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Before looking at pre- and postformation date standard deviations we
will examine the average returns of stocks included in the HML portfolios
prior to the formation date. We calculate these average preformation re-
turns for the entire set of formation dates from June 1963 through June
1993, and plot these in figure 9.1. This figure shows that the preformation
returns of the HML portfolio are strongly negative, supporting the con-
tention of Fama and French that high B/M firms are distressed and that low
B/M firms have performed well in the past. Another important feature of
this plot is the larger average returns experienced in every January of the
preformation period. Every preformation (average) return outside of Janu-
ary is negative and every average January return is positive. Note also that


CHARACTERISTICS AND RETURNS 329



   
 












Figure 9.1. HML portfolio pre-formation returns. We first rank all NYSE firms by
their book-to-market ratio at the end of year t−1 and their market capitalization
(ME) at the end of June of year t, as described in the Appendix. We form 30 percent
and 70 percent breakpoints for book-to-market and a 50 percent breakpoint for
ME based on these rankings. Starting in July of year t, we then place all NYSE/
AMEX and NASDAQ stocks into the three book-to-market groups (High, Medium,
and Low) and two size categories (Small and Big) based on these breakpoints. The
HML portfolio returns are then defined to berHML=(rHB+rHS−rLB−rLS)/2. For
this figure, we calculate the returns of the HML portfolio in each of forty-two
months prior to the formation date. This is done for each of the formation dates
from June 1963 through June 1993, and then the preformationreturns are averaged
to obtain the returns plotted presented in the figure. The portfolio weights used in
calculating the preformationreturns for each of the HB, HS, LB, and LS portfolios
are the market values of the firms on the last trading day of June of year t, divided
by the sum of these market values for the portfolio.

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