The Intraday Pattern of Trading Activity, Return Volatility and Liquidity: Evidence from the Emerging Tunisian Stock Exchange (original) (raw)
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2003
Abstract: This paper seeks whether the intraday patterns observed in most stock markets hold for an individual stock from the Istanbul Stock Exchange where the trading ceases for two hours during lunchtime. It investigates the most treated topics in market finance like liquidity, returns and volatility through their various indicators by periodically regrouping transaction data and making use of parametric and nonparametric tests. It excludes the analysis of spread due to its uninformativeness in the presence of very discrete prices. The results show that liquidity-related variables ’ path can be described by an asymmetric “W”curve, namely a “reverse J ” curve in the morning session and a “U ” curve in the afternoon session. While returns do not exhibit a precise path, overnight returns are apparent. Price and return volatilities are very high in level and stable during the day. Sensitivity analysis suggests that choice of 5-minute versus 15-minute intervals slightly matters.
International Business Research
The purpose of this paper is to study the relationship between trading volume and unconditional price volatility on the Tunisian Stock Market in order to provide an empirical support to either the hypothesis of the strategic asymmetric information models or the hypothesis of competitive asymmetric information Models. More specifically, it aims to test the volume-volatility relationship and identify the component of trading volume (number of transactions or trade size) that explains more price volatility and drives this relationship. Our empirical tests are based on daily and intraday data related to the 43 most active and dynamic listed stocks on the
The pattern of intraday liquidity in emerging markets: The case of the Amman Stock Exchange
asmda.com, 2009
This study provides an empirical analysis of several intraday liquidity dynamics for stocks listed in Amman Stock Exchange (ASE) using transaction data for the period from 1/1/2005 to 31/8/2005. We used a cross-market index, which is composed of 37 stocks, to estimate different liquidity proxies. Then these liquidity proxies are represented graphically to check for intraday commonalities. The analysis demonstrates that volume measures; bid-ask spread, instant trades and number of large trades exhibit a U-shape, liquidity ratio has a smooth L-shape, while waiting time to trade exhibits an inverse U-shape, the same results are observed for the case of an individual stock. The results reveals that ASE highest activity levels are at market open and close, whereas it is least active between 11:20 and 11:35 am, suggesting a possible high information asymmetry level at opens and intensive large traders' activities at close.
We study the intraday behaviour of the statistical moments of the trading volume of the blue chip equities that composed the Dow Jones Industrial Average index between 2003 and 2014. By splitting that time interval into semesters, we provide a quantitative account of the nonstationary nature of the intraday statistical properties as well. Explicitly, we prove the well-known [-shape exhibited by the average trading volume—as well as the volatility of the price fluctuations—experienced a significant change from 2008 (the year of the " sub-prime " financial crisis) onwards. That has resulted in a faster relaxation after the market opening and relates to a consistent decrease in the convexity of the average trading volume intraday profile. Simultaneously, the last part of the session has become steeper as well, a modification that is likely to have been triggered by the new short-selling rules that were introduced in 2007 by the Securities and Exchange Commission. The combination of both results reveals that the [ has been turning into a t. Additionally, the analysis of higher-order cumulants—namely the skewness and the kurtosis—shows that the morning and the afternoon parts of the trading session are each clearly associated with different statistical features and hence dynamical rules. Concretely, we claim that the large initial trading volume is due to wayward stocks whereas the large volume during the last part of the session hinges on a cohesive increase of the trading volume. That dissimilarity between the two parts of the trading session is stressed in periods of higher uproar in the market.
Trading Volume And Volatility In The Boursa Kuwait
2017
This paper presents the results of a study of the effect of daily trading volume on the persistence of timevarying conditional volatility for Kuwait Stock Exchange. The sample includes the market index, seven sectoral indices and 20 stocks. Whereas inclusion of contemporaneous volume in the equation of conditional variance does not reduce the persistence of volatility for the eight indices, this is not the case for individual companies. Furthermore, the lagged intraday volatility has higher predictive power for volatility than the lagged trading volume. These results lend further support to the mixture of distribution hypothesis at the level of firm, but not at the market and sectoral levels.
Measures, Determinants and Commonality in Liquidity: Empirical Tests on Tunisian Stock Market
2015
This paper examine empirically variables that can be significantly correlated with inter-temporal changes of measures of the individual’s securities, for example: trading volumes, number of transactions, return, volatility, arrival of new information etc. Before a study of a sample of 40 quoted securities in Tunisian financial market, on the period of February 07, 2011 until January 31, 2013, results appear conclusive. First, as expected, depth has negative correlation with all spread measures. Besides, we observe perfect positive correlations between spread measures. This shows the validity of these liquidity measures on the Tunisian stock market. Furthermore, the results suggest that volume, return and arrival of new information contribute to explain significantly the inter-temporal changes of various measures of the securities liquidity. Finally, we can consider, probably, the arrival of new information as a common factor for the different liquidity measures for all stocks in our...
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.
Effects of Intraday Patterns on Analysis of Stock Market Index and Trading Volume
International Journal of Modern Physics: Conference Series, 2012
We review the stylized properties of the stock market and consider effects of the intraday patterns on the analysis of the time series for the stock index and the trading volume in Korean stock market. In the stock market the probability distribution function (pdf) of the return and volatility followed the power law for the stock index and the change of the volume traded. The volatility of the stock index showed the long-time memory and the autocorrelation function followed a power law. We applied two eliminating methods of the intraday patterns: the intraday patterns of the time series itself, and the intraday patterns of the absolute return for the index or the absolute volume change. We scaled the index and return by two types of the intraday patterns. We considered the probability distribution function and the autocorrelation function (ACF) for the time series scaled by the intraday patterns. The cumulative probability distribution function of the returns scaled by the intraday ...
Volatility Analysis in Different Intraday Time Frequencies: An Empirical Investigation
International Journal of Management Studies
Volatility of Futures market study is one of the most discussed and empirically explored area of stock market research across academicians, researchers and financial analysts. Many researchers have analysed the positive volatility-volume relationship and the effect of decomposed components of volume (number of transactions and average trade size) in different markets on volatility. In this study we investigate the effect of number of transactions and trade size on volatility of S&P CNX Nifty futures index using high frequency data. Three different intraday time frequencies, 1, 15 and 30 minutes have been used for the purpose. The data is sourced from NSE (National Stock Exchange). GARCH model is found to be appropriate to explain the intraday volatility behaviour. The empirical results reveal that number of trades contains more information and has more impact than trade size on volatility and different time frequency are also able to show interesting facts explaining intraday volatility. The study contributes much relevance to the investors and researchers to analyse the volatility behaviour and markets in taking appropriate investment and further research decisions respectively.