Asymmetric information and the bid-ask spread: an empirical comparison between automated order execution and open outcry auction

This study examines the components of the bid-ask spread on the Sydney Futures Exchange. The Exchange uses open outcry auction for daytime trading, and switches to a screen-based automated order execution system at 16:30 h for overnight trading. After controlling for proxies of order flow characteristics, the study finds that screen-based traders are more sensitive to market volatility than floor-based traders in setting the bid-ask spread. Spreads from floor trading have a smaller adverse information component but a larger order processing cost component relative to screen trading. The results suggest that floor traders can better assess the presence of adverse information than screen traders.     

Information diffusion in electronic and floor trading

The attractiveness of floor trading versus anonymous electronic trading systems for traders is analysed. We hypothesize that in times of low information intensity, the insight into the order book of the electronic trading system provides more valuable information than floor trading, but in times of high information intensity, this is not true. Thus, the electronic system's market share in trading volume should decline when information intensity increases. This hypothesis is tested by DTB and LIFFE data on Bund-Future trading in the period 1991 to 1995. In the first years of trading, the DTB's market share is inversely related to price volatility and trading volume as proxies for information intensity. In recent years, this relation fades away; this can be explained by the high frequency of transactions which implies a steady flow of information on transactions.     

Does information drive trading in option strategies?

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.     

Information, Trading Demand, and Futures Price Volatility

We examine the relation between futures price volatility and trading demand by type of trader in the Standard & Poor's (S&P) 500-stock index futures market. We find that volatility covaries negatively with signed speculative demand shocks but is positively related to signed hedging demand shocks. No significant relation between volatility and demand shocks for small traders is found. Our results suggest that changes in positions of large hedgers destabilize the market, whereas changes in positions of large speculators stabilize volatility. Consistent with models with asymmetrically informed traders, we find that large speculators are likely to possess superior forecasting ability, large hedgers behave like positive feedback traders, and small traders are liquidity traders.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.     

Transactions, volume, and volatility

We show that the positive volatility-volume relation documentedby numerous researchers actually reflects the positive relationbetween volatility and the number of transactions. Thus, itis the occurrence of transactions per se, and not their size,that generates volatility; trade size has no information beyondthat contained in the frequency of transactions. Our resultssuggest that theoretical research needs to entertain scenariosin which (i) both the frequency and size of trades are endogenouslydetermined, yet (ii) the size of trades has no information contentbeyond that contained in the number of transactions.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

Dealer attention,the speed of quote adjustment to information,and net dealer revenue

Using trade and quote data from the NYSE, we examine the relation between dealer attention, dealer revenue, and the probability of informed trade. We find that dealer revenue net of losses to better-informed traders in NYSE stocks is positively related to the speed at which quotes adjust to full information levels. The speed of quote adjustment is faster for stocks with greater dealer attention, as measured by a stock’s relative prominence at its post and panel location on the NYSE floor. The level of dealer attention in turn is positively related to a stock’s probability of information-based trading. The results are consistent with a theoretical model we derive in which dealers trade multiple securities and must optimally allocate their limited attention to monitoring order flow to minimize losses to better-informed traders.     

A behavioral explanation for the negative asymmetric return–volatility relation

We examine the short-term dynamic relation between the S&P 500 (Nasdaq 100) index return and changes in implied volatility at both the daily and intraday level. Neither the leverage hypothesis nor the volatility feedback hypothesis adequately explains the results. Alternatively, we propose that the behavior of traders (from the representativeness, affect, and extrapolation bias concepts of behavioral finance) is consistent with our empirical results of a strong daily and intraday negative return–implied volatility relation. Moreover, both the presence and magnitude of the negative relation and the asymmetry between return and implied volatility are most closely associated with extreme changes in the index returns. We also show that the strength of the relation is consistent with the implied volatility skew.     

Informed traders and limit order markets

We consider a dynamic limit order market in which traders optimally choose whether to acquire information about the asset and the type of order to submit. We numerically solve for the equilibrium and demonstrate that the market is a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This effect increases when the fundamental value has high volatility and with asymmetric information across traders. Changes in the microstructure noise are negatively correlated with changes in the estimated fundamental value, implying that asset betas estimated from high-frequency data will be incorrect.     

Working Orders in Limit Order Markets and Floor Exchanges

We analyze limit order markets and floor exchanges, assuming an informed trader and discretionary liquidity traders use market orders and can either submit block orders or work their demands as a series of small orders. By working their demands, large market order traders pool with small traders. We show that every equilibrium on a floor exchange must involve at least partial pooling. Moreover, there is always a fully pooling (worked order) equilibrium on a floor exchange that is equivalent to a block order equilibrium in a limit order market.     

Learning from others,reacting, and market quality

Traders pay attention to one another but are unable to perfectly deduce each others’ beliefs from transactions alone. This explains why markets are hard to beat and also why trading occurs at all. Even when traders react rationally to the actions of others, they cannot arrive easily at a common posterior assessment of value. We model a realistic market composed of traders who combine their own private information with rational learning about the information possessed by others. We compare phenomena in this market with an otherwise identical market populated by traders who receive the same private information but ignore other traders. Using simulation to engender greater realism, we find that learning usually reduces volatility, increases the accuracy of the market price as a forecast of value, reduces trading volume, and decreases the prevalence of bubbles. However, for some combinations of market conditions, learning can have the opposite effect. The marginal influences of eight different market conditions, ranging from information heterogeneity through resource diversity, are estimated. Prices, volatility, volume, and bubbles exhibit subtle and complex responses to market conditions.     

Informed trading and order type

Each trader must choose between a limit order, a market order, or using a floor broker. We hypothesize that informed investors will: (1) concentrate their trading in floor broker orders and (2) sometimes trade patiently. Consistent with our hypotheses, empirical results suggest that most informed trading occurs through orders executed by floor brokers and that informed floor brokers are sometimes patient. Regardless of their patience, however, quote revisions following trade executions are consistent with the hypothesis that markets recognize that floor traders are more likely to be informed than other traders. As a result, informed trading moves equilibrium security values.     

Investor Sentiment,Trading Behavior and Informational Efficiency in Index Futures Markets

This paper shows that traders in index futures markets are positive feedback traders—they buy when prices increase and sell when prices decline. Positive feedback trading appears to be more active in periods of high investor sentiment. This finding is consistent with the notion that feedback trading is driven by expectations of noise traders. Consistent with the noise trading hypothesis, order flow in index futures markets is less informative when investors are optimistic. Transitory volatility measured at high frequencies also appears to decline in periods of bullish sentiment, suggesting that sentiment‐driven trading increases market liquidity.     

Determinants of bid and ask quotes and implications for the cost of trading

Financial transaction costs are time varying. This paper proposes a model that relates transaction cost to characteristics of order flow. We obtain qualitatively consistent model results for different stocks and across different time periods. We find that an unusual excess of buyers (sellers) relative to sellers (buyers) tends to increase the ask (bid) price. Hence, the ask and bid components of spread change asymmetrically about the efficient price. For a fixed order imbalance surprise these effects are muted when unanticipated total volume is high. Unexpected high volatility in the transaction price process tends to widen the spread symmetrically about the efficient price. Our findings are consistent with predications from market microstructure theory that the cost of market making should depend on both the risk of trading with better-informed traders and inventory risk. We also find that order flow surprises have a significant impact on the efficient price and can also explain a substantial amount of persistence in the volatility of the efficient price. This dependence does not violate the efficient market hypothesis since the surprises, by definition, are not predictable.     

The value of the floor

Existing theoretical literature suggests that floor trading has discernable benefits over electronic trading. In particular floor relationships lead to a reduction in asymmetric information and hence lower spreads. The ability of floor brokers to participate in incoming order flow without revealing their supply and demand curves increases total liquidity and dampens liquidity shocks leading to lower volatility. We develop hypotheses and test them on a sample of stocks that switch from floor trading to an electronic system with fairly identical rules and pre-trade transparency. We find strong support for existing theory and our hypotheses. In particular asymmetric information and volatility are significantly higher on the electronic system. This leads to an increase in investor transaction costs which dwarfs the operational cost advantages of the electronic systems. Our results are robust to tests involving samples that control for company specific factors and market wide trends.     

The price discovery role of day traders in futures market: Evidence from different types of day traders

Using proprietary account-level transaction data in the futures market where day traders are self-declared ex ante, this study investigates whether day traders enhance price discovery at the market level. From a natural classification of day traders, we find that heterogeneous day traders have differential effects on price discovery. Self-declared day traders, who benefit from low margin requirement, do not improve price discovery measured by information share. In contrast, non-declared traders, who are not self-declared as day traders, improve price discovery. Their positive impacts on price discovery are particularly significant during periods of high volatility and arrival of new information. Overall, a margin stimulating policy may encourage more day trading, but may also attract overconfident investors, especially inexperienced ones, and who do not enhance price discovery.     

Does Anonymity Matter in Electronic Limit Order Markets?

We develop a model in which limit order traders possess volatilityinformation. We show that in this case the size of the bid–askspread is informative about future volatility. Moreover, ifvolatility information is in part private, we establish that(i) the size of the bid–ask spread and (ii) its informativenessabout future volatility should change in the same directionwhen limit order traders' identifiers stop being disclosed.We test these predictions using data from the Paris Bourse.As expected, we find that the average quoted spread and itsinformativeness are significantly smaller when limit order traders'identifiers are concealed. These findings suggest that the limitorder book is a channel for volatility information.     

期货市场的交易量与价格波动

本文检验了美国期货市场WTI原油、S＆P500指数和10年期国债品种的日内、日间价格波动与日内交易量、隔日交易量之间的关系,发现预期的日内和隔日交易量都有平抑期货市场价格波动的作用,非预期的隔日交易量与期货价格波动之间有正相关关系,非预期的目内交易量对价格波动的影响不显著。从信息对称性的角度分析,预期的交易量中含有更多信息,能抑制期货价格的偏离;非预期的交易量主要由信息反馈者提供,他们往往对期货价格的变动做出过度反应,从而加剧价格波动。     

WHO ARE THE NOISE TRADERS?

Closed‐end funds often trade at a discount to net asset value. Previous research suggests that the positive correlation in discounts is associated with investor sentiment that causes systematic mispricing by noise traders. We use a newly available sample of daily fund valuations to examine the relation between intraday trading activity and discount changes. Contrary to the assumption that retail investors are noise traders, we find no relation between discount changes and the order‐flow imbalances of individual investors. Large daily discount changes are associated with institutional trading, and this may reflect the price inelasticity of closed‐end fund shares.