Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis

The objective of this paper is to examine the possible linkage between the intraday stock price crashes and jumps and public information by using data from the Chinese stock market and Baidu Index. We divided public information into two kinds of information: supply through online media and information demand across inquiries by individual investors. Using a large sample from Chinese listed firms from 2013 to 2019, our evidence clearly indicates that online information supply and demand both have a positive impact on the intraday crashes and jumps; this is, the firm with higher information supply and demand more likely to experience intraday crashes and jumps. The results are robust to an alternative measure of crash risk. Moreover, we further examine whether the market conditions have an impact on the relationship between information flow and intraday crashes and jumps, and find that the marginal effect of information supply on intraday price crashes and jumps is smaller in the bull market phase. Moreover, the bull market phase enhances the effect of information demand on intraday price crashes and jumps.

     

Liquidity,liquidity risk,and information flow: Lessons from an emerging market

This paper examines the role of public and private information flows in intraday liquidity and intraday liquidity risk in the Tunisian stock market. Our empirical results are based on ARMA and GARCH-type models and show that, for major Tunisian stocks, gradually elapsed public information together with gradually elapsed private information in the market is the dominant factor in liquidity improvements in the Tunisian stock market. Liquidity improvements are generated by a decrease in the bid-ask spread accompanied by an increase in the depth at best limit. Our results clearly indicate that the arrival of public information in a sequential manner is the dominant factor generating increases in liquidity risk related to the bid-ask spread, while the advent of private information in a contemporaneous manner is the dominant factor generating increases in liquidity risk related to the depth at best limit. Additionally, our results show that liquidity risk persistence disappears when trading volume and order imbalance are included as explanatory variables in the conditional variance equation.     

Long-lived information and intraday patterns

This paper studies the effect of clustering of liquidity trades on intraday patterns of volatility and market depth when private information is long-lived. The assumption of long-lived information allows us to distinguish between the patterns of information arrival and information use. Our results are: (i) volatility follows the same pattern as liquidity trading, (ii) there are no systematic patterns in the price impacts of orders, and (iii) the timing of information arrival is unimportant. Result (i) is the same as that obtained by Admati and Pfleiderer (1988) in a model of short-lived private information, but (ii) and (iii) are different.     

The summary informativeness of stock trades: an econometric analysis

In a security market with asymmetrically informed participants,trades are signals of private information. In this article,new measures of trade informativeness are proposed based ona decomposition of the variance of changes in the efficientprice into trade-correlated and -uncorrelated components. Thetrade-correlated component has a natural interpretation as anabsolute measure of trade informativeness. The ratio of thiscomponent to the total variance is a relative measure (i.e.,a proportion normalized with respect to the total public information).For a sample of NYSE-listed companies, trades are found to bemore informative for small firms in both absolute and relativesenses. From an analysis of intraday patterns, it appears thattrades are in absolute terms more informative at the beginningof trading, but slightly less informative in relative terms.     

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.     

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.     

Information arrivals and intraday exchange rate volatility

This paper investigates the link between information arrivals and intraday DEM/$ volatility. Information arrivals are measured by the numbers of news items that appeared in the Reuters News Service. We separate news stories into different categories and find that total headline news counts, US and German macroeconomic news and German Bundesbank monetary policy news all have a significant impact on intraday DEM/$ volatility. The larger quantitative effects of the German Bundesbank monetary policy news and US macroeconomic news at 15-min intervals are consistent with the findings of a two-stage adjustment process of public information arrivals [Fleming and Remolona, J. Finance (1999) 1901]. Our results suggest that the persistent of intraday exchange rate volatility set off by public information is extended by traders’ private information about 15 min later. The conclusions are obtained from ARCH models that incorporate intraday seasonal volatility terms.     

Intraday patterns in London listed Exchange Traded Funds

In this paper we examine the intraday trading patterns of Exchange Traded Funds (ETFs) listed on the London Stock Exchange. ETFs have been shown to be characterised by much lower bid–ask spread costs and by lower levels of information asymmetry than individual securities. One possible explanation for intraday trading patterns is that concentration of trading arises at the start of the trading day because informed traders have private information that quickly diminishes in value as trading progresses. Since ETFs have lower trading costs and lower levels of information asymmetry we would expect these securities to display less pronounced intraday patterns than individual securities. We fail to find that ETFs are characterised by concentrated trading bouts during the day and therefore find support for the argument that information asymmetry is the cause of intraday volume patterns in stock markets. We find that ETF bid–ask spreads and volatility are elevated at the open but not at the close. This lends support to the “accumulation of information” explanation that sees high spreads and volatility at the open as a consequence of information accumulating during a market closure and impacting on the market when it next opens.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.     

Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation.     

Is There Private Information in the FX Market? The Tokyo Experiment

We provide evidence of private information in the foreign exchange market. The evidence comes from the introduction of trading in Tokyo over the lunch hour. Lunch-return variance doubles with the introduction of trading, which cannot be due to public information since the flow of public information did not change with the trading rules. We then exploit microstructure theory to discriminate between the two alternatives: private information and mispricing. Four key results support the predictions of private-information models. Three of these involve changes in the intraday volatility U-shape. The fourth is that opening trade causes mispricing's share in variance to fall.     

Trading European Central Bank rumours on the EUR-USD exchange rate market

This paper investigates whether the release of market-relevant news in the form of rumours on Twitter can explain the excess of market volatility previously attributed to private information, speculation, and noise traders. We define a simple theoretical model to show that the systematic information content of such rumours should result in detectable price effects in macro-markets. We then pinpoint the arrival of 63 rumours of forthcoming ECB actions over a 420-day sample of one-minute spot EUR-USD rates, and show that there is a real-time, intraday increase in market volatility. This largely unexplored information set can potentially account for significant amounts of unexplained volatility in macro-markets and, therefore, identify a possible explanation of one of the most prominent puzzles in price discovery research.     

Stock price reaction to public and private information

I use Easley and O’Hara's [1992, Journal of Finance 47, 577–604] private information-based trading variable, PIN, together with a comprehensive public news database to empirically measure the effect of private and public information on the post-announcement drift. I show that stocks associated with high PIN, consensus public news surprises, and low media coverage experience low or insignificant drift. Thus not all information acquisition variables have the same effect on the market's efficiency. Whether information is public or private is irrelevant; what matters is whether information is associated with the arrival rate of informed or uninformed traders.     

The intraday multivariate structure of the Eurofutures markets

We investigate the multivariate intraday structure in interest rates, focusing on implied forward rates from Eurofutures contracts. Since futures markets are the most liquid for interest rate instruments and they yield high-quality intraday data, it is somehow surprising that their intraday behavior has not been thoroughly studied in the literature.We find interesting similarities with the foreign exchange market: scaling law, intraday patterns, all of which point to the heterogeneity of market participants. Other properties like asymmetric causal information flow between fine and coarse volatilities for the same time series are present in our data. There are also lead–lag correlations across the term structure of implied forward rates, but they tend to disappear as markets mature.A principal component analysis of the short end of the yield curve allows us to determine the most important components and to reduce the number of time series needed to describe the term structure. We find the decomposition rather stable over time. The first component, which describes the curve level, shows an asymmetry in the information flow between volatilities of different time resolution, i.e., the coarse-grained volatility predicts the fine-grained volatility better than the other way around, as observed in the foreign exchange market. The remaining components do not show such an effect, having instead significant negative autocorrelations for the time series themselves. A heterogeneous autoregressive conditional heteroskedasticity (HARCH) model is estimated for the first component and the impact of different market agents is discussed.     

Stale Information,Shocks, and Volatility

We propose a new approach to measuring the effect of unobservable private information on volatility. Using intraday data, we estimate the effect of a well‐identified shock on the volatility of stock returns of European banks as a function of the quality of public information available about the banks. We hypothesize that as publicly available information becomes stale, volatility effects and its persistence increase, as private information of investors becomes more important. We find strong support for this idea in the data. We further show that stock volatility is higher just before important announcements if information is stale.     

Pretrial negotiations under optimism

We develop a tractable and versatile model of pretrial negotiation in which the negotiating parties are optimistic about the judge's decision and anticipate the possible arrival of public information about the case prior to the trial date. The parties will settle immediately upon the arrival of information. However, they may also agree to settle prior to an arrival. We derive the settlement dynamics prior to an arrival: negotiations result in either immediate agreement, a weak deadline effect—settling at a particular date before the deadline, a strong deadline effect—settling at the deadline, or impasse, depending on the level of optimism. Our findings match stylized facts.     

An Analysis of Transactions Data for the Stock Exchange of Singapore: Patterns, Absolute Price Change, Trade Size and Number of Transactions

The availability of the transactions data of the Stock Exchange of Singapore allows us to examine intraday patterns and the relation among absolute price change, trade size and number of transactions. The presence of a trading halt in the mid-day results in two crude U-shaped return patterns but, contrary to Brock and Kleidon's (1992) model, it does not cause volume to be unusually high right before or after the halt. We find a positive relationship between absolute price changes and the number of transactions for both the active and inactive stocks. This supports the findings of Jones, Kaul and Lipson (1994) that these relationships also hold at the intraday level and in a market with different market architecture.     

ANALYSIS OF FEDERAL FUNDS RATE CHANGES AND VARIANCE PATTERNS

We analyze Fed funds rate changes in GARCH‐in‐mean (GARCH‐M) models and find that daily rate change and variance patterns differ with the timing of the rate observation, but that all patterns are generally consistent with optimal reserve account management. We also find that Fed funds daily and intraday variances exhibit trends and persistence, and that daily variance effects differ when using marginal rates versus daily weighted average rates. Furthermore, we find that conditional variances do not provide information about daily or intraday rate changes. Our results provide support for the use of GARCH models for studies on other financial assets. JEL classification: G21, G28     

Does Greater Market Transparency Reduce Information Asymmetry?

This research aims to determine whether the degree of asymmetric information decreases with greater pre-trade transparency in the Taiwan stock market. We used the probability of informed trading based on the Markov regime-switching model in an order-driven auction market to investigate this topic. Information asymmetry showed no conspicuous variations with greater transparency. However, after further grouping, the empirical results revealed that increased transparency facilitated a decrease in information asymmetry in the sub-samples, which originally exhibited greater information asymmetry. In addition, the intraday patterns of probability of informed trading revealed that greater transparency facilitates decreased market information asymmetry after opening.