Informed trading, flow toxicity and the impact on intraday trading factors (original) (raw)
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Intraday Trading Activity on Financial Markets
2000
2.1. Introduction 2.2. Background and Literature Review Intraday Trading Activity on Financial Markets 8 2.3. Description of the Market and Dataset. 97 2.4 The Tick-By-Tick Relationships 99 2.5. A Tick-By-Tick Ordered Probit Model 104 2.6. Conclusion 110 2.7. Tables 113 2.8. Appendix 125 157 3.7. Tables 159 4. CONCLUSIONS 165 4.1. Intraday Market Liquidity 167 4.2. The Information Content of Order Volumes 174 4.3. Lead-Lag Relationships between Stocks and Options 177 4.4. Research Agenda 181
Intraday prices and trading volume relationship in an emerging Asian market - Hong Kong
Pacific-Basin Finance Journal, 1993
Using 15-minute data on stock returns and trading volume on ~ne of the most open markets in Asia-Hong Kong, it is found that the return series has both day-of-the-week and time-of-the-day effects while the volume series is dominated by the time-of-the-day effect. There exists a significantly positive relationship between the absolute re'urns and trading volume and the relationship is asymmetric in that the relationship is stronger for positive returns than for nonpositive ones. It is also found that returns cause volume changes unidirectionally in the sense of Granger.
Easley et al. (2002, EHO) proposed a market microstructure model to derive a measure of asymmetric information reflecting the relative intensity of informed versus uninformed (liquidity) trades, called the probability of informed trading, PIN. As described in Figure 1, the PIN model assumes that each trading day may be classified as one with news or no news. Furthermore, a day with news can be one with good news or bad news. The daily aggregate number of buyer-and seller-initiated trades (buy and sell orders) are assumed to follow independent Poisson distributions with intensities dependent on whether the trading day is one with good news, bad news or no news. In the model there are two types of traders, informed traders who trade based on relevant news or information, and uninformed traders who trade for reasons not accounted for by relevant information, such as portfolio rebalancing and liquidity needs. Let B d and S d denote the aggregate number of buy-and sell-orders on day d, respectively. In the PIN model, B d and S d are assumed to be independent Poisson random variables, with different intensities for days with bad news (B), good news (G) and no news (N). Let θ E denote the probability of news being released on day d and let θ B denote the probability of bad news, conditional on the release of news. Thus, the daily state probabilities are π B = θ E θ B , π G = θ E (1 − θ B) and π N = 1 − θ E , for a day with bad news, good news and no news, respectively. The means of B d and S d (the intensity parameters) vary according to whether the trading day is one with good news, bad news or no news. In particular, for a day with no news, the means of B d and S d are λ 1 and λ −1 , respectively. For a day with bad news the sell intensity increases by a constant δ, while the buy intensity remains the same as for a day with no news. Similarly, for a day with good news the buy intensity increases by δ, while the sell intensity stays the same as for a no-news day. The PIN model assumes that orders due to informed and uninformed traders are independent.
Journal of Financial Markets, 1999
This paper presents a study of intra-day patterns of stock market activity and introduces duration based activity measures for single stocks and multiple assets. The proposed measures involve weighted durations, i.e. times necessary to sell (buy) a predetermined volume or value of stocks. As such, they capture dependencies between intra-trade durations, transaction volumes and prices, and can be interpreted as liquidity measures. This approach allows us to highlight the intra-day variations of liquidity, its costs and volatility, and to develop a liquidity based asset ordering. The extension to a multivariate analysis yields new insights into the dynamics of portfolio liquidity by revealing various aspects of asset substitution, including the e!ects of correlated trade intensities of portfolio components. Several examples are used to show that in practice, the proposed liquidity measures become e$cient instruments for strategic block trading and optimal portfolio adjustments. The paper also contains an empirical study of asset activity on the Paris Bourse. We examine the liquidity dynamics throughout the day and reveal the existence of periodic patterns resulting from world-wide interactions of major stock markets. In the multivariate setup, we report evidence on common patterns and correlations of trade intensities of selected stocks.
Recently Duarte and Young (2009) study the probability of informed trading (PIN) proposed by Easley et al. (2002) and decompose it into two parts: the adjusted PIN (APIN) as a measure of asymmetric information and the probability of symmetric order-flow shock (PSOS) as a measure of illiquidity. They provide some cross-section estimates of these measures using daily data over annual periods. In this paper we propose a method to estimate daily APIN and PSOS by extending the method in Tay et al. (2009) using high-frequency transaction data. Our empirical results show that while PIN is positively contemporaneously correlated with variance, APIN is not. On the other hand, PSOS is positively correlated with daily average e ffective spread and variance, which is consistent with the interpretation of PSOS as a measure of illiquidity. Compared to APIN, PSOS exhibits clustering and sporadic bursts over time.
Volatility–Trading volume intraday correlation profiles and its nonstationary features
• Analysis of intraday volatility–volume matrices. • Correlations increase during the morning and dwindle aftwards. • MDH and SIAH are both relevant, but in different parts of the day. a b s t r a c t We analyse the statistical properties of volatility–volume cross-correlation matrices of stocks composing the Dow Jones Industrial Average since 2003. Using different definitions of volatility, we verify there is an intraday profile where the average values of the entries significantly increase from the opening of the trading session until its midway and it dwindles therefrom afterwards. Higher-order moments of the correlation matrix are studied and exhibit intraday profiles as well. Within the scope of the (endless) discussion ''Mixture of Distributions versus Sequential Information Arrival'' our results allow us to assert that both seem to be relevant in different parts of the business day.
Zhongguo Kuaiji yu caiwu yanjiu, 2016
In light of the controversial debate over the measure of Volume-Synchronised Probability of Informed Trading (VPIN), this paper investigates the effectiveness of VPIN as a risk warning signal in the Chinese market. Using intraday transaction data on Chinese stock index futures from 2012 to 2013, we conduct a comparative analysis of VPIN metrics using three trade classification methods: the tick rule (TR), the Lee-Ready (LR) algorithm, and bulk volume (BV). We assess the predictive ability of VPIN metrics for two highly volatile market events in China: the Money Shortage Event in June 2013 and the Fat Finger Event on 16 August 2013. Our results suggest that BV-VPIN has the best risk warning effect in signalling the occurrence of volatile events. Our work suggests that BV-VPIN can be used in the prevalent high-frequency trading (HFT) mechanism of the current financial world.
Intraday trading activities and volatility in round-the-clock futures markets
International Review of Economics & Finance, 2012
In this paper we examine the relationship between intraday return volatility and volume of trading for Japanese yen futures, euro FX futures, and E-mini S&P 500 futures traded on a 24hour GLOBEX trading system in six time zones. The results support the mixture-of-distribution hypothesis (MDH), which endorses a significant contemporaneous relationship between volume and volatility, and the sequential-arrival-of-information hypothesis (SAIH), which advocates significant lagged volatility-volume relations. The net effect of trading number is positive, supporting the dispersed belief hypothesis, while the net effect of trading imbalance is negative, supporting the asymmetrical information hypothesis. Our results suggest that the four theories of volume-volatility relations are complementary, not competing. In addition, the largest effect of the trading imbalance on volatility is found during American regular trading hours, rather than the home asset market of the futures contracts, thus supporting the trading place bias.
The Impact of Day-Trading on Volatility and Liquidity
Asia-Pacific Journal of Financial Studies, 2009
We examine day-trading activities for 540 stocks traded on the Korea Stock Exchange using transactions data for the period from 1999 to 2000. Our cross-sectional analysis reveals that day-traders prefer lower-priced, more liquid, and more volatile stocks. By estimating various bivariate VAR models using minute-by-minute data, we find that greater daytrading activity leads to greater return volatility and that the impact of a day-trading shock dissipates gradually within an hour. Past return volatility also positively affects future day-trading activity. We also find that past day-trading activity negatively affects bid-ask spreads, and past bid-ask spreads negatively affect future day-trading activity. Finally, we find that day-traders use short-term contrarian strategies and their order imbalance affects future returns positively. This result is consistent with a cyclical behavior of day-traders who concentrate their buy or sell trades at the bottom or peak of the shortterm price cycles, respectively.