Regulations' effectiveness for market turbulence by large erroneous orders using multi agent simulation

Abstract

We built an artificial market model and investigated the impact of large erroneous orders on price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analyzed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules adopting the trigger method that the Japan Financial Services Agency adopted on November 2013.

Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

“…The values of these parameters were determined by trial-and-error experiments in Mizuta et al [36], which described the base model used in Yagi et al [46]. Mizuta et al [36] added a learning process to the model of Chiarella et al [34] and reproduced large-scale market confusion such as a bubble or a financial crisis in their artificial market model. Table III shows the statistics for stylized facts, which are averages over 100 simulation runs, for which we calculated price returns at intervals of 100 time units.…”

Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation

“…We modelled the learning process as follows based on Mizuta et al [12]. e reason why the learning process is necessary and why the process should be modelled as described below is given in Mizuta et al [12].…”

A Study on the Market Impact of the Rule for Investment Diversification at the Time of a Market Crash Using a Multi-Agent Simulation

“…Furthermore, no investor sticks to a single strategy when making investment decisions, but rather each switches between strategies according to market price information. Studies on artificial markets to investigate market regulations have had some success in market analysis in recent years [7], [14], [15]; however, the only study on the effects of the rule for investment diversification on the market using an artificial market has been Yagi et al [16].…”

“…Mizuta et al [7] added a learning process to the model of Chiarella et al and reproduced large-scale market confusion such as a bubble or a financial crisis in their artificial market model. This model also includes investors following different investment strategies, such as fundamental investments or technical investments.…”

“…An artificial market is a multi-agent based model of financial markets [5]- [7]. Each of the agents is assigned a specific trading (i.e., buying and selling) rule and then set to trade financial assets as an investor.…”