Linking the volume and expected return implications of various models to the
Halloween effect explanations, we will be able to differentiate one from another. The
vacation explanation argues that investors taking vacations during summer months
results in changes in risk aversion, or risk sharing capacity, in the economy (Bouman &
Jacobsen, 2002). The idea is consistent with the exogenous liquidity demand outlined in
Campbell, Grossman and Wang (1993). In particular, investors that do not take
vacations will have constant risk aversion (type A), and investors that take summer
vacations will have seasonal time varying risk aversion (type B). Prior to taking a
vacation, type B investors may become more risk averse and demand less risky assets as
they would rather be spending time relaxing than paying attention to the stock market,
or they may simply liquidate the stocks to meet their increased cash needs due to their
vacations. Type A investors would only be willing to buy risky assets if they are offered
them in conjunction with higher expected returns. This liquidity demand and shift in
expected returns is expected to occur prior to, during and after the investor takes
vacations, which should correspond with the volume related return reversals (lower
current returns accommodated with high volume and higher expected future returns).
If we also allow for heterogeneous beliefs, as investors pay less attention to the stock
market and trade less during vacations, we would expect to see less trading activities
accompanied with lower returns in the vacation season, as in Hong and Yu (2009).
The SAD effect examined in Kamstra, Kramer and Levi (2003) is relatively easy to
distinguish from other effects by investigating the trading volume patterns, since the
affected by seasonal affective disorder (SAD) become depressed during the fall months
and demand higher risk premia during winter months, causing this seasonal stock
market return pattern. While both the vacation and SAD effects suggest the same return
seasonals, they imply very different trading patterns from investors. According to the
model in Campbell, Grossman and Wang (1993), investors affected by SAD would sell
stocks starting in autumn when exposure to daylight decreases, inducing stronger
volume related return reversals. During the period with relatively less daylight, with a
smaller number of investors in the market, we would expect less trading with lower
returns.
Two recent studies attempt to establish the link between vacations and trading
activities to understand this seasonal return pattern. Using stock market trading data in
Finland, Kaustia and Rantapuska (2012) show that the seasonal variation in the buy-sell
ratio and trading volume are unrelated to length of daylight and sunniness, but related to
summer vacation seasons. They find that individual investors sell stocks before and
during summer holidays (May-July) and purchase stocks during fall months (August-
October). In addition, trading volume drops for both individual investors and
institutions during the holiday months of May-August. Similarly, Hong and Yu (2009)
document that trading activities during summer months (July-September for Northern
Hemisphere countries and January-March for Southern Hemisphere countries) are
significantly reduced from the rest of the year, accompanied by lower stock returns. One
important link these studies fail to establish is, however, a strong assumption that
summer months are correlated with a higher number of people taking vacation, while
uncorrelated with other variables that may affect trading. Although summer months are
proxy for high vacation activities, since a simple summer dummy may actually pick up
other variations unrelated to vacation taking. For example, Cao and Wei (2005) find
stock returns are negatively related to temperatures because investors become more
aggressive in risk taking when temperatures are low, leading to higher winter month
returns. This argument suggests that cold weather is associated with higher trading
activities and higher returns. The trading volume and return pattern documented in
Hong and Yu (2009) would also be consistent with this temperature hypothesis, in
which summer is also a proxy for high temperature, resulting in the relatively lower
trading volume and returns. Another example is Gerlach (2007), who claims that the
Halloween effect is partially induced by more macroeconomic news arrivals during fall
months, with the seasonal pattern disappearing if the returns are examined only using
the 60% of trading days with no macroeconomic announcements. This implies that low
trading volumes and returns during summer months could be due to the lower news
arrival rate in summer instead of vacation taking activities. In addition, Ogden (2003)
documents annual seasonal cycles of macroeconomic variables in the US market and
finds that the predictive power of stock returns for quarters ending in December and
March is greater than those ending in June and September, indicating that stock markets
are more informative during winter months than summer months and investors forecast
macroeconomic and risk conditions to pricing security only during winter months. In
addition, the forecasting variables that are supposed to capture expected risk premium
only have predictive power over the six months from October through March, indicating
that stock prices may only be priced correctly from October through March. Adopting a
simple summer dummy might attribute all these endogenous variations of economic