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Dynamic Relations between Order Imbalance, Volatility and Return of Top Losers

Dynamic Relations between Order Imbalance, Volatility and Return of Top Losers. Authors: Yong-Chern Su, Han-Ching Huang, Po-Hsin Kuo and Peiwen Chen (National Taiwan University) Discussant: Yin-Feng Gau (National Central University) NTU Finance Conference 2008. Summary.

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Dynamic Relations between Order Imbalance, Volatility and Return of Top Losers

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  1. Dynamic Relations between Order Imbalance, Volatility and Return of Top Losers Authors: Yong-Chern Su, Han-Ching Huang, Po-Hsin Kuo and Peiwen Chen (National Taiwan University) Discussant: Yin-Feng Gau (National Central University) NTU Finance Conference 2008

  2. Summary • 61 daily “loser” stocks, TAQ database, 12/1/2005-12/31/2005 • Use GARCH(1,1) to study the relation b/tw volatility and order imbalance • Use the regression of return on lagged order imbalance to study the predictability of returns • Use VAR of return and order imbalance to study the causal relation • Trading strategy based on order imbalance: buy if order imbalance is positive; sell if order imbalance is negative

  3. Comments • Where is the “story”? – better if focused on a few important themes • Contemporaneous relations b/tw • return (Rt) and order imbalance (OIt) : eq (1); Chordia and Subrahmanyam (2004) • volatility and order imbalance: eq (2); Chan and Fong (2000) • Lagged relations b/tw • Rt and OIt, OIt-2, …, OIt-4 : eq(3); Chordia and Subrahmanyam (2004) • Rt and OIt-1, OIt-2, …, OIt-5 : eq(4); Chordia and Subrahmanyam (2004) • causal relation (Granger, 1969; Chen and Wu, 1999) b/tw return and order imbalance • trading strategy based on the order imbalance

  4. Questions about Data • Data frequency: daily or tick-by-tick? • P.3: “the order imbalance is defined as daily net share volume …. • How long dose “period t” in models span? • If the estimation of models are based on “ intraday” data, how fine the data are? • If using the intraday data, the intraday seasonality in volatility should be checked and adjusted • Is there intraday seasonality in OIB? • Why use the sample of “top loser”? Motivation?

  5. Questions about Empirical Results • Is the same model (e.g. GARCH(1,1)) fitted to all 61 stocks studied • should take care of the seasonality in intrady volatility, leverage effect, day-of-the-week effect • Table 2 shows for 40% of sample returns, coefficient of OIt is positive and significant in eq(2), whereas for 40% of sample, the coefficient of OIt is negative and significant in eq(2)  How to judge the effect of OIt on volatility • As shown in Panel A of Table 6, three of four trading strategies earn negative returns, and one strategy obtains 1.06% return.  not substantial or significant enough for a profit, because the transaction costs and taxes are not considered yet

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