al 1997), modelling based on the expectations of capital market agents (Atmaz et al 2021;Greenwood and Shleifer 2014), and neuro-psycho-economics approaches (Ortiz-Teran et al 2019;Kandasamy et al 2016;Werner et al 2009). However, testing these approaches using ex-post forecasts in an out-ofsample data domain repeatedly leads to apparent forecasting successes that then may not materialize in real ex-ante settings (Kazak and Pohlmeier 2019).…”

Who gains and who loses on stock markets? Risk preferences and timing matter

“…This limitation is easily overcome using the empirical framework of Artificial Markets with virtual investors. Multi‐agent modeling is widely applied to financial market studies (Bajo et al 2017; Biondi & Righi, 2017; Jacobs et al 2010; Mizuta et al 2015; Veryzhenko et al 2017; Yang et al 2020), since these models are able to reflect the complexity and automation of financial markets. The idea of agent‐based simulations is to study complex systems by representing each of the microscopic elements or agents individually and by simulating the behavior of the entire system, keeping track of all the individual elements and their interactions over time.…”