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.     

Characteristics of the order flow through an electronic open limit order book

We estimate and examine certain characteristics of the order flow through an electronic open limit order book, using order (not trade) data. In doing this, we bring out new evidence on order flow from a market with microstructure different from that of the NYSE. We find that the proportion of informed orders is less than 10%, lower than previous estimates. Informed traders choose smaller orders than uninformed traders, but do not materially differ in their choice of limit or market orders. The proportion of informed investors is similar between good and bad news days. Finally, there are U-shaped intraday patterns in order arrival, and the information content of the order flow appears to follow this pattern across the day.     

Whose trades convey information? Evidence from a cross-section of traders

This paper contributes empirically to our understanding of informed traders. It analyzes traders’ characteristics in a foreign exchange electronic limit order market via anonymous trader identities. We use six indicators of informed trading in a cross-sectional multivariate approach to identify traders with high price impact. More information is conveyed by those traders’ trades which—simultaneously—use medium-sized orders (practice stealth trading), have large trading volume, are located in a financial center, trade early in the trading session, at times of wide spreads and when the order book is thin.     

Price limits in futures markets: effects on the price discovery process and volatility

Price limits are actively employed by many futures exchanges as a regulatory mechanism directed at reducing volatility and improving price discovery process. The aim of this paper is to investigate whether price limits achieve these goals without affecting market liquidity for a number of agricultural futures contracts. We employ models of changing volatility in order to show that price limits do not appear to significantly reduce market volatility. In addition, we find evidence confirming the hypothesis that price limits delay price discovery instead of facilitating it. Our results also suggest that the impact of price limits on volatility and price reversals, found in previous studies, are mainly due to the properties inherent to the futures returns, such as volatility clustering. Finally, although trading decreases significantly due to the price limits, traders do not seem to switch from the contracts affected by price limits to other maturities in order to minimize the impact of circuit breakers.     

Informed trading and the price impact of block trades: A high frequency trading analysis

Using high frequency data from the London Stock Exchange (LSE), we investigate the relationship between informed trading and the price impact of block trades on intraday and inter-day basis. Price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery.     

The informativeness of pro forma and street earnings: an examination of information asymmetry around earnings announcements

Using adverse-selection cost as a proxy for information asymmetry, we find evidence that non-GAAP earnings numbers issued by management (pro forma earnings) and analysts (street earnings) improve price discovery. First, information asymmetry before an earnings announcement is positively associated with the probability of a non-GAAP earnings number at the forthcoming earnings announcement. Second, the post-announcement reduction in information asymmetry is greater when managers or analysts issue non-GAAP earnings at the earnings announcement and when the magnitude of the non-GAAP earnings adjustment is larger. Our results suggest that earnings adjustments by analysts and managers increase the amount and precision of earnings information and help to narrow information asymmetry between informed and uninformed traders following earnings announcements. Alternatively, the findings may be attributable to characteristics of non-GAAP firms and overall better reporting quality for those firms rather than non-GAAP earnings disclosure per se.     

The “make or take” decision in an electronic market: Evidence on the evolution of liquidity

This paper uses experimental asset markets to investigate the evolution of liquidity in an electronic limit order market. Our market setting includes salient features of electronic limit order markets, as well as informed traders and liquidity traders. We focus on the strategies of the traders and how these are affected by trader type, characteristics of the market, and characteristics of the asset. We find that informed traders use more limit orders than do liquidity traders. Our main result is that liquidity provision shifts as trading progresses, with informed traders increasingly providing liquidity in markets. The change in the behavior of the informed traders seems to be in response to the dynamic adjustment of prices to information; they take (provide) liquidity when the value of their information is high (low). Thus, a market-making role emerges endogenously in our electronic markets and is ultimately adopted by the traders who are least subject to adverse selection when placing limit orders.     

Public information arrival: Price discovery and liquidity in electronic limit order markets

How information is translated into market prices is still an open question. This paper studies the impact of newswire messages on intraday price discovery, liquidity, and trading intensity in an electronic limit order market. We take an objective ex ante measure of the tone of a message to study the impacts of positive, negative, and neutral messages on price discovery and trading activity. As expected, we find higher adverse selection costs around the arrival of newswire messages. Negative messages are associated with higher adverse selection costs than positive or neutral messages. Liquidity increases around positive and neutral messages and decreases around negative messages. Available order book depth as well as the trading intensity increases around all news. Our results suggest that market participants possess different information gathering and processing capabilities and that negative news messages are particularly informative and induce stronger market reactions.     

Noise trading and the price formation process

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.     

The impact of short-selling and margin-buying on liquidity: Evidence from the Chinese stock market

We propose a framework based on limit order book to analyze the impact of short-selling and margin-buying on liquidity. We show that when short-sellers are perceived as informed, adverse selection may lead to uninformed traders withdrawing their limit orders. Given that the Chinese stock market has strong information asymmetry and a high proportion of uninformed traders, we predict that the pilot program launched in March 2010, which lifts restrictions on short-selling and margin-buying for a designated list of stocks, may have a negative impact on liquidity. We perform difference-in-differences tests and show evidence that allowing for short-selling and margin-buying indeed has a significantly negative impact on liquidity for stocks on the designated list. In particular, the negative impact on liquidity is more pronounced for stocks with high information asymmetry. Nevertheless, when short-selling volume dries up due to regulation changes in August 2015, i.e., the “T+1” trading rule on short-selling, we show that consistent with model predictions, lifting restrictions on short-selling and margin-buying has a positive effect on liquidity.     

Price Discovery without Trading: Evidence from Limit Orders

We analyze the contribution to price discovery of market and limit orders by high‐frequency traders (HFTs) and non‐HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders. HFTs submit the bulk of limit orders and these limit orders provide most of the price discovery. Submissions of limit orders and their contribution to price discovery fall with volatility due to changes in HFTs’ behavior. Consistent with adverse selection arising from faster reactions to public information, HFTs’ informational advantage is partially explained by public information.     

Herd mentality in the stock market: On the role of idiosyncratic participants with heterogeneous information

This paper examines herd behaviour using aggregate market data for stocks, with a focus on the role of idiosyncratic participants with heterogeneous information. We look at herding asymmetry between up and down markets, taking into consideration the daily price limits and the impact of the recent financial crisis. We also improve upon existing tests for fundamental and non-fundamental herding, as well as proposing a method for investigating herd behaviour of different groups of investors. Empirical evidence based on the Ho Chi Minh Stock Exchange in Vietnam reveals a greater level of herding on up compared to down market days, and a significant reduction in the magnitude of herding following the crisis. We document robust intentional herding even when unintentional (fundamental) herding is factored out. Our empirical results also uncover potential within-group herding and between-group interactions among arbitrageurs and noise traders in the market.     

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.     

Arbitrage Chains

A privately informed trader will engage in costly arbitrage, that is, trade on his knowledge that the price of an asset is different from the fundamental value if: (1) his order does not move the price immediately to reflect the information; and (2) he can hold the asset until the date when the information is reflected in the price. We study a general equilibrium model in which all agents optimize. In each period, there may be a trader with a limited horizon who has private information about a distant event. Whether he acts on his information, and whether subsequent informed traders act, is shown to depend on the possibility of a sequence or chain of future informed traders spanning the event date. An arbitrageur who receives good news will buy only if it is likely that, at the end of his trading horizon, a subsequent arbitrageur's buying will have pushed up the expected price. We show that limited trading horizons result in inefficient prices, because informed traders do not act on their information until the event date is sufficiently close. We also show that limited horizons can arise because of the cost-carry associated with holding an arbitrage portfolio over an extended period of time.     

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.     

Macroeconomic announcements and price discovery in the foreign exchange market

This study shows that the dominance of the overlapping trading hours of London and New York in the price discovery of the EUR/USD and USD/JPY markets only applies on days with U.S. announcements. Different from Cai et al. (2008) and Wang and Yang (2011), we highlight the informational advantage of local traders at the arrival of macroeconomic announcements in the local market, and find that macroeconomic announcements affect the pattern of price discovery across different markets, consistent with Chen and Gau (2010) and Jiang et al. (2012). We also examine changes in information shares before and after the announcement. A significant increase in price discovery before the announcement suggests the possibility of information leakage, while enhanced price discovery efficacy after the announcement suggests that prices gradually adjust to new information, not just immediately respond to the arrival of announcements.     

Modelling the buy and sell intensity in a limit order book market

In this paper, we model the buy and sell arrival process in the limit order book market at the Australian Stock Exchange. Using a bivariate autoregressive intensity model we analyze the contemporaneous buy and sell intensity as a function of the state of the market. We find evidence that trading decisions are both information as well as liquidity driven. Confirming predictions from market microstructure theory traders submit market orders by inferring from the recent order flow and the book with respect to upper and lower tail expectations as well as trading directions. However, traders also tend to take liquidity when the liquidity supply is high. Moreover, we find evidence that traders pay more attention to recent order arrivals and the current state of the order book than to the past order flow.     

Monitoring and limit order submission risks

This paper presents a formal analysis of the relation between monitoring and limit order submission risk. With heterogeneous information, limit order traders face two types of risk. First, they may be “picked off” when prices change unexpectedly after the limit order is entered (known as free trading option risk). Second, they face the possibility that their limit order will not result in a trade. To mitigate these risks, traders can monitor information and prices and cancel or revise their orders as needed. But such monitoring is costly, resulting in a trade-off between the cost of monitoring and the risks of limit order submission. The model predicts that if the stock is actively traded, limit order submission risks and order cancellations/revisions are positively related. Further, shares with a wide bid-ask spread will tend to have a lower rate of order cancellations and revisions than shares with small bid-ask spreads. Finally, the model suggests that if larger capitalization stocks have lower costs of gathering information (and hence more intense monitoring of limit orders), there will be more cancellations and revisions in limit orders. A sample of 23 liquid stocks provides evidence that is consistent with these three main hypotheses.     

The Dynamics of Security Trades,Quote Revisions,and Market Depths for Actively Traded Stocks

We examine the dynamics of return volatility, trading volume, and depth—in an intraday setting for a sample of actively traded NYSE and NASDAQ stocks. We show that depth is a useful intervening variable and mitigates the impact of trading activity on price volatility. Furthermore, depth is affected by the perception of prevailing information asymmetry between informed and uninformed traders. We demonstrate empirically that the NYSE supplies greater depth under conditions of high, perceived information asymmetry as compared to NASDAQ. NASDAQ makes up for this deficiency by its capability of managing large volume shocks without a major decline in depth.JEL Classification: C32, D82, G10     

Momentum,Reversal, and Uninformed Traders in Laboratory Markets

We report the results of three experiments based on the model of Hong and Stein (1999) . Consistent with the model, the results show that when informed traders do not observe prices, uninformed traders generate long‐term price reversals by engaging in momentum trade. However, when informed traders also observe prices, uninformed traders generate reversals by engaging in contrarian trading. The results suggest that a dominated information set is sufficient to account for the contrarian behavior observed among individual investors, and that uninformed traders may be responsible for long‐term price reversals but play little role in driving short‐term momentum.