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The first group of measures focuses on price delay and assess the explanatory power of
lagged returns in an extended market model relative to a base model to quantify how speedily
and accurately systematic information is incorporated into prices (Busch & Obernberger,
2016). In line with the methodology of Boehmer and Wu (2013) and Philips (2011), I
construct two measures using daily stock returns instead of weekly or monthly returns as
daily returns increase the number of observations and diminish potential observation errors.
Unlike the aforementioned authors I use five lagged market returns instead of four lagged
returns for the extended market model to cover all trading days in a week (Busch and
Obernberger, 2016). In sum, the base market model (2) and extended model (3) estimated
through OLS for each firm and each month are as follows:
2(,* = ,( + 1(
F 2G,* + H(,* (Base model) (3)
2(,* = ,( + 1(
F 2G,* + 1(
I>0 + H(,* (Extended market model) (4)
where 2(,* is the daily return on stock i, 2G,* is the daily return on the AEX All Share valueweighted
index (a proxy for market return) and 2G,*/I represents the lagged market returns.
The key interpretation of both models is that the coefficient for concurrent market return 1(
reflects whether all new information available is incorporated directly into a company’s stock
price. If there is a delay in the incorporation of information into stock prices one observes a
coefficient for lagged market returns 1(
I that is significantly different from zero in the
extended model. In the latter case, the extended model (4) will have higher explanatory
power than the base model (3). The two price delay measures are derived by using the
regression estimates of the base and extended model. The first measure is simply one minus
the ratio of the R2 of the base model over the R2 of the extended market model:

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