Learning,information processing and order submission in limit order markets

By introducing a genetic algorithm learning with a classifier system into a limit order market, this paper provides a unified framework of microstructure and agent-based models of limit order markets that allows traders to determine their order submission endogenously according to market conditions. It examines how traders process and learn from market information and how the learning affects limit order markets. It is found that, measured by the average usage of different group of market information, trading rules under the learning become stationary in the long run. Also informed traders pay more attention to the last transaction sign while uninformed traders pay more attention to technical rules. Learning of uninformed traders improves market information efficiency, but not necessarily when informed traders learn. Opposite to the learning of informed traders, learning makes uninformed traders submit less aggressive limit orders and more market orders. Furthermore private values can have significant impact in the short run, but not in the long run. One implication is that the probability of informed trading (PIN) is positively related to the volatility and the bid-ask spread.     

Estimating heterogeneous agents behavior in a two-market financial system

In this paper, we propose a two-market empirical model with heterogeneous agents based on Chiarella et al. (J Econ Behav Organ 83(3):446–460, 2012). Using monthly data of French and US stock markets, the regression shows that individual markets have features of a two-regime switching process. By including inter-market traders whose trading decision is based on fundamental value of foreign market, the two-market model has a better capability in explaining both markets with domestic fundamental traders turning to be significant. The existence of inter-market traders implies that the two markets impact each other through their fundamentals and hence share some common set of factors, which provides foundation of market interactions, such as market co-movement.     

Fake news

This analysis uses Twitter stock and options prices sampled at a 30 s frequency around the fake news announcement, of a bid for a controlling stake in Twitter stock, to investigate how noise trading and informed trading is disseminated into equity and option markets. We find reaction to the fake news occurred in the equity market, and the option market reacted with a delay. This differs from many analyses of actual news events, which found informed traders prefer the options market, and information from their trades then leaks into the equity market. We conclude uninformed traders, and those aware of the hoax, prefer to trade in equity over option markets. This result has implications for isolating informed trading around actual news events.     

Information interaction,behavioral synchronization and asset market volatility

This paper focuses on the synchronization of trading behavior caused by interactions among traders. The study begins with an agent-based model in which most agents make investment decisions based on three types of information (neighbor, public and private information) according to their heterogeneous personal preferences. Considering the influence of the social relationship network in reality, traders in our model are connected by a complex network. We use four different types of networks. The simulated results of this model reproduce several stylized facts about the asset market. Simulated results show that when preference of traders for neighbor information increases, the level of trust among traders will gradually increase and then there will be a “leap” at a certain point. The mutual trust among traders is achieved through coupling effect in the network. Moreover, with the frequent synchronization of traders’ behavior, extreme phenomena in financial markets will emerge. In addition, our numerical simulations show that when the average degree of network is higher, behavioral synchronization will emerge at a lower level of dependence on neighbor information. The degree distribution of the network will also have a significant influence on the convergence processes of behavioral synchronization. The more heterogeneous the degree distribution is, the easier the behavioral synchronization process will occur. In our simulations, the mode of agent behavior expectation will also enhance the effect of network structure to behavioral synchronization. Finally, this paper affirms the important role of public information and learning mechanism (expectation formation mode) in the process of behavioral synchronization.     

Foreign equity flows: Boon or bane to the liquidity of Malaysian stock market?

This paper examines the impact of gross foreign equity inflows on aggregate liquidity of the Malaysian stock market using newly assembled foreign trading data and the best performing bid-ask spread proxy. Employing vector autoregression, we discover a one-way causality from gross inflows to aggregate liquidity, and foreign investors erode liquidity of the Malaysian stock market. Additional analyses reveal that uncertainties in the U.S. markets negatively affect aggregate liquidity through the flows of foreign institutions, whose positive feedback trading destabilizes the local bourse. Despite the shocks, there is sufficient liquidity provision from local state-backed institutional funds and local proprietary day traders.     

中国城市土地市场存在“赢者诅咒”吗？

借鉴相关研究成果,构建一个综合性的时间窗口分析模型,利用我国境内房地产上市公司在20个城市购置的205宗土地作为样本,设置前向与后向共八个事件窗口,定量考察土地市场价格信号对股票市场的影响。实证结果表明：土地市场与股票市场是两个高度关联的市场,两个市场之间存在信号传递作用,短期内具有正向冲击效应,地价信号在土地出让后能够在短期内影响股票收益率,形成对股票价格的短期冲击效应;土地出让价格信号对股票市场的影响具有时间上的不对称性,地价信号对于股票收益率的影响主要发生在土地成交之后,股票市场对于土地竞拍以前的信息没有明显响应;土地市场所发现的价格信号是关联市场价格波动的信号源,改变竞价人的预期是市场稳定的关键。     

Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models

This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.     

The stock–bond comovements and cross-market trading

We propose an asset pricing model with heterogeneous agents allocating capital to the stock and bond markets to optimize their portfolios, utilizing the dynamic interaction between the two markets. While some agents focus on the stock market and have more expertise in it, the others specialize in the bond market. Based on their comparative advantages in a particular market, heterogeneous agents constantly revise their investment portfolios by taking into account the time-varying stock–bond return comovements and the changing market conditions. Agents׳ collective investment behavior shapes the stock–bond interlinkage, which feedbacks on their subsequent capital allocations. Using monthly US stock and bond data from January 1990 to June 2014, we estimate the vector autoregression model with threshold and Markov switching mechanisms. We find evidence in support of flight-to-quality and show that it is mainly driven by the technical traders who actively sell stocks and buy bonds during periods of high market uncertainty.     

Time–frequency quantile dependence between Bitcoin and global equity markets

In this paper, we examine return dependence between Bitcoin and stock market returns using a novel quantile cross-spectral dependence approach. The results suggest a right-tail (high return) dependence between Bitcoin and the stock markets in the long term and that said dependence decreases significantly from yearly to monthly investment horizons. Furthermore, right-tail dependence between Bitcoin and the US stock market is the strongest compared with other stock markets. We also extract information on the time-varying and time–frequency structure of co-movements between Bitcoin and the stock markets using wavelet-coherence analysis, the results of which suggest that the co-movement between Bitcoin and the US stock market is positive, whereas, for other stock markets, it is negative at certain frequencies and time periods. Overall, the findings highlight additional risk-management capabilities of Bitcoin according to different stock markets.     

The impact of individual investor trading on information asymmetry in the Korean stock market

This paper examines the impact of individual investor trading on information asymmetry in the market. In particular, we examine the relationship between the trading volume by individual investors and the corresponding bid-ask spread in the Korean stock market, where the majority of the trading activity is driven by individual investors and therefore information asymmetry can be evident. We find that high trading activity by individual investors increases the bid-ask spread in a short investment horizon, suggesting that individual investors, as uninformed and unsophisticated traders, amplify the degree of information asymmetry in the market through trading.     

What do movements in financial traders’ net long positions reveal about aggregate stock returns?

Previous financial economics studies have successfully identified the existence of informed trading in futures markets; however, there is no study on the specific type of strategy chosen by informed agents to maximize profits. To fill this gap in the literature, we investigate the importance of movements in futures traders’ net long positions in predicting aggregate equity market returns. This study finds that movements in the net long positions of bond, commodity, and stock futures traders are strong predictors of aggregate stock returns as they outperform a large number of popular return predictors both in and out of sample. In addition, a one-standard-deviation change in futures traders’ net long positions can lead to an increase (decrease) of up to 3.4% (4.12%) in annualized market excess equity returns. The study’s first-order autocorrelation results reveal an absence of persistence in the net long predictors. A vector autoregression decomposition shows that the economic source of financial traders’ net long position predictive power stems predominantly from the discount rate and cash flow channels. Overall, the study finds that financial traders are informed traders who are able to anticipate future aggregate cash flows and associated discount rate news.     

Modelling contagion of financial crises

Inspired by the empirical findings, we include international traders to capture linkage between markets and propose a two-market heterogeneous agents model to simulate financial crisis with contagion effect. This paper manages to calibrate sudden crash behavior of US and UK stock markets during “Black Monday” of 1987 besides smooth crisis and disturbing crisis categorized in literature. It is implied that financial crisis and its contagion could be endogenous, which supports a scenario of over-valuation causing a financial crisis. In addition, the model shows that financial system could be fragile in which small shock(s) hitting individual market’s fundamental could cause financial crisis spreading to the other market. This also supports a scenario of external shock triggering a financial crisis. Lastly, to demonstrate the relevance of our model to financial markets, we manage to match typical stylized facts, especially cross-correlation which is exclusive to a multiple-market case.     

Profit opportunities with the American put-call parity when dividends are not expected

The objective of this research is to study borrowing and lending profit opportunities with the put-call parity of American options when dividends on the stock are not expected. Studying profit opportunities embedded in the put-call parity is intriguing because of their relative simplicity. The only assumptions necessary for the parity to hold are that option markets are frictionless and generate efficient prices of puts and calls around the underlying stock price. For this reason alone (parsimony of postulates) the put-call parity is a tempting vehicle for studying option market efficiency. In this work it is shown that both synthetic lending and borrowing parities (before and after transaction costs), on average and ex post, have negative expected profits (i.e. put-call parity implied rates are inferior to the observed riskless rate). When certain trading rules are established, however, empirical evidence of substantial profit opportunities with both lending and borrowing with the American parity (even after considering transaction costs) is observed. It is also shown that these opportunities are greater for some stocks than for others. The existence of these disparities might be an indication that the pricing mechanism of the respective options is not always in sync. The duration of disequilibrium between the options market and the stock market suggests that such occurrences are not just random bursts.     

Self-organization and the persistence of noise in financial markets

A dynamic model of financial markets with learning is demonstrated to produce a self-organized system that displays critical behavior. The price contains private information that traders learn to extract and employ to forecast future value. Since the price reflects the beliefs of the traders, the learning process is self-referencing. As the market learns to correctly extract information from the price, the market deemphasizes private information. Despite the convergence of the model towards the parameters producing efficiency, pricing deviations remain constant due to the increased sensitivity of the price to small errors in information extraction produced by the model's own convergence.     

沪深股市在牛市和熊市阶段的波动非对称效应实证分析

本文分别采用EGARCH-M、TGARCH-M模型对沪深股市在牛市和熊市阶段的非对称波动效应进行了分析,这两个模型得出了相同的结论,在牛市阶段利好消息引起股市更大的波动,在熊市阶段利空消息引起股市更大的波动,而且这两个模型同时也说明了我国股市风险和收益的正相关关系,并从我国股票市场交易者构成和交易机制两方面说明了波动非对称的原因。     

Costly arbitrage through pairs trading

We study the optimal trading policy of an arbitrageur who can exploit temporary mispricing in a market with two convergent assets. We build on the model of Liu and Timmermann (2013) and include transaction costs, which impose additional limits to the implementation of such convergence trade strategy. We show that the presence of transaction costs could reveal an endogenous stop-loss concern in a certain economy, which affects the optimal policy of the arbitrageur in significant ways. Using pairs of dual-listed Chinese stock shares as samples and a pairs trading strategy based on standard deviation of the spread as benchmark, we demonstrate the efficiency of the strategy implied by our model. Several extensions of our model are also discussed.     

Self-organized criticality in a herd behavior model of financial markets

This paper explains the fat-tail distribution of asset transaction volumes and prices by a model of rational herd behavior of traders. Each trader decides whether to buy an asset by observing private information and other traders’ actions. A trader’s buying action reveals his positive private information and affects the other traders’ beliefs in favor of buying, leading to strategic complementarity. A power-law distribution emerges for the number of buying actions in a static Nash equilibrium. This model provides an economic reason as to why the stock market has to exhibit a criticality in the connectivity of the traders’ actions. I am benefited by comments from the seminar participants at University of Tokyo and the Econophysics Colloquium 2006 at International Christian University, the editors of the special issue, and particularly an anonymous referee.     

Determining the optimal market structure using near-zero intelligence traders

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, which orders are executed as well as a range of parameters regarding market intervention by specialists and the presence of informed traders. We optimize these trading rules using a population-based incremental learning algorithm seeking to maximize the trading volume. Our results suggest markets should choose a large tick size and ensure only a small fraction of traders are informed about the order book. The effect of trading rules regarding the determination of prices, priority rules, and specialist intervention, we find to have an ambiguous effect on the outcome.     

Time series properties of an artificial stock market

This paper presents results from an experimental computer simulated stock market. In this market artificial intelligence algorithms take on the role of traders. They make predictions about the future, and buy and sell stock as indicated by their expectations of future risk and return. Prices are set endogenously to clear the market. Time series from this market are analyzed from the standpoint of well-known empirical features in real markets. The simulated market is able to replicate several of these phenomenon, including fundamental and technical predictability, volatility persistence, and leptokurtosis. Moreover, agent behavior is shown to be consistent with these features, in that they condition on the variables that are found to be significant in the time series tests. Agents are also able to collectively learn a homogeneous rational expectations equilibrium for certain parameters giving both time series and individual forecast values consistent with the equilibrium parameter values.     

Does a ‘financial transaction tax’ drive out information mirages? An experimental analysis

Motivated by the debate over the economic implications of financial transaction taxes, the present study involved a thorough investigation of the impact of such taxes on a financial market of the type described by Camerer and Weigelt (J Bus 64:463–493, 1991), whereby noise traders are unaware of whether privileged information is fluctuating in the market. Two treatment conditions were opposed to a baseline condition in which no tax was levied. The two treatment conditions imposed a transaction tax equal to 0.5% and 1% of each transaction’s market value, respectively. The findings show that: (1) the introduction of a tax did not affect the occurrence of a mirage, (2) the introduction of a tax did not improve market efficiency and (3) the introduction of a tax did not reduce the number of transactions.