投资者情绪、市场波动与股市泡沫

我国投资者情绪容易受到噪音交易者影响,其他类型交易者可利用噪音交易者的交易策略在博弈中获取超额利润,这为投机性泡沫的产生提供了微观基础。在市场波动机制中,投资者情绪与股价变化存在动态关系,股价泡沫存在内在持续性,引发市场正反馈效应,从而促成投机性泡沫的生成。     

Investor sentiment,information and asset pricing model

We present an asset pricing model with investor sentiment and information, which shows that the investor sentiment has a systematic and significant impact on the asset price. The equilibrium price's rational term drives the asset price to the rational, and the sentiment term leads to the asset price deviating from it. In our model, the proportion of sentiment investors and the information quality could amplify the sentiment shock on the asset price. Finally, the information is fully incorporated into prices when sentiment investors learn from prices. The model could offer a partial explanation of some financial anomalies: price bubbles, high volatility, asset prices' momentum effect and reversal effect.     

异质信念、投机均衡与农产品期货定价*

农产品期货市场套利并不充分,交易者也不是完全理性的。本文假设农产品期货市场有限套利、交易者异质信念并遵循“经验法则”预期,构建了农产品期货投机均衡定价模型,并认为集中竞价规则下产生的农产品期货价格是由交易者的预期决定的;前期期货价格水平、现货价格和前期期货价格的变动趋势、不同类型交易者的比例结构及其预期模式共同影响农产品期货价格的形成与波动;基本分析法交易者占主导地位的农产品期货市场具有更高的套期保值与价格发现效率。针对中国七种主要农产品期货的实证结果显示,农产品期货投机均衡定价模型对解释中国农产品期货价格的形成与波动是有效的。这意味着在期货行情系统中实时披露现货价格信息,培育和引导交易者运用基本分析法预测期货价格走势,有助于提升农产品期货市场的效率。     

Rational panics and stock market crashes

This paper offers an explanation for stock market crashes which focuses on the role of rational but uninformed traders. We show that uninformed traders can precipitate a price crash because as prices decline, they surmise that informed traders received negative information, which leads them to reduce their demand for assets and drive the price of stocks even lower. The model yields several implications, such as that crashes can occur even when the fundamentals are strong, and that the magnitude of the crash depends on the fraction of uninformed investors and the amount of unsophisticated passive investing present in the market.     

Price Limits, Information Acquisition, and Bid–ask Spreads: Theory and Evidence

A number of futures markets use price limits which, in effect, preclude trade from occurring at prices outside certain exogenous bounds. Noting that such markets are characterized by heterogeneously informed traders, whereas previous work on price limits assumes symmetrically informed traders, we examine the effects of price limits in a setting where market participants are asymmetrically informed. We find that imposing price limits generally lowers the quality of information acquired in equilibrium, but lowers bid–ask spreads as well. Thus, depending on the relative weights placed by society on liquidity versus price efficiency, there may exist a set of price limits that are most efficient in achieving a trade-off between liquidity and informational efficiency. We perform empirical tests of some implications of the model using cross-sectional data on price limits. We find that price limits are strongly negatively related to both price volatility and trading volume. Though other explanations for our empirical findings cannot be ruled out, these results are not inconsistent with the model's implication that price limits should be tighter for contracts which offer greater profit potential for informed traders.     

Two-period trading sentiment asset pricing model with information

We present a dynamic asset pricing model with investor sentiment and information, which shows that the investor sentiment plays a systematic and important role in the asset prices and the information is gradually incorporated into prices. The model has an analytical solution to the sentiment equilibrium price. We find that sentiment trading quantity not only increases the market liquidity, but also causes the asset prices' overreaction if the intensity of sentiment demand is more than a constant value. Therefore, the continuing overreactions result in a short-term momentum and a long-term reversal. The model could offer a partial explanation to some financial anomalies such as price bubbles, high volatility, asset prices' overreaction and so on.     

Price efficiency and trading behavior in limit order markets with competing insiders

We study price efficiency and trading behavior in laboratory limit order markets with asymmetrically informed traders. Markets differ in the number of insiders present and in the subset of traders who receive information about the number of insiders present. We observe that price efficiency (i) is the higher the higher the number of insiders in the market but (ii) is unaffected by changes in the subset of traders who know about the number of insiders present. (iii) Independent of the number of insiders, price efficiency increases gradually over time. (iv) The insiders’ information is reflected in prices via limit (market) orders if the asset’s value is inside (outside) the bid-ask spread. (v) In situations where limit and market orders yield positive profits, insiders clearly prefer market orders, indicating a strong desire for immediate transactions.     

Higher order expectations in sentiment asset pricing model

In the spirit of beauty contests, we study the effect of higher order expectations on sentiment asset pricing. The sentiment asset pricing model with higher order expectations shows that, in general the higher sentiment causes the higher price, but, higher order expectations contribute to smoother price path and defend the impact of sentiment. Regarding the problem of taking higher order or first order, the investors with second order can survive in a specific area where sentiment is rather optimistic or pessimistic and investors with first order expectations are the majority.     

Rock around the clock: An agent-based model of low- and high-frequency trading

We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. By contrast, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency traders increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e., their ability to i. generate high bid-ask spreads and ii. synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.     

Conservative traders,natural selection and market efficiency

This paper examines the impact of conservative traders on market efficiency in an evolutionary model of a commodity futures market. This paper shows that the long-run market outcome is informationally efficient, as long as in every period there is a positive probability that entering traders are more conservative than their predecessors. Conservative traders are those who correctly predict the spot price with a positive probability, and more importantly, who in their mistakes err on the side of caution, and rarely overpredict the spot price as buyers, and underpredict the spot price as sellers. This result does not require entry of traders with better information than their predecessors.     

A new interpretation of known facts: The case of two-way causality between trading and volatility

Efficient price setting implies that news create volatility since traders flock to the market in order to re-optimise their portfolios. In due course of the price finding process volatility should decline once the asset price approaches its new, efficient level. In this note I present evidence that the reverse mechanism plays as well. Traders genuinely increase volatility challenging the presumption that more traders help to identify the efficient price more quickly.     

Strategic noise traders and liquidity pressure with a physically deliverable futures contract

Do physically deliverable futures contracts induce liquidity pressure in the underlying spot market? The answer is believed to be no since the asset is delivered sometimes after the expiration of the contract so that the futures trader's payoff does not clearly depend on the price of the underlying stock at expiration. We construct a rational expectations equilibrium model in which a strategic uninformed trader induces liquidity pressure in the underlying spot market at the expiration of a physically deliverable futures contract. Liquidity pressure is the result of a pure informational advantage: if it is known that futures traders hedge their position in the spot market then a strategic trader with no information about the fundamental value of the underlying has an incentive to create noise in the futures market in order to gain information on the composition of the spot order flow at future auctions. We show that informed traders benefit from this form of strategic noise and that the efficiency of the prices remains unaffected.     

An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This article features an analysis of the relationship between the DOW JONES Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The recent growth in the availability of on-line financial news sources, such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. TRNA is one such data set. In this study, we use the TRNA data set to construct a series of daily sentiment scores for DJIA stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and mutual information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.     

Information,trade and common knowledge

In any voluntary trading process, if agents have rational expectations, then it is common knowledge among them that the equilibrium trade is feasible and individually rational. This condition is used to show that when risk-averse traders begin at a Pareto optimal allocation (relative to their prior beliefs) and then receive private information (which disturbs the marginal conditions), they can still never agree to any non-null trade. On markets, information is revealed by price changes. An equilibrium with fully revealing price changes always exists, and even at other equilibria the information revealed by price changes “swamps” each trader's private information.     

Emotions Matter: Sentiment and Momentum in Foreign Exchange

The author introduces news sentiment as a variable that can explain and predict subsequent changes in the USD/EUR exchange rate and therefore close a gap in the foreign exchange literature. By applying the concept of frequency filtering from the domain of electrical engineering, the author shows an innovative way of filtering for noise not only in news sentiment, but also in price momentum. The author finds that news sentiment is not correlated to price momentum, and that trading strategies based on news sentiment achieve around twice as high information ratios (up to 0.9) than with trading strategies based on price momentum.     

Commissions Matter: The Trading Behavior of Institutional and Individual Active Traders

We examine how commissions influence trading behavior by analyzing a unique data set of the equity trades of both individual and institutional active traders. Individual traders pay higher trading costs than institutional traders. As a result, they engage in more risky trading behaviors in order to cover these costs. Individual traders also trade significantly less because of their higher cost of trading. Individual traders tend to trade higher-priced stocks, hold their trades longer, and they experience much larger price swings than institutional traders. This leads individual traders to realize more dramatic gains and losses on their round-trips.     

Effects of electronic trading on the Hang Seng Index futures market

This investigation of the switch from open-outcry trading to electronic trading on the Hang Seng Index (HSI) futures contract reveals that the bid–ask spread narrows and the futures price plays more of a role in information transmission. Factors, such as anonymity in trading and fast order execution in electronic trading, attract informed traders to the futures market, enhancing the information flow. Our results provide support for the worldwide trend of transforming open-outcry markets into electronic trading platforms.     

Dynamic sentiment asset pricing model

Conventional wisdom suggests that the equilibrium stock price is not affected by investor sentiment, and the equilibrium price at an early time is higher than the one at a later time. In contrast to this wisdom, we present a dynamic asset pricing model with investor sentiment and we find that investor sentiment has a significant impact on the equilibrium stock price. The equilibrium stock price, which is affected by pessimistic sentiment at time 0, may be lower than the one at time 1. Moreover, consistent with the reality stock market, our model shows that time varying sentiments can lead to various price changes. Finally, the model could offer a partial explanation for the financial anomaly of high volatility.     

Compensation schemes,liquidity provision,and asset prices: an experimental analysis

In an experimental setting in which investors can entrust their money to traders, we investigate how compensation schemes affect liquidity provision and asset prices, two outcomes that are important for financial stability. Compensation schemes can drive a wedge between how investors and traders value the asset. Limited liability makes traders value the asset more than investors. To limit losses, investors should thus restrict liquidity provision to force traders to trade at a lower price. By contrast, bonus caps make traders value the asset less than investors. This should encourage liquidity provision and increase prices. In contrast to these predictions, we find that under limited liability investors increase liquidity provision and asset price bubbles are larger. Bonus caps have no clear effect on liquidity provision and they fail to tame bubbles. Overall, giving traders skin in the game fosters financial stability.     

Expectile CAPM

Conventional wisdom suggests that the uncertainty of uninformed noise-traders’ sentiment deters rational traders’ arbitrage activities. However, nowadays, social media have made the public sentiment highly predictable, whereas the CAPM-motivated beta-return relation still does not hold in practice. This study advances an argument that the sentiment can also be brought about by rational, sophisticated investors’ use of psychological insight; resultantly, the arbitrage activities are demotivated by their own sentiment, rather than deterred by noise-traders’ sentiment risk. The proposed expectile CAPM provides a parsimonious way to account for this claim, and leads to a sentiment-based functional form of pricing kernel.