Trade Size Preference of Informed Traders in Indian Equity Markets

This study utilized high frequency transactions data to analyze the trade size preference of informed traders in Indian equity markets. It is observed that informed traders at an aggregate level adopt stealth trading strategy, wherein they prefer medium sized trades over large sized trades in order to camouflage their private information. However, the stealth trading behavior varies across stocks, wherein informed traders prefer more large sized trades on firms that are part of an index compared to non-index firms. Trading behavior also varies across other market conditions. It has been noted that informed traders prefer large sized trades during periods of high market thickness, negative returns, and low volatility. This study also provides a rationale for such varied behavior of informed traders.     

Dynamic Relationship among Intraday Realized Volatility,Volume and Number of Trades

In this paper, the vector autoregressive model is fitted to find out the causal relationship among realized volatility, the number of transactions and volume with the intraday time intervals of 10, 20 and 30 min. To understand the impact of shock to the market on specific variables, a multivariate Impulse Response Function analysis is also introduced to visualize the causal relationship among the variables. From the analysis of a stock listed on the Stock Exchange of Hong Kong, we find that realized volatility reacts positively to the lagged average trade size. However, the realized volatility forms a negative relationship with the first few lagged number of trades. As a result, the intraday causal relationship among realized volatility, volume and the number of trades is quite different from that obtained on a daily basis. The findings of this paper can enhance the understanding of how the number of trades and the average trade size per transaction affect the risk evolution of financial securities and thus improve the risk management of day trading strategies.     

Trading and Liquidity on the Tokyo Stock Exchange: A Bird's Eye View

The trading mechanism for equities on the Tokyo Stock Exchange (TSE) stands in sharp contrast to the primary mechanisms used to trade stocks in the United States. In the United States, exchange-designated specialists have affirmative obligations to provide continuous liquidity to the market. Specialists offer simultaneous and tight quotes to both buy and sell and supply sufficient liquidity to limit the magnitude of price changes between consecutive transactions. In contradistinction, the TSE has no exchange-designated liquidity suppliers. Instead, liquidity is provided through a public limit order book, and liquidity is organized through restrictions on maximum price changes between trades that serve to slow down trading. In this article, we examine the efficacy of the TSE's trading mechanisms at providing liquidity. Our analysis is based on a complete record of transactions and best-bid and best-offer quotes for most stocks in the First Section of the TSE over a period of 26 months. We study the size of the bid-ask spread and its cross-sectional and intertemporal stability; intertemporal patterns in returns, volatility, volume, trade size, and the frequency of trades; and market depth based on the response of quotes to trades and the frequency of trading halts and warning quotes.     

Time and dynamic volume–volatility relation

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.     

Return Volatility,Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.     

Public re-release of going-concern opinions and market reaction

This paper examines the market reaction to the public announcement of going-concern (GC) opinions through the news media. In the early 2000s, NASDAQ and AMEX required firms listed on their exchanges to publicly announce previously disclosed information, such as the issuance of a GC opinion, through a press release or the news media. We examine the stock market reaction to the re-release of GC opinions. We find significant abnormal stock return volatility and trading volume at the re-release of this information. Further, based on an analysis of intraday transactions, we find higher abnormal trading activity in small trades around the re-release of the GC opinion, but largely no changes in large trades during the same period. In this respect, the investors that initiate the small trades act as if they are surprised by the information contained in the press release of GC opinions. Such an action, in turn, can be viewed as evidence of a delayed response to the information in GC opinions by a section of investors.     

Which trades move prices in emerging markets?: Evidence from China's stock market

This paper extends Barclay and Warner's [Barclay, M.J. and J.B. Warner (1993), ‘Stealth trading and volatility: which trades move prices?’, Journal of Financial Economics, vol. 34, pp. 281–306.] original work on stealth trading by analysing which trades move price for the emerging Chinese stock market. A large block trade/manipulation hypothesis is proposed in addition to the stealth and public information hypotheses examined by Barclay and Warner. Using high-frequency data the results show that while medium and large-size trades are associated with disproportionately large, overall, cumulative stock price changes, it is the large-size trades (in terms of the number of transactions) which have the largest effect on cumulative price increases. Thus, while there is some support for stealth trading in the Chinese market, there are other effects in operation such as large block trades/price manipulation.     

Trading Behavior and the Unbiasedness of the Market Reaction to Dividend Announcements

This article examines the price formation process during dividend announcement day, using daily closing prices and transactions data. We find that the unconditional positive excess returns, first documented by Kalay and Loewenstein (1985) , are higher for small-firm and low-priced stocks. Price volatility and trading volume also increase during this period. Examination of trade prices relative to the bid-ask spread and volume of trades at bid and asked prices shows that the excess returns cannot be attributed to measurement errors or to spillover effects of tax-related ex-day trading. Rather, the price behavior is related to the absorption of dividend information.     

Information content of options trading volume for future volatility: Evidence from the Taiwan options market

This study follows the approach of Ni et al. [Ni, S.X., Pan, J., Poteshman, A.M., 2008. Volatility information trading in the option market. Journal of Finance 63, 1059–1091] – based upon the vega-weighted net demand for volatility – to determine whether volatility information exists within the Taiwan options market. Our empirical results show that foreign institutional investors possess the strongest and most direct volatility information, which is realized by the delta-neutral options/futures trades. In addition, a few individual investors (less than 1% of individuals’ trades) might be informed and realize their volatility information using the strangle strategy. Surprisingly, we find no evidence to support the predictive ability of the volatility demand from straddle trades, despite the widespread acknowledgement that such trades are sensitive to volatility.     

Time and the Price Impact of a Trade

We use Hasbrouck's (1991) vector autoregressive model for prices and trades to empirically test and assess the role played by the waiting time between consecutive transactions in the process of price formation. We find that as the time duration between transactions decreases, the price impact of trades, the speed of price adjustment to trade-related information, and the positive autocorrelation of signed trades all increase. This suggests that times when markets are most active are times when there is an increased presence of informed traders; we interpret such markets as having reduced liquidity.     

NUMBER OF TRANSACTIONS AND VOLATILITY: AN EMPIRICAL STUDY USING HIGH‐FREQUENCY DATA FROM NASDAQ STOCKS

Our empirical evidence based on transactions data of a sample of Nasdaq stocks indicates that trades of large firms are related to the proxies of marketwide and firm‐specific information. For large firms, an increase in the number of trades seems to have a beneficial effect on liquidity as measured by bid‐ask spreads. On the other hand, trades of small and medium firms are associated with firm‐specific information and are not related to marketwide information. For small and medium firms, the frequency of trades is positively associated with bid‐ask spreads, apparently because of the adverse information content of trades. JEL classification: G10, G12, G13     

The impact of transaction duration,volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.     

Realized volatility and transactions

This paper re-examines the impact of number of trades, trade size and order imbalance on daily stock returns volatility. In contrast to prior studies, we estimate daily volatility using realized volatility obtained by summing up intraday squared returns. Consistent with the theory of quadratic variation, realized volatility estimates are shown to be less noisy than standard volatility measures such as absolute returns used in previous studies. In general, our results confirm [Jones, C.M., Kaul, G., Lipson, M.L., 1994. Transactions, volume, and volatility. Review of Financial Studies 7, 631–651] that number of trades is the dominant factor behind the volume–volatility relation. Neither trade size nor order imbalance adds significantly more explanatory power to realized volatility beyond number of trades. This finding is robust to different time periods, firm sizes and regression specifications. The implications of our results for microstructure theory are discussed.     

Trading Activity and Price Volatility in the Municipal Bond Market

Utilizing a comprehensive database of transactions in municipal bonds, we investigate the volume–volatility relation in the municipal bond market. We find a positive relation between the number of transactions and a bond's price volatility. In contrast to previous studies, we find a negative relation between average deal size and price volatility. These results are found to be robust throughout the sample. Our results are inconsistent with current theoretical models of the volume–volatility relation. These inconsistencies may arise because current models fail to account for the effects of overall market liquidity on the costs of large transactions.     

The Impact of Trades on Daily Volatility

This article proposes a trading-based explanation for the asymmetriceffect in daily volatility of individual stock returns. Previousstudies propose two major hypotheses for this phenomenon: leverageeffect and time-varying expected returns. However, leveragehas no impact on asymmetric volatility at the daily frequencyand, moreover, we observe asymmetric volatility for stocks withno leverage. Also, expected returns may vary with the businesscycle, that is, at a lower than daily frequency. Trading activityof contrarian and herding investors has a robust effect on therelationship between daily volatility and lagged return. Consistentwith the predictions of the rational expectation models, thenon-informational liquidity-driven (herding) trades increasevolatility following stock price declines, and the informed(contrarian) trades reduce volatility following stock priceincreases. The results are robust to different measures of volatilityand trading activity. (JEL C30, G11, G12)     

Anonymity,Stealth Trading,and the Information Content of Broker Identity

This paper examines whether the identity of a broker involved in transactions contains information. Using a sample of transactions from the Australian Stock Exchange—where broker identity is transparent—we provide evidence that consecutive buyer‐/seller‐initiated transactions by the same broker have a relatively high permanent price impact. This implies that broker identity conveys information to market participants, and that markets in which broker identity is disclosed are likely to be more efficient. We also find that medium‐sized trades by the same broker convey greater information than large and small trades, which is consistent with stealth trading by informed investors.     

DOES VOLATILITY DECREASE AFTER REVERSE STOCK SPLITS?

Previous research documents that volatility decreases after reverse stock splits. I show that measurement effects bias observed volatility, especially for lower priced stocks. Based on observed returns, volatility decreases 25% after reverse splits. Controlling for bid–ask bounce, volatility still decreases for stocks with prices above $5.00. However, for stocks below $2.00, volatility increases slightly. The portion of observed volatility attributable to measurement effects declines as the stock price increases and as the minimum tick size decreases. Finally, there is a significant and positive cross‐sectional relation between changes in the number of trades and changes in volatility after reverse splits.     

Trading activity,realized volatility and jumps

This paper takes a new look at the relation between volume and realized volatility. In contrast to prior studies, we decompose realized volatility into two major components: a continuously varying component and a discontinuous jump component. Our results confirm that the number of trades is the dominant factor shaping the volume–volatility relation, whatever the volatility component considered. However, we also show that the decomposition of realized volatility bears on the volume–volatility relation. Trade variables are positively related to the continuous component only. The well-documented positive volume–volatility relation does not hold for jumps.     

VPIN,liquidity, and return volatility in the U.S. equity markets

We examine the volume-synchronized probability of informed trading metric (the VPIN flow toxicity metric, developed by Easley, Lopez de Prado, & O'Hara, 2012) as a real-time risk management tool for liquidity deteriorations in the U.S. equity markets. We find that VPIN provides information about market liquidity and stock return volatility on ex-ante basis. These results indicate that VPIN can be a useful risk-management tool for market makers, regulators and traders in the U.S. equity markets. We also document that VPIN is negatively associated with volume and number of trades, but positively associated with trade size and volume fragmentation. These findings suggest that VPIN indicates the adverse selection problem of liquidity providers by capturing the information in volume.     

The UK equity market around the ex-split date

Using UK stock market data this study unveils positive abnormal returns on and around the ex-split date. These excess returns are partially predictable using the publicly available information prior to the ex-split date. There is also a persistent increase in the post-split volatility of these stocks with the results being robust to the choice of the volatility proxy. Post-split volatility is found to be positively related to trading activity. Contrary to the US findings, volatility dynamics following the stock split are better captured by changes in the daily trading volume rather than by the number of trades.