Experimental evidence on trading behavior,market efficiency and price formation in double auctions with unknown trading duration

The reasons for the highly efficient market outcomes observed under the double auction remain unclear. This paper presents a series of experimental financial markets designed to investigate the importance of unknown trading period duration on trading behavior and the convergence tendencies of such markets. Using panel data techniques the results support the conclusions that individuals generally display more aggressive trading strategies, trading earlier in a period, and that markets exhibit reduced levels of informational efficiency when unknown duration is present. Markets with imperfect information structures are also studied and, in a unique result, are associated with significantly slower rates of trade, as traders become more cautious over their trading strategies. Investigation of the price formation process provides evidence that the pricing error varies over time and the estimation of a fixed effects model provides unique support that learning effects and unknown trading period duration influence the price formation process. Future refinement of theoretical models of the price formation process or institutions of exchange should recognize the effect of unknown trading period duration on market behavior, along with potential learning effects. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

Impact of value-at-risk models on market stability

Financial institutions around the world use value-at-risk (VaR) models to manage their market risk and calculate their capital requirements under Basel Accords. VaR models, as any other risk management system, are meant to keep financial institutions out of trouble by, among other things, guiding investment decisions within established risk limits so that the viability of a business is not put unduly at risk in a sharp market downturn. However, some researchers have warned that the widespread use of VaR models creates negative externalities in financial markets, as it can feed market instability and result in what has been called endogenous risk, that is, risk caused and amplified by the system itself, rather than being the result of an exogenous shock. This paper aims at analyzing the potential of VaR systems to amplify market disturbances with an agent-based model of fundamentalist and technical traders which manage their risk with a simple VaR model and must reduce their positions when the risk of their portfolio goes above a given threshold. We analyse the impact of the widespread use of VaR systems on different financial instability indicators and confirm that VaR models may induce a particular price dynamics that rises market volatility. These dynamics, which we have called `VaR cycles’, take place when a sufficient number of traders reach their VaR limit and are forced to simultaneously reduce their portfolio; the reductions cause a sudden price movement, raise volatility and force even more traders to liquidate part of their positions. The model shows that market is more prone to suffer VaR cycles when investors use a short-term horizon to calculate asset volatility or a not-too-extreme value for their risk threshold.     

When are prediction market prices most informative?

Prediction markets are a popular platform for the elicitation of incentivised crowd predictions. This paper examines the variation in the information contained in prediction market prices by studying Intrade prices on U.S. elections around the release of opinion polls. We find that poll releases stimulate an immediate uptick in trading activity. However, much of this activity involves relatively inexperienced traders, meaning that the price efficiency declines in the immediate aftermath of a poll release, and does not recover until more experienced traders enter the market in the following hours. More generally, this suggests that information releases do not necessarily improve prediction market forecasts, but instead may attract noise traders who temporarily reduce the price efficiency.     

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.     

Transaction taxes, greed and risk aversion in an agent-based financial market model

Recent agent-based financial market models came to the result that taxing financial transactions does not per se increase financial stability and that the response of volatility and misalignments to rising tax rates seem to be u-shaped. Moreover, greed and the risk appetite of traders are often blamed for financial instability and there is no evidence how greed and risk aversion affect the effectiveness of regulations in financial markets. We aim to add to this gap in the literature by analyzing how the effectiveness of transaction taxes depend on different behavioral patterns within an agent-based framework. Our simulations indicate that a tax rate of 0.1% demarcates the stabilizing tax regime from the destabilizing one. We figure out that transaction taxes are less effective, either when chartists trade more aggressively, fundamentalists trade less aggressively, agents switch more frequently between trading strategies or only have short memory in their fitness measures. Lower risk aversion of agents, however, makes higher tax rates more effective as indicated by a flatter volatility response curve. We conclude that additional regulations should concentrate on the traders’ responsibilities for their risk-exposure.     

Multidimensional noise and non-fundamental information diversity

This paper relaxes the common assumption of the standard competitive noisy rational expectations framework that noise is one-dimensional. Within an environment characterized by multidimensional noise, I explore the strategic interactions between different traders that are informed about different components of the noise inherent in the market price. If noise is two-dimensional, several new types of complementarities in traders’ interactions arise that cannot be studied in the classical one-dimensional framework. The higher-dimensional case uncovers that higher dimensionality of noise mitigates the possibility of a market breakdown by weakening adverse selection. On the basis of the theoretical results, I discuss some predictions and implications concerning the effects of the increased usage of “payment for order flow” in financial markets.     

Do real estate investors trade on momentum?

Real estate markets are known to be less-than-efficient for many reasons, but what roles short-term trading plays are unclear. Do short-term investors bring additional risk to the market and cause prices to deviate from fundamental values? Based on an extensive dataset of property transactions and a policy shock that substantially raised the cost of short-term trading in Hong Kong, we estimate ‘real estate risk’ with and without short-term trading based on return predictability, return volatility, and price dispersion. Our results show that as short-term investors exit the market, market returns are less predictable and less volatile, while prices are less dispersed cross-sectionally. Consistent with herding models in behavioral finance, the findings suggest that short-term investors are momentum traders who do not enhance price efficiency.     

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.     

A dynamic analysis of moving average rules

The use of various moving average (MA) rules remains popular with financial market practitioners. These rules have recently become the focus of a number empirical studies, but there have been very few studies of financial market models where some agents employ technical trading rules of the type used in practice. In this paper, we propose a dynamic financial market model in which demand for traded assets has both a fundamentalist and a chartist component. The chartist demand is governed by the difference between current price and a (long-run) MA. Both types of traders are boundedly rational in the sense that, based on a fitness measure such as realized capital gains, traders switch from a strategy with low fitness to the one with high fitness. We characterize the stability and bifurcation properties of the underlying deterministic model via the reaction coefficient of the fundamentalists, the extrapolation rate of the chartists and the lag length used for the MA. By increasing the intensity of choice to switching strategies, we then examine various rational routes to randomness for different MA rules. The price dynamics of the MA rule are also examined and one of our main findings is that an increase of the window length of the MA rule can destabilize an otherwise stable system, leading to more complicated, even chaotic behaviour. The analysis of the corresponding stochastic model is able to explain various market price phenomena, including temporary bubbles, sudden market crashes, price resistance and price switching between different levels.     

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.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

Markets with heterogeneous beliefs: A necessary and sufficient condition for a trader to vanish

What does it take to survive in the market? Previous literature has proposed sufficient conditions for a trader to vanish, which depend on pairwise comparisons of traders’ discounted beliefs. We propose a novel condition that focuses on the ratio of traders’ discounted beliefs and (approximate) equilibrium prices. Unlike existing conditions, ours is both necessary and sufficient for a trader to vanish and delivers the exact rate at which vanishing traders lose their consumption shares. As an application, we analyze the performance of two intuitive behavioral strategies: the “Follow the Leader Strategy” that prescribes mimicking the beliefs of the most successful trader, and the “Follow the Market Strategy” that prescribes to use beliefs which coincide with the state price density. Further, we show that the relative performance of vanishing traders cannot be studied in isolation. Our analysis highlights an intuitive point obscured by the existing conditions: trading in financial markets is qualitatively different from bilateral trading.     

Reinsurance or securitization: The case of natural catastrophe risk

We investigate the suitability of securitization as an alternative to reinsurance for the purpose of transferring natural catastrophe risk. We characterize the conditions under which one or the other form of risk transfer dominates using a setting in which reinsurers and traders in financial markets produce costly information about catastrophes. Such information is useful to insurers: along with the information produced by insurers themselves, it reduces insurers’ costly capital requirements. However, traders who seek to benefit from trading in financial markets may produce ‘too much’ information, thereby making risk transfer through securitization prohibitively costly.     

Heterogeneous Traders,the Leverage Effect and Volatility of the Chinese P2P Market

The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market. This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China. Based on the background data of the P2P platform, Honglingchuangtou, we use the factor analysis method to construct a platform volatility (PV) index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market. The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility. Similar to traditional financial markets, the volatility of the P2P market also shows a leverage effect, which means that the negative volatility of trader actions should have a negative impact on market fluctuations. With regard to the leverage effect, the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.     

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.     

An equilibrium model of insider trading in continuous time

We study a Bayesian–Nash equilibrium model of insider trading in continuous time. The supply of the risky asset is assumed to be stochastic. This supply can be interpreted as noise from nonrational traders (noise traders). A rational informed investor (the insider) has private information on the growth rate of the dividend flow rewarded by the risky asset. She is risk averse and maximizes her inter-temporal utility rate over an infinite time-horizon. The market is cleared by a risk neutral market maker who sets the price of the risky asset competitively as the conditional present value of future dividends, given the information supplied by the dividend history and the cumulative order flow. Due to the presence of noise traders, the market demand does not fully reveal the insider’s private information, which slowly becomes incorporated in prices. An interesting result of the paper is that a nonstandard linear filtering procedure gives an a priori form for the equilibrium strategy to be postulated. We show the existence of a stationary linear equilibrium where the insider acts strategically by taking advantage of the camouflage provided by the noise which affects the market maker’s estimates on private information. In this equilibrium, we find that the insider’s returns on the stock are uncorrelated over long periods of time. Finally, we show that the instantaneous variance of the price under asymmetric information lies between the instantaneous variance of the price under complete and incomplete information. The converse inequalities hold true for the unconditional variance of the price.     

Insider,outsider and information heterogeneity

In financial markets, although the insider has superior information relative to the outsider, the outsider may possess some information the insider can’t precisely observe. This work investigates the impacts of information heterogeneity between the insider and outsider based on Foster and Viswanathan (1994). It shows that the less the insider knows about the outsider, the more she commits to aggressive trading, enabling her to earn more at the cost of outsider’s losses. Meanwhile, information heterogeneity improves the liquidity and benefits noise traders.     

Asset allocation and multivariate position based trading

I add a second risky asset and a risk free bond to the univariate artificial market investigated by Lux and Marchesi (Int J Theor Appl Finance 3(4):675–702, 2000), keeping track of traders aggregate positions and wealth. Asset allocation and security selection are modeled as separate decision processes, as is common practice in financial institutions. Introducing position based trading avoids inconsistencies in traders inventories resulting from the order based setup of the original model, while preserving its ability to reproduce the stylized facts of financial return series.      

Financially constrained arbitrage in illiquid markets

This article analyzes trading strategies when arbitrageurs impact prices. Trades of financially constrained arbitrageurs are feedback functions of their capital, which depends on the amount traded. A component of arbitrage trading ensures financial flexibility. This hedging component explains why price deviations persist in spite of arbitrage. Financial constraints are responsible for volatile prices and for time variation in the correlations of prices across markets. Distortions arise when regulated firms can influence the dynamics of prices on which capital requirements are based. Under current value at risk (VaR) measures, large traders behave aggressively and have a cost advantage relative to other traders.