Trade size, order imbalance, and the volatility–volume relation

Chan, Kalok; Fong, Wai‐Ming

doi:10.1016/s0304-405x(00)00057-x

Cited by 375 publications

(232 citation statements)

References 34 publications

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“…Figure 1(A), for example, confirms the above observations: both imbalance measures are well above the x-axis; NOIMB, albeit less volatile, remains much higher after day 30 8 In their paper, the same OIMB database is used as in this study.…”

Section: A Descriptive Statistics For the Seo Firmssupporting

confidence: 70%

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Order Flow Patterns around Seasoned Equity Offerings and their Implications for Stock Price Movements

Huh

Subrahmanyam

2004

SSRN Journal

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. We appreciate the constructive and thoughtful feedback of Antonio Bernardo, Rossen Valkanov, and Neal Stoughton. We express special thanks to Xiao Chen of the UCLA Academic Technology Services for providing useful programming assistance. Abstract Order Flow Patterns around Seasoned Equity Offerings and their Implications for Stock Price MovementsIn this study, we employ order imbalance measures to provide evidence that there exists an individual/institutional dichotomy in reactions to seasoned equity offerings (SEOs).The evidence supports the notion that small, possibly naïve, individual investors keep trading SEO stocks aggressively while the returns of these stocks reverse in the post-issue period. Investors appear to be tardy in adjusting their overoptimism, and their trades systematically lag the return response. It appears to take more than two years for small individual investors to adequately revise their overoptimistic views. Consequently, the SEO portfolios that individual investors buy on net strongly underperform relative to the size-matching nonissuer portfolios as well as to the SEO portfolios that institutional investors buy on net in the post-issue period.

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Section: A Descriptive Statistics For the Seo Firmssupporting

confidence: 70%

“…document that for the sample of 1,322 NYSE stocks over the 11-year period, the average of daily NOIMB is −1.72% and that of daily DOIMB is −0.54%. 8 That is, on average there are more trading days with large seller-initiated trades than with large buyer-initiated trades.…”

Section: A Descriptive Statistics For the Seo Firmsmentioning

confidence: 99%

Order Flow Patterns around Seasoned Equity Offerings and their Implications for Stock Price Movements

Huh

Subrahmanyam

2004

SSRN Journal

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“…Chordia, Roll, and Subrahmanyam (2002) and Chan and Fong (2000) additionally highlight the effect of the order imbalance on price variability, providing evidence for both Propositions 4 and 5.…”

Section: Proposition 5 (Price Variability and Volume)mentioning

confidence: 99%

“…Chan and Fong (2000)) implicitly yield (b), and we are not aware of empirical studies that test (a). The positive relation between volume and order imbalances that we identify here provides insights for the strong empirical performance of the so called Amihud measure.…”

Section: B Volume and The Order Imbalancementioning

confidence: 99%

“…Proposition 4 order imbalance ր average absolute price change ր Chordia, Roll, and Subrahmanyam (2002), Chan and Fong (2000) Proposition 5 volume ր average absolute price change ր Chordia, Roll, and Subrahmanyam (2002), Chan and Fong (2000), Gallant, Rossi, and Tauchen (1992) Proposition 7 entry rate ր average volume ր average order imbalance ր average price changes ր duration ց causes for changes in entry rate: index inclusion, international market opening financial deregulation, cross-listing Propositions 8 & 9 information quality ր price impact ր average volume ր average order imbalance ր average price changes ր duration ց , Roll, and Subrahmanyam (2008), causes for changes in information quality: new disclosure rules, changes in analyst coverage, changes in information technology, changes in ownership structure (hedge funds rather than mutual funds), development of a new analysis tool, improvements in analysts' earnings forecasts Proposition 10 informed trading ր average volume ց duration ր (numerically) price changes ր causes for changes in the proportion of informed trading: changes in ownership structure (hedge funds rather than retail), market changes that trigger more retail participation or day trading …”

Section: Claim (Ii)mentioning

confidence: 99%

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Trading Volume in Dealer Markets

Malinova

Park

2010

J. Financ. Quant. Anal.

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We develop a financial market trading model in the tradition of Glosten and Milgrom (1985) that allows us to incorporate non-trivial volume. We observe that in this model price volatility is positively related to the trading volume and to the absolute value of the net order flow, i.e. the order imbalance. Moreover, higher volume leads to higher order imbalances. These findings are consistent with well-established empirical findings. Our model further predicts that higher trader participation and systematic improvements in the quality of traders' information lead to higher volume, larger order imbalances, lower market depth, shorter duration, and higher price volatility. * Malinova, katya.malinova@utoronto.ca, +1 416 978 5283; Park, andreas.park@utoronto.ca, +1 416 978 4189; University of Toronto, 150 St. George Street, Max Gluskin House, Toronto, M5S 3G7, ON, Canada. We thank Lones Smith as well as Ryan Davies, Rob McMillan, Jim Pesando, Carolyn Pitchik, and seminar participants at the 2007 CEA meetings and the University of Michigan for helpful discussions and comments. We are especially grateful to Hank Bessembinder (the editor) and Ohad Kadan (the referee) for detailed comments and suggestions. Financial support from the SSHRC and the Connaught Foundation is gratefully acknowledged. All errors remain our responsibility. I IntroductionAn important empirical regularity is that price volatility is positively related to trading volume and to the order imbalance (i.e. the absolute difference between the volumes of buy and sell orders); see Chordia, Roll, and Subrahmanyam (2002) for a comprehensive list of references or Karpoff (1987) for earlier studies. Volume is generated by trading activity, which is commonly explained by diversification and hedging motives, liquidity needs, or asymmetric information. Order imbalances occur either by chance or because of diverging opinions, which are often the result of heterogenous information.Focussing on the information motive, we develop a model to study the impact of nontrivial volume on prices. A key feature of our model is the parsimonious formulation of traders' information. It allows us to study how changes in the underlying information environment influence market activity. The economic questions that can then be addressed range from the impact of regulatory changes, such as regulation Fair Disclosure, to the effect of improvements in data processing technology. For instance, do such changes increase or decrease price volatility and do they lead to more or less trading?Our model combines features of Glosten and Milgrom (1985) and Kyle (1985) and has the following structure. Liquidity is supplied by an uniformed, risk-neutral and competitive market maker (or dealer). Demanders of liquidity either trade for reasons outside the model (e.g., to rebalance their portfolio or inventory), or they have private information about the fundamental value of the security. Specifically, informed traders receive private binary signals of heterogenous precisions, or qualities. ...

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International Business

Noi¹

2012

What's Your MBA IQ™?

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Trade size, order imbalance, and the volatility–volume relation

Cited by 375 publications

References 34 publications

Order Flow Patterns around Seasoned Equity Offerings and their Implications for Stock Price Movements

Order Flow Patterns around Seasoned Equity Offerings and their Implications for Stock Price Movements

Trading Volume in Dealer Markets

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