Adverse effects of leverage and short-selling constraints in a financial market model with heterogeneous agents

This paper investigates the impact of leverage and short-selling constraints on financial market stability. Investors׳ demand is modelled in a well-known asset pricing model with heterogeneous beliefs. In particular, I generalise the heterogeneous agents model of Brock and Hommes (1998) and Anufriev and Tuinstra (2013) to allow for leverage constraints as well as a short-selling tax. I consider two examples of adaptive belief systems describing the coevolution of prices and investors׳ beliefs. First, if the market is inhabited by fundamentalist and chartist traders, demand constraints have potential adverse effects and may restrict the stabilising fundamentalist strategy such that mispricing and price volatility increase. Second, if the market is inhabited by fundamentalists, optimists and pessimists with fixed beliefs, demand constraints drive down price volatility, but mispricing remains. The results suggest the stabilising effects of demand constraints in financial markets are limited. Only if asset prices are too high compared to fundamentals, policy makers should consider constraining leverage ratios in order to deflate financial bubbles.     

浅析中国房地产市场的风险及其控制

近几年来,我国的房地产市场出现供需两旺、房价一路攀升的过热现象。房价上涨过快、市场过热也潜伏着相当大的风险。房地产商在分享市场过热效应带来高收益的同时,也必然要承担社会政策与市场供求等方面变化带来的各种风险。因此,房地产开发企业要树立持续发展的经营理念和理性开发意识,建立合理有效的理财模式,加强资产管理和成本控制,以便于规避和控制风险。     

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.     

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.     

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.     

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.     

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model’s goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.     

Noise Trading and the Management of Operational Risk; Firms,Traders and Irrationality in Financial Markets*

abstract Efficient market models cannot explain the high level of trading in financial markets in terms of asset portfolio adjustment. It is presumed that much of this excessive trading is irrational ‘noise’ trading. A corollary is that there must either be irrational traders in the market or rational traders with irrational aberrations. The paper reviews the various attempts to explain noise trading in the finance literature, concluding that the persistence of irrationality is not well explained. Data from a study of 118 traders in four large investment banks are presented to advance reasons why traders might seek to trade more frequently than financial models predict. The argument is advanced that trades do not simply occur in order to generate profit, but it does not follow that such trading is irrational. Trading may generate information, accelerate learning, create commitments and enhance social capital, all of which sustain traders' long term survival in the market. The paper treats noise trading as a form of operational risk facing firms operating in financial markets and discusses approaches to the management of such risk.     

Trader matching and the selection of market institutions

We analyze a stochastic dynamic learning model with boundedly rational traders who can choose among trading institutions with different matching characteristics. The framework allows for institutions featuring multiple prices (per good), thus violating the “law of one price.” We find that centralized institutions are stochastically stable for a broad class of dynamics and behavioral rules, independently of which other institutions are available. However, some decentralized institutions featuring multiple prices can also survive in the long run, depending on specific characteristics of the underlying learning dynamics such as fast transitions or optimistic behavior.     

A comparison of different trading protocols in an agent-based market

We compare price dynamics of different market protocols (batch auction, continuous double auction and dealership) in an agent-based artificial exchange. In order to distinguish the effects of market architectures alone, we use a controlled environment where allocative and informational issues are neglected and agents do not optimize or learn. Hence, we rule out the possibility that the behavior of traders drives the price dynamics. Aiming to compare price stability and execution quality in broad sense, we analyze standard deviation, excess kurtosis, tail exponent of returns, volume, perceived gain by traders and bid-ask spread. Overall, a dealership market appears to be the best candidate, generating low volume and volatility, virtually no excess kurtosis and high perceived gain.     

金属期货与现货市场价格互动关联规则挖掘研究

期货市场传递的价格信息反映未来供求状况的预期,因此,研究金属期货和现货市场价格互动,对于国民经济发展、企业规避价格风险、投资者进行投资决策及政府进行市场监管都有重要意义。本文将数据挖掘中的关联规则挖掘方法引入金融时间序列分析研究领域,针对挖掘对象期货市场的特点,提出基于时间约束的时间序列关联规则挖掘算法。与传统的忽视数据时间信息的关联规则挖掘算法相比,该算法对期货价格与现货价格间的互动关联规则进行挖掘,能发现反映时间序列局部动态互动关联关系,具有一定的短期预测效果。     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

Smart predictors in the heterogeneous agent model

We extend the original heterogeneous agent model by introducing the concept of smart traders. The idea of smart traders is based on the endeavor of market agents to estimate future price movements. The main result of the simulations is that the probability distribution functions of the price deviations change significantly with an increasing number of smart traders in the model. We also find that the Hurst exponent is significantly increasing with an increasing number of smart traders in the simulations. Hence the introduction of the smart traders concept into the model results in significantly higher persistence of the simulated price deviations.     

Prospect theory and risk appetite: an application to traders’ strategies in the financial market

According to behavioral finance theories, in this article we develop a dynamic model with heterogeneous traders, where the asset price is determined by the interaction among four different groups of agents: trend reversers, trend followers, risk averters and risk seekers. The main purpose of the study is centered on modeling and testing how the market efficiency changes along with the changes of agent’s behavior preference without exogenous influence. Combining with the assumption of risk appetite and prospect theory, focusing on analyzing the rules for selecting strategies, we establish a more reliable and comprehensive dynamic mechanism. In particular, our study suggests that diversified trading strategies will help to realize market efficiency.     

The impact of reduced pre-trade transparency regimes on market quality

This paper studies the effects of pre-trade quote transparency on spread, price discovery and liquidity in an artificial limit order market with heterogeneous trading rules. Our agent-based numerical experiments suggest that full quote transparency incurs substantial transaction costs to traders and dampens trading activity in an order-driven market. Our finding reveals that exogenous restriction of displayed depth, up to several best quotes, does not benefit market performance. On the contrary, endogenous restriction of displayed quote depth, by means of iceberg orders, improves market quality in multiple dimensions: it reduces average transaction costs, maintains higher liquidity and moderate volatility, balances the limit order book, and enhances price discovery.     

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.     

后危机时代浙江艺术品拍卖市场可持续发展研究

本文从浙江艺术品拍卖市场格局着手,重点分析金融危机给浙江艺术品拍卖市场带来的恐慌、画价缩水、业务下滑等灾难性影响,深入探讨其存在的根源,提出在困境中的浙江艺术品市场迎接新机遇的根本策略。从错位经营、疯狂操作到理性回归各自市场本位;从市场多层次需求开辟新的市场方向,构建由单一到多元的经营结构;从单打独斗的粗放经营到走品牌化发展之路,逐步与国际艺术品拍卖市场接轨,实现浙江艺术品拍卖市场的可持续发展。     

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.     

基于交易者预期的房地产价格变化模型

本文基于交易者预期和正反馈,建立了一个房地产价格变动模型.根据该模型,分类讨论了交易者对房地产政策预期的不同,对房地产价格演变方向的影响.研究结果表明,房地产调控政策能否有效,除取决于政策出台时间、政策力度外,还取决于其能否有效改变交易者对市场未来走势的预期.     

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.