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

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.     

An evolutionary multi‐objective optimization of trading rules in call markets

We evaluate an agent‐based model featuring near‐zero‐intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices and which orders are executed, as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. We optimize these trading rules using a multi‐objective population‐based incremental learning algorithm seeking to maximize the trading volume and minimize the bid–ask spread. Our results suggest that markets should choose a small tick size if concerns about the bid–ask spread are dominating and a large tick size if maximizing trading volume is the main aim. We also find that unless concerns about trading volume dominate, time priority is the optimal priority rule. Copyright © 2011 John Wiley & Sons, Ltd.     

THE EFFECT OF BOUNDARY CONDITIONS ON EFFICIENCY AND PRICING IN DOUBLE‐AUCTION MARKETS WITH ZERO‐INTELLIGENCE AGENTS

The computational tractability of many markets with computerized agents requires they be subject to boundary conditions that are both unnecessary and undesirable in markets with humans. In particular, double‐auction markets with zero‐intelligence agents, which are a standard baseline in computational economics, constrain all bids, offers and trade prices to lie between an upper and a lower bound. This paper shows how changes in these boundary conditions influence pricing and efficiency. It is found that boundary conditions consistent with competitive pricing are generally inconsistent with the most efficient operation of those markets. Copyright © 2012 John Wiley & Sons, Ltd.     

ANALYSIS OF AN OPTION MARKET DYNAMICS BASED ON A HETEROGENEOUS AGENT MODEL

We propose a two‐market model in which an option market and its underlying market interact. Many artificial markets representing stock markets have been developed, and these models have been actively used to investigate the effects of market rules. However, no artificial market model for derivatives has been intensively studied, even though derivative markets are increasingly important. We tested stylized facts that can be observed in an option market and our model can replicate fat‐tailed distributions, positive skew of the return and positive autocorrelation of the square of return of implied volatility. We found that the speed of volatility mean reversion for fundamentalists and the existence of chartists are important factors for replicating the positive skew of an option market. The value of fat‐tailed distributions and positive skewness of the return get closer to the real value by coupling an option market and an underlying market. Copyright © 2014 John Wiley & Sons, Ltd.     

FORECASTING FIRM RISK IN THE EMERGING MARKET OF CHINA WITH SEQUENTIAL OPTIMIZATION OF INFLUENCE FACTORS ON PERFORMANCE OF CASE‐BASED REASONING: AN EMPIRICAL STUDY WITH IMBALANCED SAMPLES

With the development of the Chinese economy, how to make the right decision regarding firms’ risk is becoming more and more important. Case‐based reasoning (CBR) is a potential method that can help forecast business risk status in advance; it is easy to apply and is able to provide good explanations of output. In order to obtain more accurate prediction with CBR, it is essential to investigate factors that influence CBR's performance, and to optimize these factors sequentially for the improvement of CBR's performance in firm risk prediction in emerging markets under a more practicable assumption. We verified that sequential optimization of feature selection, feature weighting, instance selection and the number of nearest neighbours is a possible alternative for improving predictive performance of CBR forecasting under the assumption that the number of failed samples is smaller than that of nonfailed samples. The detailed implementation includes: (1) selecting significant features through a correlation matrix and reducing feature dimensions with factor analysis; (2) using variance contribution ratios of features from factor analysis as feature weights; (3) eliminating noisy cases via a state matrix; and (4) obtaining the optimal number of nearest neighbours from empirical results among different numbers of nearest neighbours. To validate the usefulness of the sequential optimization approach, we applied it to a real‐world case: firm risk prediction with imbalanced data from the emerging market of China. Experimental results show that predictive accuracy of CBR applied in the emerging market was improved with the sequential optimization approach. Insightful thoughts from the results of the sequential optimization of the CBR forecasting system on modelling social tasks are also provided. Copyright © 2013 John Wiley & Sons, Ltd.