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DARK OMENS IN THE SKY: DO SUPERSTITIOUS BELIEFS AFFECT INVESTMENT DECISIONS?

 

Gabriele M. Lepori*

(June 2009)

Abstract

Psychological research documents that individuals are more likely to resort to superstitious practices when operating in environments dominated by uncertainty, high stakes, and perceived lack of control over the outcomes. Based on these findings, we suggest that the stock market represents an ideal breeding ground for superstition and then test whether superstition-induced behavior affects investment decisions. Our empirical analysis focuses on some beliefs associated with eclipses, phenomena that are typically interpreted as bad omens by the superstitious both in Asian and Western societies, and we employ a dataset containing 362 such events over the period 1928-2008. Using four broad indices of the U.S. stock market, we uncover strong evidence in support of our superstition hypothesis in four distinct ways. First, the occurrence of negative superstitious events (i.e. eclipses) is associated with below-average stock returns, which is consistent with a diminished buying pressure coming from the superstitious. Second, the size of the superstition effect is estimated to increase in times of high market uncertainty and when eclipses draw wide media coverage and public attention. Third, the negative performance of the market during the superstitious event is followed by a reversal effect of similar magnitude (10 basis points per day) on the subsequent trading days. Fourth, eclipses are accompanied by a trading volume decline. When we extend our analysis to a sample of Asian countries, we find analogous results. The patterns we document are inconsistent with the Efficient Market Theory, as eclipses are perfectly predictable events.

 

    JEL Classification Codes: A12, C22, D81, G11, G12, G14 Keywords: Superstition, predictable irrational behavior, event effect, reversal effect, trading volume, behavioral finance, U.S. stock market, abnormal returns, media coverage     *Department of Finance, Copenhagen Business School, Solbjerg Plads 3, A5.36, 2000 Frederiksberg, Denmark. Tel. +45 38153547. Email: gl.fi@cbs.dk. I would like to thank Richard Baillie, Geoffrey Booth, Raoul Minetti, Emma Iglesias, David Hirshleifer, Stephen Dimmock, Meir Statman, and seminar participants at Michigan State University’s Red Cedar conference for helpful comments and suggestions. All remaining errors are mine.

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Introduction   

It is no secret that certain groups of people such as sports athletes and gamblers tend to engage

in superstitious activities. Players often wear lucky caps and socks, eat lucky foods, and wear all sorts of

other good luck amulets (Burger and Lynn, 2005; Schippers and Van Lange, 2005). When they believe

they are on a winning streak, some of them want to wear the same clothes and repeat the same rituals

before a game. It is much less known, though, that similar behaviors are found among professional

traders and other agents whose actions would be expected to strictly adhere to a rational approach

(Vyse, 1997; Insana, 1996; Melamed and Tamarkin, 1996; Zaloom, 2006). More than two centuries

after the cultural revolution brought by The Enlightenment, superstitious beliefs and practices are still

widespread among the general public. As suggested by several recent surveys, this appears to be the

case for both Asian societies and Western civilizations, though to a lesser extent as far as the latter are

concerned (Gallup and Newport, 1991; Nisbet, 1998; Tsang, 2004; Musella, 2005).

Several studies conducted in the fields of psychology and sociology have already documented

that superstition is able to affect a set of human choices and attitudes (Vyse, 1997; Stevenson, 2000;

Kramer and Block, 2008), and we believe that the science of decision making can definitely benefit

from the insights that can be derived from an investigation of the influence that superstitious beliefs

exert on economic/financial decisions. So far, this kind of topic has not attracted much attention in the

financial literature, one of the few exceptions being the analysis of the so-called Friday the Thirteen

superstition by Kolb and Rodriguez (1987). In this paper we scrutinize a novel type of superstitious

beliefs that is found to be particularly popular among many societies around the world and then we test

whether superstition-induced behavior affects investment decisions and stock returns in the U.S. market

and in a sample of Asian countries.

We start our analysis by discussing the psychology of superstition and highlighting the factors

that contribute to the developing of superstitious attitudes. A recurrent finding in this area of research is

that individuals tend to resort more heavily to superstition when operating in an environment in which

there is high uncertainty, the stakes are high, and outcomes are perceived to be to a large extent beyond

one’s control (Malinowski, 1955; Felson and Gmelch, 1979; Keinan, 2002). Since investment decisions

involve all of these elements, we suggest that the stock market represents an ideal breeding ground for

the developing of superstitious attitudes. The anecdotal evidence we collect about the behavior of

professional traders in the U.S. and in other Western countries indeed supports such a view, as we

illustrate that many of the superstitious rituals in which athletes and gamblers engage are also employed

by floor traders (King, 1975; Fenton-O’Creevy et al. 2007).

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In our empirical examination we focus on the superstitious beliefs associated with eclipses, as

the folklore literature provides detailed evidence that these phenomena have been perceived for

millennia as inauspicious omens by many cultures around the globe. In particular, we collect data about

362 (solar and lunar) eclipses that have occurred between 1928 and 2008. We conjecture that the

occurrence of an eclipse encourages noninformational superstitious investors to momentarily give up or

delay their purchases of risky assets. To argue that eclipses induce negative sentiment in the

superstitious that reduces demand in the stock market and decreases equity returns and trading volume,

we require four crucial factors. First, we provide evidence that the attitudes of a considerable fraction of

Americans are influenced by superstition. Second, we document that within the American society (as

well as in other societies) eclipses are interpreted as bad omens by the superstitious. Third, using the

Vanderbilt Television News Archive and Google News Archive, we show that eclipses spawn wide

media coverage and draw public attention, which implies that a large fraction of the population is aware

of their occurrence. Fourth, we also illustrate that media coverage is concentrated in a short time

window around the event and, as such, it is reasonable to believe that the occurrence of an eclipse

affects people’s behaviors at approximately the same time.

Using four broad indices of the U.S. stock market and controlling for possible confounders, we

find support for our superstition hypothesis and contribute to the existing literature in five distinct ways.

First, we show that negative superstitious events (i.e. eclipses) are associated with lower than average

U.S. stock returns, where the marginal effect is approximately 10 basis points per day over a three-day

window around the event. Using the slope of the U.S. yield curve to control for economic shocks, we

suggest that this superstition effect is not attributable to the regular flow of economic news. Second, we

demonstrate that the size of the superstition effect is amplified (around 15 basis point per day) during

periods of high market uncertainty (i.e. high volatility of returns), which is consistent with the view that

people are more likely to resort to superstitious behaviors when experiencing an intense feeling of lack

of control. Third, we document that the magnitude of the superstition effect is larger when eclipses

generate more press coverage and draw public attention. Fourth, using both parametric and

nonparametric tests, we find that the negative performance of the stock market during the superstitious

event is followed by a reversal effect in the subsequent few trading days. Furthermore, the size of the

reversal effect is similar to the size of the original superstition effect (approximately 10 basis points per

day over a two-day window). This suggests that the market quickly adjusts and inefficiencies do not last

for long. Fifth, we demonstrate that trading volume experiences a decline when eclipses take place,

which is once again consistent with the view of a diminished buying pressure coming from the

superstitious. We employ a set of robustness checks to show that these empirical findings are not

sensitive to outliers and do not depend on the definition of the superstitious-event window. The patterns

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we detect are also robust to the choice of the set of controls and of the estimation framework (OLS,

GARCH). In the last section of the paper, we extend our analysis to a sample of ten Asian countries,

namely China, Hong Kong, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, Taiwan, and

Thailand. The evidence we find by pooling these time-series cross-sectional data is once again

consistent with our superstition story, and the eclipse effect we estimate is similar, in terms of

magnitude, to the effect documented in the U.S. case.

Overall, our findings are strongly consistent with the hypothesis that superstition-induced

attitudes have a transitory impact on investment decisions and stock prices. Moreover, since eclipses are

perfectly predictable events and we show that a profitable trading strategy could be constructed based on

their occurrence, the evidence that we produce is inconsistent with the Efficient Market Theory.

The rest of the paper is organized as follows. Section I analyzes the psychology of superstition

and discusses the pervasiveness of superstitious attitudes in modern society and in the context of

financial markets. Section II introduces the superstitious beliefs associated with eclipses. Section III

examines the extent to which eclipses generate media coverage and public attention, and section IV

presents the specific hypotheses under investigation. Section V describes the data and section VI

provides an empirical analysis of the behavior of U.S. stock returns during superstitious events (i.e.

eclipses). Section VII sheds additional light on the proposed causality link by examining the

relationships between superstition effect, market uncertainty, and media coverage of the event and by

testing the presence of reversal effects; this section also contains an analysis of the dynamics of trading

volume during eclipses. Section VIII provides some international evidence, and Section IX concludes.

 

I. Exploring Superstitious Beliefs in Modern Society A. The Mechanics of Superstition “The root of all superstition is that men observe when a thing hits, but not when it misses.” – Francis Bacon

The term “superstition” is frequently used in common speech and is usually employed in a

pejorative sense, the reason being that, at least implicitly, superstition is generally perceived as an

irrational and unfounded phenomenon (Vyse, 1997). In most occasions, its meaning is simply assumed

rather than defined, though. Several definitions have been advanced in the sociological and

psychological literature. Webster’s New World College Dictionary describes superstition as “… any

belief, based on fear or ignorance, that is inconsistent with the known laws of science or with what is

generally considered in the particular society as true and rational; such as beliefs in charms, omens, the

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supernatural, etc…”. Jarvis (1980) provides a broad classification of superstitions, which may be sorted

into omens, taboos and spells. Omens are signs that believers regard as predictions of the future and

may involve good luck, bad luck, death and new birth. Taboos are prohibitions whose violation may

result in a punishment. Finally, spells are a means of averting bad luck or promoting good luck.

Obviously, the survival of superstitious beliefs and practices in modern industrialized societies

poses a challenge to scientists. In the nineteenth century, social theorists predicted that superstition

“would gradually fade away in the face of that rationalism and empiricism which was presumed to be

characteristic of modern capitalist society” (Campbell, 1996). Nevertheless, as the following sections

will discuss, there is extensive evidence that during the twentieth century there was no significant

decline in the popularity of superstition. Though more widespread among Asian cultures, superstitious

beliefs and behaviors remain common, either at a subconscious or conscious level, in Western societies

as well.

To account for such a phenomenon, researchers have put forward several theories, most of

which build upon either a psychological or anthropological framework. According to the highly

influential work of Skinner (1948), superstitious beliefs are misconceptions that result from incorrect

reasoning or learning processes. In particular, imperfect knowledge about cause-and-effect relationships

would be responsible for making people draw wrong deductions. In Skinner’s interpretation the

development of superstitions follows a recurrent pattern: an individual or group of people faces

uncertainty regarding an outcome of high interest; before the outcome is revealed by Nature, something

fortuitous happens and the fortuitous event is then taken as cause, even if no actual cause-and-effect

relationship exists between the two factors; accidental repetition of this fortuitous pairing of events

subsequently acts as a positive reinforcer and facilitates the survival and transmission of such a false

belief.

Anthropologist Malinowski (1955) maintains that superstitions are psychologically necessary

for human existence and should not be dismissed as futile primitive beliefs. According to his “theory of

the gap”, magic/superstition fulfills some vital functions within any given culture, as it helps fill the

void of the unknown and thus mitigate anxiety. Such a need is so strong in mankind that, in the words of

Scheibe and Sarbin (1965), “where he lacks truth and needs it, [man] makes it up, somehow”. It is not

surprising, as such, that Cohen at al. (1959) find that individuals are likely to prefer distorting facts or

logic to discarding their superstitions. In a world in which quasi-rational decision-makers are

constrained by limited cognitive abilities and pursue satisfactory solutions rather than the optimal ones,

by providing information from supernatural sources superstition offers people an illusion of control. By

allowing access to a supposedly higher knowledge, it gives the false impression of relieving individuals

of their physical limitations (Tsang, 2004).

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Henslin (1967) asserts that it is the social nature of humans that plays a crucial role in the

survival and diffusion of superstitions. People’s behaviors always arise within a social context and the

reactions shown by significant others act as positive or negative reinforcers. What appear to be

successful behaviors are then taught or transmitted to new members of the society and slowly become

part of a culture. Scheibe and Sarbin (1965) summarize this point claiming that “it is as if each culture

presents its participants with a rule book which states standard beliefs about what leads to what”.

Casual observation suggests that the extent to which superstitious beliefs and practices affect

human behavior and decision-making varies across cultures, individuals, and contexts. The next section

will be devoted to the analysis of the factors that researchers have identified as possible antecedents of

superstitious behavior.

B. The Perfect Mix: High Stakes, Uncertainty, and Perceived Lack of Control

The growing body of literature on superstition has produced some consistent findings. People

tend to turn to superstitious practices when dealing with outcomes that are important to them and facing

conditions of high uncertainty, competition, and stress (Felson and Gmelch, 1979; Keinan, 2002, Case

et al., 2004; Schippers and van Lange, 2005; Burger and Lynn, 2005). Malinowski (1955) is perhaps the

first to introduce the idea that individuals are more likely to resort to superstitious strategies when they

believe outcomes are largely affected by uncontrollable forces. Observing the behavior of Trobiand

Islanders in Melanesia, he noticed that they heavily employed magic rituals when performing activities

that were to a large extent influenced by external factors (e.g. fishing, protecting crop plants from

parasites), whereas no such practices were used when dealing with activities that merely required

knowledge or skill (e.g. boat building). Even more interestingly, only those tribe members involved in

deep-sea fishing (dangerous and highly unpredictable in terms of catch) resorted to complex magical

rituals, whereas those fishing in the calm waters of the lagoon seemed to rely entirely on their skills.

Based on these observations, Malinowski suggested that superstition works as a coping mechanism. It

provides a feeling of control over the uncontrollable. When uncertainty, strong competition, and stress

pose a threat to perceptions of control “people attempt to regain control through the use of superstitious

strategies or magical thinking” (Case et al., 2004).

Such an interpretation has found support in many studies and in a variety of contexts. Sportsmen

(Schippers and Van Lange, 2005), students (Keinan, 2002), and businessmen (Tsang, 2004), for

instance, have been found to resort to superstitious rituals under conditions of high uncertainty,

competition, and stress. Within an experimental setting, Whitson and Galinsky (2008) show that the

desire to fight uncertainty and retain control is sufficiently strong as to affect perception to the extent of

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inducing people to see illusory patterns where none exist, including “seeing images in noise, forming

illusory correlations in stock market information, perceiving conspiracies, and developing

superstitions”. In his examination of the superstitious behaviors of various classes of individuals (e.g.

soldiers, financial investors, students, athletes, gamblers, actors), Vyse (1997) claims that “a universal

truth about superstition is that superstitious behavior emerges as a result of uncertainty to circumstances

that are inherently random or uncontrollable”. Interestingly, what also emerges is that often people make

use of superstitious strategies not because they think it is actually effective, but because those rituals

offer them the reassurance of feeling in control (Tsang, 2004).

These findings are extremely important for the present study. From the perspective of any

individual market participant, stock market outcomes appear as highly uncertain. The dominant

paradigm in financial economics, the Efficient Market Hypothesis, specifically maintains that all

relevant information is immediately incorporated into equity prices and, as such, prices only react to the

arrival of new information. Since unexpected news, by definition, is random, investors have no choice

but to cope with uncertainty on a daily basis. Moreover, it is largely acknowledged that competition is

fierce in financial markets, stress is high (e.g. Coates and Herbert, 2008) and, obviously, outcomes are

enormously important to the individuals involved. As a result, the stock market seems like an ideal

breeding ground for superstition, and investment decisions represent a perfect set for testing the extent

to which decision-making is affected by superstitious beliefs.1

Before illustrating the evidence that has been produced so far in this area, and before providing

an anecdotal account of superstitious attitudes common among the trading floor community, we now

briefly discuss the enduring popularity of superstition within American culture.

 

C. Prevalence of Superstitious Beliefs among Americans

The extent to which superstitious beliefs are common and socially accepted in Asian cultures is

generally considered to be larger than in Western countries (Tsang, 2004). Yet, Vyse (1997) reports that

at least 9% of Americans suffer from a fear of Friday the thirteenth, and on such a date many of them

will avoid flying, starting a new job, getting married, and closing on a house (Harris, 2006;

Brockenbrough, 2006). In the U.S. alone, such an unwillingness to go to work or do business on Fridays

the thirteenth is estimated to generate an economic loss that ranges between $800 and $900 million, on

average, during each occurrence (Palazzolo, 2005). According to a spokesman for Otis Elevator                                                             1 In this regard, it is well-known that the use of technical analysis is seen by many academics as a superstitious practice (Mobio, 2000). In the words of Mackenzie (2003), “chartism seems fated permanently to the status of ‘astrology’ with respect to the ‘astronomy’ of financial economics”.

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Company, the world’s largest manufacturer of elevators, around 85% of the buildings in the world do

not feature a named 13th floor (Perkins, 2002), and such a percentage reaches 90% in the United States

(Kramer and Block, 2008). Overall, the belief that the number 13 brings bad luck appears to be a very

popular superstition within the culture of the United States (Jahoda, 1969).

Superstitious beliefs can also be found in numerous other domains, though. Responding to the

media buzz originated by the publication of a White House Chief of Staff’s memories, First Lady Nancy

Reagan openly admitted that she used to consult an astrologer when planning the president’s schedule

after his attempted assassination (Eve and Dunn, 1990). Phillips (2003) reports that U.S. soldiers driving

tanks during the 2003 Iraq war would not eat apricots, as for some reason they thought those fruits

would bring bad luck. The pervasiveness of superstition among fishermen, both in the U.S. and in other

countries, is well-known (Gill, 1994). American baseball players and coaches “frequently wear lucky

socks and caps, sit in lucky places, eat lucky foods, and engage in various superstitious activities before

and during baseball games” (Burger and Lynn, 2005). Similar superstitious practices appear to be

common in many other American sports such as football, basketball, gymnastics, and track and field

(Bleak and Frederick, 1998; Todd and Brown, 2003).

Mitchell (1995) reports that approximately 175,000 part-time and 10,000 full-time astrologers

operate in the U.S., horoscopes are featured in almost 70% of American daily newspapers, and 66% of

the population reads them at least once per week. Interestingly, Gallup and Newport (1991) and Mowen

and Carlson (2003) find a positive correlation between belief in astrology and belief in superstition.

Many Americans “carry good-luck charms or engage in simple acts, such as knocking on wood or

crossing fingers, that they hope will ward off bad fortune and bring on good” (Vyse, 1997). According

to the 1990 Gallup national survey, about 18% of Americans admits to being very or somewhat

superstitious, and another 25% claims to be “not very” superstitious. Jointly with the responses to other

survey questions related to the belief in paranormal phenomena, these outcomes lead Gallup and

Newport (1991) to claim that “Americans express a belief in the existence of paranormal, psychic,

ghostly, and otherworldly experiences and dimensions to a surprising degree”. Similar surveys have

been conducted in 1997, 2001, and 2005, confirming that this kind of beliefs is persistent within the

American society (Nisbet, 1998; Musella, 2005). It can also be added that, being superstition often

associated with ignorance and naivety, many people may be reluctant to openly reveal such beliefs and,

as a result, survey outcomes are likely to underestimate their pervasiveness (Jueneman, 2001).

One may argue that the acceptance of pseudoscience and superstition is restricted to the least

educated portion of the population, i.e. the scientific illiterate. In the words of Miller (1987), the

influence that these beliefs exercise may be explained in light of the fact that “for the 25 million

Americans who do not have a high school diploma, the world is a strange, hostile, and somewhat

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dangerous place”. Contrary to this view, though, several studies have failed to find any negative

relationship between education and paranormal thinking, thus suggesting that superstitious beliefs may

be common among college-educated individuals as well (De Robertis and Delaney, 1992; Goode, 2002;

Mowen and Carlson, 2003; Farha and Steward, 2006).

 

D. Superstition and Economic Decisions

The impact of superstitious beliefs on decision-making has been the subject of numerous studies

in the fields of psychology and sociology, revealing that even critical decisions related to one’s own

health and family planning can be influenced by superstition (e.g. Kaku, 1974; Stevenson, 2000; Do and

Phung, 2006).

On the other hand, in economics and finance little attention seems to have been devoted to this

topic so far. Among the few exceptions are Kramer and Block (2008) who, studying the preferences of

U.S. consumers within an experimental setting, report that “participants became significantly more risk-

averse in their choices after thinking about Friday the thirteenth, as compared to a day that is not

associated with bad luck”. Kolb and Rodriguez (1987), using a sample of U.S. daily stock returns from

the CRSP value- and equally-weighted indices from 1962 through 1985, uncover some evidence in

support of the hypothesis that mean returns for Friday the Thirteenth are significantly lower than those

of other Fridays, as one would expect based on the “Friday the Thirteenth” superstition.2

Partly related to this stream of literature, Dichev and Janes (2003) and Yuan et al. (2006)

provide some evidence that stock returns exhibit a relationship with the lunar cycle in a large sample of

countries, whereas Lucey (2008) documents a link between lunar phases and some precious metal

returns.

Among the studies mainly concerned with Asian cultures, Tsang (2004) shows that superstition

plays a relevant role in the decision process of Chinese businessmen, who often employ experts of Feng

Shui (an ancient Chinese art that studies the interaction between people and their living environment to

create a positive “flow of energy”) as their consultants. Woo and Kwok (1994), analyzing license plate

auctions in Hong Kong, find that plates containing the number 8 (a sign of prosperity in Cantonese-

speaking countries) sell for statistically higher prices than plates featuring the number 4 (a sign of

death). Brown and Mitchell (2004) focus on trading at the Shanghai and Shenzhen stock exchanges, and

take advantage of the fact that these two markets were characterized by a strict segmentation over much                                                             2 Subsequent analyses by other authors using alternative samples have not confirmed these findings, though. See Dyl and Maberly (1988), Chamberlain et al. (1991), and Coutts (1999). Lucey (2000), using data from 19 countries, suggests that returns on Friday the Thirteenth are statistically greater than returns on other Fridays.

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of the period under consideration: A-shares could only be held by domestic investors, whereas B-shares

could only be traded by foreign investors. In such a setting, the authors find that the prices of A-shares

are statistically more likely to feature the number 8 than the number 4 as a last digit, while the

preference for the number 8 is much weaker for B-shares, consistently with the Chinese superstition

story. Similar findings are presented by Brown et al. (2002) with regard to the number preferences of

traders in Hong Kong, whereas the evidence is weak in the case of Australia, Indonesia, the Philippines,

and Singapore.

A valuable lesson taught by these studies is that, as the degree of integration among international

financial markets has considerably increased during the recent decades, an ideal setting for testing the

impact of superstition on investment decisions should involve some beliefs that are shared by many

cultures around the world.

 

E. Are Traders Superstitious?

The stock market, given its inherent randomness, the strong competition, and the high stakes

involved, has been deemed in section I.B as an ideal breeding environment for superstition. U.S. banker

J. P. Morgan is rumored to have frequently sought advice from an astrologer back in the 1920s and,

currently, at least two U.S. fund managers (one based in New York and one in Abu Dhabi) provide

wealthy investors with money management services based on strategies derived from financial astrology

(Eff, 1996; McMahon, 2005).

While it is well-known, and to some extent socially accepted, that athletes indulge in all sorts of

superstitious rituals before and during taking part in sports events (Schippers and Van Lange, 2005), it is

much less acknowledged that traders exhibit similar behaviors. In his autobiography, Leo Melamed, a

former chairman of the Chicago Mercantile Exchange, maintains that “most traders, whether they admit

it or not, are superstitious…Some have a lucky tie that they must wear every day, some use a lucky

pencil, others drive to work down the same streets, and so on. I have even heard of lucky underwear that

had to be washed every night so that the trader could wear it the next morning” (Melamed and

Tamarkin, 1996). Recounting her experiences on the floor of the Chicago Board of Trade and in a

dealing room in the City of London, former clerk and trader Caitlin Zaloom offers a similar perspective

and claims that “some traders carry talismans like a lucky tie or assign magical value to different

objects. Like professional athletes, some do not change socks when they are on a winning streak. Many

obsess over keeping all conditions exactly the same” (Zaloom, 2006). Analogous intriguing anecdotes

are reported by Insana (1996) with respect to Wall Street floor traders and by King (1975) with regard to

the San Francisco Stock and Exchange Board. Fenton-O’Creevy et al. (2007) interview 118 traders at

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four investment banks in London and document numerous superstitious rituals. Keyes (1999), when

describing the microcosm inside the forex dealing room at a major U.S. bank, recounts that “many

traders are so superstitious that the dealing room is filled with fuzzy bears, Gumbys, and the like, which

are rubbed, patted, and tossed into the air for luck”.

Moving the focus to the broader group of investors, Bates and Bowles (1994) claim that

astrology has been used by financial market speculators since at least 1543, when Kristofer Kurz

implemented the first known trading system based on the relative position of celestial bodies. Today,

any simple search conducted using an Internet search engine returns literally hundreds of thousands of

web sites, message boards, blogs, books, and weekly newsletters that claim to deliver trading signals

and advice based on the tools of the so-called financial astrology and the horoscope. As a result, there

seems to be evidence that, amidst the unpredictability that governs financial markets, at least a portion

of market participants is willing to resort to any instrument that can provide them with some form of

guidance when making financial decisions, no matter whether the instrument in question is rational or

irrational. In the words of a former trader, “superstitions, the little rules we enact for ourselves, provide

a sense of security and control in an environment where neither exist…” (Hoenig, 2007).

Encouraged by these anecdotal accounts, we now turn to analyzing a specific type of

superstitious beliefs, and then we empirically test whether it affects investment decisions in the expected

way.  

II. Evil in the Sky: Eclipses between Awe and Fear Clearly, not all superstitious attitudes will have a discernible impact on aggregate economic

variables even if they influence the decisions of some people. A detectable effect requires that (1) the

belief in question affects the behavior of a large group of individuals, (2) it does so at nearly the same

time and (3) in the same direction. To this end, in this paper we focus on the superstitious beliefs

spurred by solar and lunar eclipses. As the folklore literature documents, through the ages, celestial

phenomena have been watched in awe and fear (Park, 2008). Eclipses, in particular, have been

commonly hailed as evil omens in most cultures throughout the globe, and there is also considerable

evidence that, in many modern societies, such beliefs are still partly alive (Steel and Davies, 2001).

Through the course of history, eclipses have been often interpreted as presages foretelling the future

occurrence of horrific events such as calamities, famines, and outbreaks of deadly diseases. This

is not surprising, as in the eyes of our ancestors there was nothing more vital than the sun’s daily

illumination and nothing more regular than the moon cycle, so their sudden disappearance could easily

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evoke the end of the world. Though superstitious attitudes were more widespread among the common

multitude than within the literate elite, according to Carrier (1998) “the majority of lunar and solar

eclipses mentioned in ancient works are presented as coinciding with wars, battles, or the deaths of

prominent persons, and these coincidences are by and large invented”. Descriptions that lunar eclipses

tend to prophesy earthquakes and storms can be found in Aristotle (2004), Pliny the Elder (2004), and

Virgil (1982). Historian Plutarch (2007) writes that “men regard [the eclipse] as monstrous and as a sign

sent from god portending some great misfortunes”. According to the narrations of Tacitus and Plutarch,

in the early Roman Empire the common man would respond to lunar eclipses by raising a clamor,

playing horns and trumpets, and holding up to the sky many burning torches in order to “prevent

wizards from drawing down the moon” (Carrier, 1998).

The halo of suspicion that surrounds eclipses does not seem to have disappeared in modern

Western and Asian societies. In his encyclopedia of superstitions of the Western world, Webster (2008)

reports that according to a popular belief “you need to be cautious and take extra care for three days

before and after any eclipse has taken place…An eclipse of the moon is a sign of bad luck, making it a

bad time for investments and important decisions”. Similar, in her collection of popular omens, signs,

and superstitions, Cielo (2008) documents that “to be born during an eclipse, denotes hardship and

poverty”. The role of eclipses as sentinels of doom is deeply rooted into the Christian origins of Western

civilization, as a solar eclipse is believed to have accompanied Jesus’ death. Quoting from the gospel of

Luke:

“by now it was about midday and there came a darkness over the whole land, which lasted until three in the afternoon; the sun was in eclipse. And the curtain of the temple was torn in two.”

Indeed, Hand (1961-1964), in his collection Popular beliefs and superstitions from North Carolina, lists

eclipses among the doomsday signs. Similarly, Hand and Talley (1984), in their compilation of Popular

beliefs and superstitions from Utah, catalogue eclipses as one of the signs of the last days.

Interestingly, examining numerous surveys, Wilson (1977) and Boyer (1992) suggest that apocalyptic

conjectures are pervasive at a popular level, with millions of Americans presently holding beliefs

about an imminent destruction of the world. In particular, a 1994 survey has revealed that a

surprisingly large number of Americans believe the Bible should be taken literally when it speaks about

a Battle of Armageddon (44%) and a final Judgment Day (60%).3

As one may expect, superstitions about eclipses extend beyond American society. A popular

belief among Hispanic communities is that, when eclipses occur, pregnant women are in danger of

having a baby with a cleft lip and should stay inside their houses (Meyerson, 1990; Castro, 1995;                                                             3 U.S. News & World Report - December 19, 1994, p. 64.

[13] 

 

Franklin, 2005). Muslims discern the action of malevolent spirits in the obscurations of the sun and the

moon and indulge in ritual prayers during their occurrence (Zwemer, 2008). One of the two key books

of orthodox Judaism, the Talmud (Sukkah 29a), declares that “an eclipse of the sun is a bad omen for

the world”. In his collection of Chinese symbols and superstitions, Morgan (2004) recounts how solar

eclipses were considered as evil presages, as it was believed that the emperor could die or experience a

loss of virtue. In Hindu mythology, it is believed that some demons “biting” the sun or the moon are

responsible for the occurrence of eclipses, which are considered as inauspicious events (Dickens, 1996).

Millions of Indians still believe that some calamity will take place.4 In modern Thailand, devotees burn

black joss sticks and present offerings in the days before an eclipse, and when it occurs they clash

gongs and explode firecrackers to frighten “the demon that is biting“ the sun or the moon.5 Similarly, in

Cambodia thousands of people fire with handguns and rifles to scare the moon and “help the sun

escape”. In Japan, it was believed that eclipses were signs of a disease affecting the sun and that

protection was required in order to avoid incurring in such a disease. Milan (2000) documents that, in

our time, some people still cover their wells to prevent them from being poisoned by the “celestial

illness”.

In conclusion of this section, it is worth pointing out that, since modern science has proved that

eclipses are simply picturesque natural phenomena, only if people hold superstitious beliefs about these

events, then their occurrence can have an actual impact on some macro socioeconomic variables.

 

III. Media Coverage of Eclipses  

In our approach, to argue that eclipses induce negative sentiment in the superstitious that

reduces buying pressure in the stock market and equity returns, we require four key elements. First,

there must be evidence that the attitudes of a considerable fraction of the population are affected by

superstitious beliefs. Second, there must be evidence that eclipses, in particular, have spurred popular

superstitious beliefs that are likely to affect human behavior in a predictable way. Third, the

superstitious event in question must generate wide media coverage and draw public attention. Fourth,

media coverage and public attention should be highly clustered within a short time window around the

event, so that the impact on different people’s attitudes and decisions, if any, can be expected to take

place at nearly the same time. We have discussed the first two factors in sections I and II, and we now

address the last two.

                                                            4 The Independent, October 22, 1995. 5 Asiaweek - October, 20 1995. 

[14] 

 

Given the pervasiveness of television viewing within American society (Comstock et al., 1978;

Kubey and Csíkszentmihályi, 1990), we start our analysis by examining TV coverage of eclipses. We do

so by searching the Vanderbilt Television News Archive (a unit of the Vanderbilt University Libraries),

which contains detailed recordings of news broadcasts from the U.S. national television networks –

mainly ABC, CBS and NBC (since 1968), an hour per day of CNN (since 1995), and Fox news (since

2004). We search the database for news containing the keyword “solar eclipse” and repeat the exercise

using the keyword “lunar eclipse”. Adopting an event-study type of approach, we compute the average

number of news broadcasts containing these keywords that were transmitted on day t, t+1, t+2, and so

on over the period covered by the archive, where t represents the day on which a given eclipse has taken

place (according to New York City time).6 The results are reported in Figure 1, and show not only that

news about the occurrence of eclipses is reported by national TV networks, but also that coverage is

highly clustered in a 3-day window around the event.

We also examine other types of media coverage by making use of Google News Archive, a

news search engine that, as of today, continually monitors more than 4,500 news sources and also

extracts content from historical newspapers (Lloyd et al. 2005; Paterson, 2006; Lee et al., 2007). In

particular, we query the English-language sources of Google News Archive for the term “solar eclipse”

over the period 1950 through 2008, and repeat the same exercise with the term “lunar eclipse”.7 Though

the news sources in English included in Google News are not limited to the United States, casual

observation reveals that, especially over the period preceding the rise to mass popularity of the Internet,

the search engine mainly mines content from the historical archives of U.S. daily newspapers. As such,

we believe that the results, reported in Figure 2 using the same event-study style previously described,

represent a good proxy for press coverage in the United States of the eclipse phenomena. Even if our

raw keyword-search technique inevitably introduces some noise in the data, the picture clearly shows

that the number of news articles employing the expression “solar eclipse” (or “lunar eclipse”) is

disproportionately high in the days that strictly surround the eclipse event. We also try to divide the

sample into two sub-samples of similar length (1950-1978 and 1979-2008) and obtain analogous

outcomes.

Overall, these patterns suggest that eclipses attract substantial press coverage and, as such, we

can safely assume that the general public is aware of their occurrence. Furthermore, media (and likely

public) attention is highly concentrated around the event date, specifically on day t-1, t, and t+1.

                                                              6 Over the period covered by the TV news archive 182 eclipses occurred, 89 of which were solar. Eclipse data are described in section V. 7 Over this period, 263 eclipses took place, 129 of which were solar.

[15] 

 

IV. Hypotheses under Investigation Based on the evidence presented in the previous sections - namely that a considerable portion of

the population is influenced by superstition and that eclipses are associated with bad luck by the

superstitious - we conjecture here that, all else equal, the time an eclipse takes place is viewed as a non-

propitious time for starting an investment project by individuals who are sensitive to superstition. Once

they become aware that an eclipse is about to occur or has just occurred, these individuals will tend to

postpone the timing of their investment until confidence is regained.

In the spirit of Campbell et al. (1993), we suppose that the market is populated by some rational

risk-averse utility maximizers and some “noninformational” traders who desire to buy and sell stock for

exogenous reasons. We take a further step and assume that some of these noninformational traders are

influenced by superstitious beliefs. In turn, we propose that, when an eclipse takes place, the relative

buying pressure coming from noninformational traders will fall. Following Campbell et al. (1993), we

may also expect that rational traders will be willing to partly accommodate the emerging excess supply,

but will do so only if compensated with an adequate reward (i.e. a lower stock price). This leads us to

our first testable hypothesis, H1, as follows:

H1. The occurrence of inauspicious superstitious events (i.e. eclipses) exerts a negative impact on buying pressure in the stock market and on equity returns.

Based on the psychological evidence discussed in section I.B, we speculate that the extent to which

individuals’ attitudes are affected by superstition depends on the degree of uncertainty that they face in

the context in which they operate. This leads us to our second testable hypothesis, H2, as follows:

H2. The higher the uncertainty in the stock market, the greater the negative influence that inauspicious superstitious events exert on demand for stocks and on equity returns.

We also conjecture that, the wider the media coverage and public attention drawn by an eclipse, the

stronger the effect of superstition-induced behavior is on stock prices. In other words, we formulate our

third testable hypothesis, H3, in the following way:

H3. The wider the public attention drawn by a negative superstitious event, the stronger the negative impact on buying pressure in the stock market and on stock returns.

After the event, we may expect that the buying pressure from noninformational traders will return to

normal. Also, there will tend to be price increases on days following the event, as rational traders

demanded a higher expected return (i.e. a lower stock price) to partially absorb the excess supply that

[16] 

 

accompanied the superstitious event. In other words, we expect the market to rapidly adjust and to

observe a reversal effect. This directs us to our fourth testable hypothesis, H4, as follows:

H4. The market quickly adjusts after a negative superstitious event, and the initial negative reaction of stock prices is followed by a reversal effect.

It is worth emphasizing here that obscurations of the sun and the moon are perfectly predictable and, as

a result, only if the stock market is not entirely efficient one would expect to observe a systematic

relationship between the occurrence of eclipses and equity returns.8 As a result, the analysis that we

present seems particularly appealing: rejecting the null hypothesis of no link between eclipses and stock

returns would imply both that superstition plays a role in shaping financial decisions and that the market

exhibits some inefficiencies.

We derive a fifth implication by examining the dynamics of trading volume. If the buying

pressure originated by noninformational superstitious investors is reduced as a result of an eclipse taking

place, then, all else equal, we would expect that a drop in the size/quantity of buying orders should

translate into a trading volume decline. Based on this insight, we formulate our fifth testable hypothesis,

H5, as follows:

H5. By having a negative impact on buying pressure, inauspicious superstitious events reduce trading volume in the stock market.

 

V. Data In our empirical analysis we mainly focus on the U.S. stock market. We do so not only due to

the obvious relevance of this market, but also because U.S. stock return series cover an extremely long

time span, which compensates for the low frequency of the superstitious event under investigation. In

the last section of the paper, to provide more evidence in support of our hypothesis, we also examine

data from a sample of Asian countries. To ensure the robustness of our results, we employ several broad

indices of the U.S. market with a long history, namely Dow Jones Industrial Average, S&P500, NYSE

Composite, and Dow Jones Composite Average.9 We compute daily log returns using closing values of

                                                            8 NASA has already computed all eclipses that will be visible on Earth within the next two thousand years. See http://eclipse.gsfc.nasa.gov/eclipse.html. 9 Daily data about the NYSE Composite (code: NYSEALL) and Dow Jones Composite Average (code: DJCMP65) indices have been obtained from Datastream. Data about the S&P500 index from 12/31/1963 to 12/31/2008 have been obtained from Datastream (code: S&PCOMP), and I thank Richard Baillie for providing me with the data between 01/03/1930 and 12/30/1963. Daily data about the Dow Jones Industrial Average index from 01/02/1951 to 12/31/2008 have been acquired from Datastream (code: DJINDUS), and data between 10/1/1928 and 12/31/1950 have been collected from Yahoo! Finance (code: DJI).

[17] 

 

these indices. The longest sample we obtain covers a period of approximately 80 years, more

specifically from October 2, 1928 to December 31, 2008. Some descriptive statistics are reported in

Table I, which also describes the time series of stock returns for the ten Asian markets included in our

study.10

Information about eclipses has been acquired from NASA (see Footnote 8). Over the 80-year

period previously mentioned, 362 eclipses were visible somewhere on Earth (179 solar and 183 lunar

eclipses). We date eclipses according to date and time in New York City (EDT), and report in Figure 3

and Figure 4 their sample frequency by day of the week and calendar month, respectively. Based on

these two temporal parameters, the occurrence of these phenomena does not seem to follow any

particular pattern.11 Building upon the evidence presented earlier with regard to the media coverage of

eclipses, we construct a superstitious-event dummy variable that takes value 1 on day t-1, t, and t+1 and

0 otherwise, where t represents the day on which an eclipse takes place and t-1 and t+1 are the previous

and following trading day, respectively.12 Most of the empirical analysis will be implemented following

this approach, but in later sections of the paper we will also employ alternative definitions of the

superstitious-event dummy and show that the results are robust to the choice of the event window.

The control variables we employ are constructed using data from disparate sources. Following

Kamstra et al. (2003), we create two variables that are expected to capture the Seasonal Affective

Disorder effect in the U.S., i.e. a Fall dummy (DFall) and a variable SAD that measures the standardized

daily number of hours of night in New York City. Dichev and Janes (2003) and Yuan et al. (2006) have

provided some evidence that stock returns tend to be higher during new-moon periods than full-moon

periods. Since solar eclipses can only occur during a new moon whereas lunar eclipses only take place

during a full moon (Steel and Davies, 2001), we control for the effects of the lunar cycle by including

two dummy variables: a full-moon dummy (DFullMoon) that takes value 1 up to three days before and after

each full moon date and 0 otherwise, and a new-moon dummy (DNewMoon) that takes value 1 up to three

days before and after each new moon date.13 Empirical research has also discovered that solar eclipses

have a transient impact on several geophysical variables (Kotrappa et al., 1981; Strestik, 1999) and, at

the same time, behavioral finance researchers have documented that some environmental factors seem

to influence human decisions and stock returns (e.g. Keef and Roush, 2002; Hirshleifer and Shumway,

2003; Krivelyova and Robotti, 2003; Cao and Wei, 2004). To control for these influences, we obtain

                                                            10 Daily data for these countries have been collected from Datastream. For each Asian market we employ the country index calculated by Datastream (i.e. Datastream Total Market Index). The relevant codes are shown in the table. 11 The periodicity and recurrence of eclipses follows the so-called Saros cycle, a period of approximately 18 years, 11 days, and 8 hours. ”Any two eclipses separated by one Saros cycle share very similar geometries” (Espenak, 1987). 12 This means that if, for example, an eclipse occurs on a Sunday, then the dummy will take value 1 on the previous Friday and on the subsequent Monday. 13 Lunar phases have been obtained from NASA. See http://sunearth.gsfc.nasa.gov/eclipse/phase/phase2001gmt.html.

[18] 

 

daily data about average temperature, rainfall (a dummy, DRain, taking value 1 if a positive amount of

rain has fallen), average wind speed, and atmospheric pressure in New York City from the National

Climatic Data Center in Asheville, North Carolina. In the spirit of Krivelyova and Robotti (2003), we

also construct a variable GeoStorm that measures disturbances of the Earth’s magnetosphere using the

C9 geomagnetic index provided by the National Geophysical Data Center in Boulder, Colorado. To

control for well-known calendar anomalies, we create a Monday dummy (DMonday) taking value 1 on

Mondays and 0 otherwise (Gibbons and Hess, 1981; Ko et al., 1997), and a Tax dummy taking value 1

over the first seven days of January and 0 otherwise (Branch, 1977; Dyl, 1977). Finally, we obtain from

Datastream historical data on 10-year U.S. Treasury bond yields (code: FRTCM10) and 3-month

Treasury bill yields (code: FRTBS3M), and construct a variable Spread that measures the difference

between the two and provides a proxy for the slope of the U.S. yield curve. From Datastream we also

collect trading volume data (number of shares traded) for the U.S. stock market (code:

TOTMKUS[VO]).14

VI. Empirical Analysis A. Superstitious Events and Stock Returns: Hypothesis H1

We begin our analysis with a simple comparison between U.S. equity returns on “regular days”

and returns during superstitious events. More specifically, we estimate the following equation and

perform statistical inference using Newey-West (1987) adjusted standard errors (max 5 lags):

∑=

− +++=J

jttEventjtjt EventRR

1εγβα (1)

where Rt is the relevant index return on day t, Eventt is a dummy that captures the occurrence of

superstitious events (i.e. eclipses) and, as previously mentioned, takes value 1 on trading days t-1, t, and

t+1 (t is the day on which an eclipse occurs). A flexible number of lagged returns Rt-j has been added to

each regression to control for time serial correlation. In the case of the longest time series of returns

(D.J. Industrial Average index), 362 eclipses are included in the analysis. The results are reported in

Table III and provide some initial supporting evidence for our hypothesis H1. For all indices in question

the estimated coefficients on the event dummy are negative and, with the exception of the S&P500,

standard statistical significance is achieved. More specifically, over a 3-day time window centered

                                                            14 In particular, these data refer to the trading conducted on a thousand U.S. stocks included in the Datastream U.S. stock market index (code: TOTMKUS).

[19] 

 

around the eclipse event, stock returns are estimated to be approximately 6 basis points lower than

average.

 

B. Excluding Global Economic Shocks

It is natural to wonder whether the negative relationship just documented is the result of some

eclipses coinciding with major negative economic events. In the spirit of Yuan et al. (2006), we begin to

address this issue by excluding from the estimation some periods characterized by critical global

economic shocks.15 This is a conservative approach, as according to our superstition hypothesis we

expect to observe negative returns on the event days.

The outcomes of re-estimating model [1] when key global shocks are excluded are shown in

Table IV. Overall, the statistical significance of the results is similar to what discussed in the previous

section. Furthermore, the magnitude of the coefficients on the superstitious-event dummy, and thus their

economic significance, remains basically untouched. In the rest of the empirical analysis we will keep

excluding these key global shocks, yet the findings are qualitatively similar whether they are included or

not.

 

C. Controlling for Other Confounding Factors

To assess more precisely the effect of superstition-induced behavior on investment decisions, we

add to our model a set of controls that in the literature have been employed to capture some calendar

anomalies and behavioral effects. In particular, we run the following regression:

∑

∑ ∑

=−

= =−

++++++++

++++++++=

3

19876543

21

132

11

11

Prj

tjtjtFallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

εµµµµµµµ

µµγγγγβα (2)

where Dj

Month is a dummy variable that takes value 1 in the jth calendar month and 0 otherwise, and the

remaining variables have been defined in section V. The estimated coefficients, reported in Table V, tell

a consistent story. The marginal effect of the superstitious-event dummy is always estimated to be

                                                            15 In particular, we exclude the 2008 global financial crisis (September 2008 through December 2008), the 2003 invasion of Iraq (March 20, 2003 through May 1, 2003), the terrorist attacks on the twin towers (September 2001), the Russian crisis (August 11, 1998 through January 15, 1999), the Asian crisis (July 2, 1997 through December 3, 1997), the Mexican peso crisis (December 24, 1993 through January 31, 1994), the Gulf War (January 17, 1991 through February 17, 1991), the 1987 stock market crash (October 1987), World War II (September 1939 through August 1945), the 1929 Wall Street crash and the Great Depression (October 1929 through December 1932).

[20] 

 

negative, and its magnitude ranges between 5 and 10 basis points per day (over a 3-day window),

depending on the index in question. While only marginal statistical significance is reached in the case of

the S&P500 index (P-value: 0.13), for the other three indices the null hypothesis of no relationship

between the occurrence of eclipses and equity returns can always be rejected at very high confidence

levels. In terms of economic significance, the size of the superstition effect appears to be similar to the

one of the well-known Monday effect, which we also document here. Moving to the role played by the

other variables, we find that stock returns are consistently lower than average during rainy days and

during the fall.

D. Controlling for Economic News

Since the set of controls employed in model [2] does not contain any economic variable, one

may still wonder whether the results are driven by some negative economic news that, by pure chance,

happened to hit the market exactly when eclipses took place. To tackle this issue, one could pinpoint the

dates in which announcements about key macro indicators (e.g. GDP, inflation, unemployment, etc)

were made and employ the unexpected component of these indicators as an explanatory variable in

model [2]. Of course, in order to implement such a strategy, one would need a good measure of the

market participants’ expectations about these indicators (Rigobon and Sack, 2006). Since data about

agents’ expectations are not available for the most part of the sample period under scrutiny, we prefer to

follow an indirect approach here and take advantage of the expectations embedded in the term structure

of interest rates. It is well documented that the yield curve incorporates agents’ expectations about the

future dynamics of the economic system and its shape changes in response to changing expectations as

new pieces of information hit the market. In particular, numerous authors have suggested that the yield

spread between long-term and short-term Treasury bonds contains useful information for predicting

economic activity, future short-term interest rates and inflation.16 We therefore add to model [2] a proxy

for the slope of the U.S. yield curve, and run the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλ

µµµµµµµ

µµγγγγβα

+∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

21

3

19876543

21

132

11

11

Pr (3)

                                                            16 See, for example, Estrella and Hardouvelis (1991), Plosser and Rouwenhorst (1994), Estrella and Mishkin (1997), Dueker (1997), Estrella and Mishkin (1998), and Hamilton and Kim (2002).

[21] 

 

where Spreadt measures the yield spread between 10-year and 3-month U.S. Treasury bonds and

∆Spreadt measures the change in spread between day t-1 and day t.17

There are two main reasons why the slope of the yield curve can act as a suitable control in this

setting. First, according to the psychology of superstition, market participants in the bond market should

be less affected by superstitious attitudes than participants in the stock market. This follows from the

fact that in the former there is less uncertainty, and uncertainty is a key ingredient in the developing of

superstitious strategies, as discussed in section I.B. Second, even if market participants in the bond

market are partly influenced by superstition, we conjecture that, when eclipses take place, buying

pressure should tend to diminish nearly uniformly across all bond maturities, so that the slope of the

yield curve should be barely affected.18 As a result, the yield spread should mainly capture the flow of

relevant economic news that hits the market on a daily basis.

We display the estimated coefficients in Table VI. Consistently with our superstition hypothesis,

the marginal effect of the eclipse-event dummy is negative and highly statistically significant across all

four market indices. The magnitude of the superstition effect is estimated to range between 8 and 10

basis points per day (over a 3-day window), depending on the index at hand, which seems remarkable.

As for the remaining explanatory variables, the Monday and rain effects are found to be still

significant and, not surprisingly, we also find evidence of a systematic relationship between yield spread

and equity returns.

E. Robustness Checks

Although the evidence presented in the previous section appears to confirm that our findings are

not caused by the arrival of negative economic news, a legitimate question is whether a few extreme

observations may account for the results. To investigate this issue, we try to control for the role played

by the days featuring the most extreme positive and negative returns. To do so, we re-estimate model [3]

including a dummy variable, Extremet, that takes value 1 when Rt belongs to the 1% of observations

featuring the most extreme negative or positive returns, and 0 otherwise. We then repeat the same

exercise, where this time the dummy Extremet takes value 1 when Rt belongs to the 1% of observations

featuring the most extreme negative returns. In a third exercise, the dummy Extremet takes value 1 on

the five event days that feature the most extreme negative returns. The results of these three estimations,

limitedly to the coefficients of interest, are reported in Table VII. What emerges from these robustness

                                                            17 Both the augmented Dickey-Fuller and the Phillips-Perron test reject the null hypothesis of a unit root in the yield spread series at standard confidence levels. 18 In support of our intuition, when we try to regress the yield spread on the superstitious-event dummy (and a set of controls just like in equations [1], [2], and [3]) we find no evidence of a relationship between the two factors.

[22] 

 

tests is that the superstition effect remains largely significant in most instances, thus suggesting that the

findings are unlikely to be driven by outliers.

We also want to determine whether our results are robust to the choice of the superstitious-event

time window. Up to this point we have employed a dummy Eventt that takes value 1 on the trading days

t-1, t, and t+1 (where t represents the day on which an eclipse occurs), and 0 otherwise. As an

alternative, we re-estimate model [3] using a dummy Eventt that takes value 1 on the calendar days t-1,

t, and t+1.19 We repeat the exercise using a dummy Eventt that takes value 1 on the calendar days t-2, t-

1, t, and t+1. In a third exercise we limit our attention to those eclipses that occur on a day when the

market is open and employ a dummy Eventt that takes value 1 on the trading days t-1, t, and t+1. We

present the outcomes of these three estimations in Table VIII, which suggests that our findings are

largely robust to the definition of the event window. In particular, when we focus on those eclipses that

took place on weekdays when the market was open, the size of the superstition effect is estimated to

range between 11 and 14 basis points, depending on the index at hand, which is even larger than in the

baseline scenario.

As it is well known that equity returns exhibit time-varying volatility clustering, we perform an

additional test by estimating equation [3] using the following GARCH specification (Engle, 1982;

Bollerslev, 1986):

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλ

µµµµµµµ

µµγγγγβα

+∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

21

3

19876543

21

132

11

11

Pr (4)

tttt zzσε = ~ iid N(0,1)

2

122

1102

−− ++= tt σαεαασ Table IX displays the estimated coefficients, which in the case of the superstitious-event dummy are

once again highly statistically significant and match the earlier estimates in terms of sign and

magnitude. Taken as a whole, these tests provide evidence in favor of the robustness of our findings.

Equity returns are estimated to be below the average when eclipses occur. To make a case for causality,

we now turn to investigating the other three hypotheses introduced in section IV.

                                                            19 This implies that if, for example, the market is open on days t-1 and t, whereas it is closed on day t+1, then the superstition effect is assumed to work only during the first two days.

[23] 

 

VII. Investigating the Link of Causality  A. Uncertainty and Superstitious Attitudes: Hypothesis H2

In our hypothesis H2, following the evidence put forward in the fields of psychology and

anthropology, we have speculated that individuals are more likely to resort to superstitious behaviors

when facing high levels of uncertainty and experiencing an intense feeling of lack of control over the

outcomes. To measure the level of uncertainty prevailing in the stock market we employ a proxy based

on the volatility of equity returns. For each eclipse in the sample we calculate the standard deviation of

stock returns (using each of the four market indices) over the six months preceding the occurrence of the

eclipse, and then compute the median standard deviation. Then we sort eclipses into two approximately

equal-size groups: an eclipse falls into the high-volatility class if its corresponding standard deviation of

returns is greater or equal to the median standard deviation; if this is not the case, then it is classified

into the low-volatility group. We then define a superstitious-event dummy variable for each of the two

groups. A dummy EventtLowControl takes value 1 when Eventt takes value 1 and the eclipse in question

belongs to the high-volatility class, and 0 otherwise. A dummy EventtHighControl takes value 1 when Eventt

takes value 1 and the eclipse at hand belongs to the low-volatility group, and 0 otherwise.

We re-estimate model [4] after replacing the individual dummy Eventt with the two dummies

EventtLowControl and Eventt

HighControl, and report the results in Table X. What emerges is that the size of the

superstition effect is estimated to be about three times larger when eclipses take place in periods of high

market uncertainty (i.e. high variability of returns) than during calm phases. The negative marginal

effect of eclipses in times of high volatility is estimated to be around 0.15% per day (over a 3-day

window), and it is highly statistically significant for three of the market indices in question (marginal

significance is also achieved in the case of the S&P500). We also repeat this exercise computing the

standard deviation of returns over a three-month and twelve-month period, respectively. The estimates

we obtain are qualitatively very similar to the ones displayed in Table X.

Overall, these results are highly consistent with the view that uncertainty induces people to rely

more on superstition and, as such, provide support to our hypothesis.

B. Eclipse Magnitude and Media Coverage: Hypothesis H3

We have conjectured in our hypothesis H3 that the extent to which superstition-induced

behaviors affect prices in the stock market is likely to also depend on the degree of public attention that

a superstitious event draws. All else equal, taking as given the incidence of superstitious attitudes

[24] 

 

among the population, the greater the number of people that become aware of the occurrence of a

negative superstitious event, the greater the influence on the equity market that we expect to come from

the decisions of the superstitious. Though public attention to a given event is hard to measure directly, it

is likely to exhibit a positive relationship with the amount of media coverage (Ball-Rokeach, 1976; Behr

and Iyengar, 1985).

To test our hypothesis we employ a proxy for media coverage rather than a direct measure.20 In

particular, we choose to use the magnitude of an eclipse as a proxy for the media coverage and public

attention that the eclipse drew. In the case of solar eclipses, the magnitude measures the fraction of the

Sun’s diameter covered by the Moon at maximum eclipse, whereas for lunar eclipses it measures the

fraction of the Moon’s diameter covered by the Earth’s umbral shadow at maximum eclipse (Duffett-

Smith, 1990). Our supposition is that large-magnitude eclipses are likely to have a higher symbolic

meaning in the eyes of the superstitious and also draw more attention than small-magnitude ones as they

provide a more intense watching experience. We sort all eclipses in the sample into two categories based

on their magnitude: a solar (lunar) eclipse falls into the large-magnitude class if its magnitude is greater

or equal to the median magnitude of all solar (lunar) eclipses in the sample; if this is not the case, then

the eclipse falls into the small-magnitude class. To verify whether our intuition is correct – namely that

large-magnitude eclipses draw more media coverage and public attention than small-magnitude eclipses

– in Figure 5 we present a modified version of Figure 2 in which we sort press coverage based on the

two eclipse-magnitude categories just defined. The figure indeed reveals that large-magnitude eclipses

generate by far more news articles and, as such, we believe it is reasonable to assume that they also

draw more public attention.

We then re-estimate model [4] after replacing the dummy variable Eventt with two dummies,

BigEventt and SmallEventt, that capture large-magnitude and small-magnitude eclipse events,

respectively. The results are presented in Table XI. Even though the coefficients on the two event

dummies are not statistically different from each other at standard confidence levels, we can notice that

the coefficient on large-magnitude events is estimated to be sizably greater (around 30%) than the

coefficient on small-magnitude events with respect to all four market indices. Moreover, while the

former is highly statistically significant across the four indices, it is not so for the latter. Overall, these

findings are consistent with our initial hypothesis, and suggest that the relationship between eclipses and

stock returns is stronger when eclipses attract more media coverage, which is what would be expected in

the case of a causal link between the two.

                                                            20 The number of news sources from which Google News Archive mines content is not constant over time. Rather, it tends to shrink as the more distant past is searched for news articles. As such, simply counting the number of news articles referring to each given eclipse in the sample would produce a biased measure of media coverage.

[25] 

 

C. Reversal Effects: Hypothesis H4

If the lower-than-average stock returns that we document around eclipse dates were simply due

to negative economic news that, by pure chance, happened to hit the market when eclipses occurred,

then there would be no reason to expect an abnormal pattern of returns after the eclipse event. On the

other hand, if eclipses cause the superstitious to reduce demand in the stock market, then, as described

in our hypothesis H4, we would expect to observe that lower-than-average returns during the

superstitious event are followed by a reversal effect on the subsequent days.

We start exploring this issue through the help of some nonparametric tests and then conclude the

analysis with a regression model. Initially, we hypothesize a reversal-effect window of two days

(covering trading days t+2 and t+3) and later on we also test some alternative window lengths. For each

market index under scrutiny and for each eclipse in the sample we compute the mean return during the

superstitious-event window (trading days t-1, t, t+1) and the mean return during the reversal-effect

window. Using the Wilcoxon matched-pairs signed-rank test we then test the null hypothesis that the

average difference between these pairs of observations is equal to zero versus the alternative hypothesis

that mean returns are higher in the reversal window. We repeat the same exercise using a sign test that

considers the median difference between the two types of mean returns. The results are reported in

Table XII and show that, especially in the case of the sign test, the null hypothesis can always be

rejected at standard confidence levels in favor of the alternative that returns are higher over the reversal

window than over the eclipse-event window.

We also employ an alternative technique in the spirit of Kaplanski and Levy (2008). According

to our hypothesis H4 we would expect that mean returns below the median (-) over the superstitious-

event window are followed by mean returns above the median (+) over the reversal window. If equity

returns are random and are not affected by eclipses, then the frequency of (-,+) should be identical to the

frequency of (+,-). On the other hand, H4 implies that the frequency of (-,+) should be systematically

greater than the frequency of (+,-). Using the binomial distribution we test the null hypothesis of equal

frequency versus our alternative H4 and report the results in Table XIII. In the case of the S&P500,

NYSE, and D.J. Composite indices the null hypothesis can be rejected at standard confidence levels,

and also for the D.J. Industrial Average index there is marginal evidence in support of H4.

To control for potential confounding factors, we also estimate the following GARCH model:

[26] 

 

tttj

jtjt

Fallt

Fullmoont

NewMoonttt

Raintt

J

j

Taxt

Mondayt

j

MonthjtjtversaltEventjtjt

SpreadSpreadGeostormSAD

DDDWindessureDeTemperatur

DDDversalEventRR

ελλµµ

µµµµµµµ

γγγγγβα

+∆++++

++++++++

+++++++=

∑

∑ ∑

=−

= =−

21

3

198

7654321

132

11

11Re

Pr

Re

(5)

where Eventt is the superstitious-event dummy previously defined and Reversalt is a dummy that takes

value 1 on a certain number of trading days that follow the event and 0 otherwise. In particular, we

experiment with reversal windows of different lengths: one day (t+2), two days (t+2, t+3), and three

days (t+2, t+3, t+4). We also experiment with windows defined over trading days versus windows

defined over calendar days. The results for one-day and two-day reversal windows are shown in Table

XIV and consistently document that, after the event, a price correction of similar magnitude to the

original eclipse effect takes place. Below-average equity returns during the superstitious event are

immediately followed by above-average returns. These effects are statistically and economically

significant across market indices and definitions of reversal window. For example, in the case of the

NYSE Composite index, an initial negative marginal effect of about 9 basis points is followed by a

positive marginal effect of about 8 basis points over the 2-trading-day reversal window.

The reversal effect seems to take place entirely over a period of one to two trading days after the

event, as in the case of 3-day reversal windows (not reported in the table) we do not observe statistically

significant results. As such, the stock market appears to quickly correct the inefficiencies introduced by

the behavior of “irrational” agents whose decisions are influenced by superstition. In this sense, the

market is indeed efficient.

D. Trading Volume: Hypothesis 5

We now turn to examining the fifth hypothesis that we have put forward, namely that the

occurrence of negative superstitious events reduces demand in the stock market and, all else equal,

causes a trading volume decline. To analyze the dynamics of trading volume we initially employ a

simple specification in the spirit of Andersen (1996) and Yuan et al. (2006). In particular, to achieve

stationarity we detrend the trading volume series by dividing each daily observation by a monthly

moving average (i.e. a two-sided equally-weighted moving average of length twenty-two trading days),

as follows:

∑−=

+

= 11

11

1

jjt

tt

Volume

VolumeDV

[27] 

 

where Volumet measures the number of shares traded on day t. Then we run the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raintt

Taxt

Mondayt

j

MonthjtjtEventt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventDV

ελλ

µµµµµµµ

µµγγγγα

+∆++

++++++++

+++++++=

∑

∑

=−

=

21

3

19876543

2132

11

111

Pr (6)

where all variables have the same meaning previously discussed. The results are reported in Table XV

and reveal that the normalized measure of trading volume we have constructed exhibits a negative

relationship with the occurrence of eclipses. Since DV1t is on average equal to 1, loosely speaking when

negative superstitious events take place normalized trading volume tends to decrease by approximately

2%.

Following Gallant et al. (1992), Lo and Wang (2000), and Chuang and Lee (2006), we also try

to impose more structure on the trading volume data. More specifically, we employ the following

detrending formula:

  ⎟⎟⎠

⎞⎜⎜⎝

⎛−= ∑

−

−=+

1

22221ln)ln(

jjttt VolumeVolumeV  

and we further deseasonalize the above detrended trading volume series as follows:

⎟⎟⎠

⎞⎜⎜⎝

⎛+++−= ∑ ∑ ∑

= = =−

51

1

4

1 112

ˆˆˆˆj j

J

jjtjjtjjtjtt VWeekdayWeekVDV δγβα                                  (7) 

where Weekjt is a dummy taking value 1 on the jth week of the year and 0 otherwise, and Weekdayjt is a

dummy taking value 1 on the jth day of the week and 0 otherwise. The autoregressive terms Vt-j are

employed to filter out any serial correlation in the trading volume series.21 In the spirit of Chuang and

Lee (2006), we then estimate the following GARCH model:

tttj

jtj

tFallt

Fullmoont

NewMoonttt

Raintt

Taxt

J

jjtjtttEventt

SpreadSpreadGeostorm

SADDDDWindessure

DeTemperaturDRSDREventDV

ελλµ

µµµµµµ

µµγδδδγα

+∆+++

+++++++

++++++++=

∑

∑

=−

=−+

21

3

19

876543

2111

2212

Pr (8)

tttt zzσε = ~ iid N(0,1) 212

2110

2−− ++= tt σαεαασ

                                                            21 We add a number of autoregressive terms until we can no longer reject the null hypothesis of no serial correlation of the residual terms at the 1% confidence level based on the Ljung-Box Q-statistics.

[28] 

 

where |Rt| is the absolute value of Rt and SDt is the standard deviation of U.S. daily stock returns over

the previous month (i.e. twenty-two trading days).22 The outcomes are shown in Table XVI and,

consistently with hypothesis H4, suggest that the marginal effect of the superstitious-event dummy on

trading volume is negative. The size of the effect is similar to what has been found using the previous

model. In particular, when an eclipse occurs trading volume is estimated to decrease by approximately

1.5% with respect to its twenty-two-day moving-average value. To check the robustness of this result

we have repeated the same exercise using a two-month (45 trading days) and three-month (67 trading

days) trading volume moving average, respectively. The findings are qualitatively unchanged.

VIII. International Evidence

In this section of the paper we extend the analysis beyond the U.S. market by examining data

from a sample of ten countries in Eastern and Southeastern Asia, namely China, Hong Kong, India,

Indonesia, Japan, Malaysia, the Philippines, Singapore, Taiwan, and Thailand. Since the time series of

equity returns for these markets cover a relatively short period, to gain efficiency we estimate the

following pooled model:

∑

∑ ∑

=−

= =−

+++++++

++++++++=

3

1,,9,8,7,6,5,4

,21

,1,3,2

11

1,1,,,

jtijtijti

Fallti

Fullmoonti

NewMoontiti

Rainti

J

jti

Taxti

Mondayti

j

MonthjtijtiEventjtijti

GeostormSADDDDWind

DeTemperaturDDDEventRR

εµµµµµµ

µµγγγγβα (9)

where Ri,t measures the log return for country i at time t, and the remaining variables have the same

meaning previously described.23 Following the method proposed by Beck and Katz (1995), we compute

OLS parameter estimates and employ panel-corrected standard errors (PCSE) to perform statistical

inference. The PCSE design adjusts for heteroskedasticity and contemporaneous correlation across

panels, as well as for autocorrelation within each panel (Beck and Katz, 2004; Kristensen and Wawro,

2005). The results, not reported here due to space limitations, suggest that the superstitious event in

question has a negative marginal effect of approximately 8.5 basis points per day (over a three-day

window), which appears to be both economically and statistically significant (P-value: 0.07), and is

                                                            22 U.S. stock returns are computed here using the Datastream U.S. market index (code: TOTMKUS), which includes the stocks employed to measure trading volume in the U.S. stock market. 23 The variables measuring local atmospheric pressure and the yield spread are not included as these data are not available for most of the countries considered. To construct the Eventi,t dummy variable we date eclipses according to date and time in country i.

[29] 

 

comparable to the effect detected when using U.S. data. Similar estimates are obtained when country

fixed effects are included.

Overall, these patterns provide evidence in favor of the hypothesis that the superstitious beliefs

associated with eclipses influence financial behavior in Asian societies as well, which confirms and

strengthens the findings previously presented with regard to the United States.

IX. Concluding Remarks  

Psychology, sociology, and folkloristics document that superstition still enjoys surprisingly high

levels of popularity in modern Asian and Western societies, and it influences attitudes and decisions in

many spheres of daily life. In this paper we address the question of whether superstition plays a role in

affecting investment decisions in the U. S. stock market and in a sample of Asian countries. Our study is

among the first attempts conducted in this area of research and we believe that it contributes to the

existing literature on decision-making in several important ways. Our findings suggest that the time a

negative superstitious event occurs is viewed as a non-propitious time for starting an investment project

by individuals on whom superstition exerts an influence. More specifically, we find evidence consistent

with the hypothesis that demand for risky assets temporarily falls when negative superstitious events

take place, ultimately affecting equity returns. We also document that the price inefficiencies introduced

by this apparently irrational behavior are quickly absorbed by the market, which generates a transitory

reversal effect that could be profitably exploited by an informed trader.

 

 

 

 

 

 

 

 

 

 

[30] 

 

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01/06, Center for International Communications Research. Perkins B. (2002). Bottom Line Conjures Up Realty's Fear Of 13, Realty Times, 09/13/2002,

accessed on 04/14/2008. Phillips M. M. (2003). Superstitions abound at camp as soldiers await war in Iraq. The Wall Street

Journal, March 3, pp. 1. Pliny the Elder (2004). Natural history, English version, Oxford University Press.

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Plosser C. I., and K. G. Rouwenhorst (1994). International Term Structures and Real Economic Growth, Journal of Monetary Economics, 33, pp. 133–155.

Plutarch (2007). Lives of Nicias, Crassus, Aratus and Theseus, English version translated by J. Langhorne and W. Langhorne, Kissinger Publishing.

Rigobon R., and B. Sack (2006). Noisy macroeconomic announcements, monetary policy, and asset prices, Working paper, Sloan School of Management, MIT.

Scheibe E. Karl, and Theodore R. Sarbin (1965). Towards a Theoretical Conceptualisation of Superstition, The British Journal for the Philosophy of Science, 16, pp. 143-158.

Schippers M. C. And P. A.M. Van Lange (2005). The Psychological Benefits Of Superstitious Rituals In Top Sport, Erim Report Series Research In Management, Ers-2005-071-Org.

Skinner B. F. (1948). Superstition in the pigeon, Journal of Experimental Psychology, 38, pp. 168-172. Steel D. and P. Davies (2001). Eclipse: the celestial phenomenon that changed the course of history,

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Strestik J. (1999). The response of the 11 August 1999 total solar eclipse in the geomagnetic field, Earth Moon & Planets, 85, pp. 561-566.

Todd M. and C. Brown (2003). Characteristics Associated with Superstitious Behavior in Track and Field Athletes: Are There NCAA Divisional Level Differences?, Journal of Sport Behavior, Vol. 26,

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Publisher: Bibliolife.  

[35] 

 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

t-10 t-8 t-6 t-4 t-2

t

t+2

t+4

t+6

t+8

t+10

Ave

rage

Num

ber o

f N

ews

Bro

adca

sts

 

Figure 1. TV coverage of eclipses over the period 1968-2008, during which 182 eclipses occurred. The vertical axis measures the average number of news broadcasts per calendar day containing the keywords “solar eclipse” or “lunar eclipse” (depending on the type of eclipse that takes place), where t represents the day on which an eclipse occurs. TV coverage data have been obtained by searching the Vanderbilt Television News Archive, which contains detailed recordings of news broadcasts from the U.S. national television networks – mainly ABC, CBS and NBC (since 1968), an hour per day of CNN (since 1995), and Fox news (since 2004).

 

 

 

 

 Figure 2. Press coverage of eclipses over the period 1950-2008, during which 263 eclipses occurred. The vertical axis measures the average number of news articles per calendar day containing the keywords “solar eclipse” or “lunar eclipse” (depending on the type of eclipse that takes places), where t represents the day on which an eclipse occurs. Press coverage data have been obtained by searching Google News Archive.

 

 

[36] 

 

 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Monda

y

Tuesd

ay

Wed

nesd

ay

Thursd

ayFrid

ay

Saturda

y

Sunda

y

Freq

uenc

y

 Figure 3. Sample frequency of eclipses (solar and lunar) by day of the week over the period 1928-2008, during which 362 eclipses occurred.

 

 

0%1%2%3%4%5%6%7%8%9%

10%

Janu

ary

Februa

ryMarc

hApri

lMay

June Ju

ly

Augus

t

Septem

ber

Octobe

r

Novem

ber

Decem

ber

Freq

uenc

y

 Figure 4. Sample frequency of eclipses (solar and lunar) by calendar month over the period 1928-2008, during which 362 eclipses occurred.

 

 

 

[37] 

 

 

Figure 5. Press coverage of eclipses by eclipse magnitude over the period 1950-2008, during which 263 eclipses occurred. In the case of solar eclipses, the magnitude measures the fraction of the Sun’s diameter covered by the Moon at maximum eclipse, whereas for lunar eclipses it measures the fraction of the Moon’s diameter covered by the Earth’s umbral shadow at maximum eclipse. Eclipses whose magnitude is greater or equal to the sample median magnitude are classified into the large-magnitude category. Eclipses whose magnitude is less than the sample median magnitude are classified into the small-magnitude category. The vertical axis measures the average number of news articles per calendar day containing the keywords “solar eclipse” or “lunar eclipse” (depending on the type of eclipse that takes place), where t represents the day on which an eclipse occurs. Press coverage data have been obtained by searching Google News Archive.

 

 

 

 

 

 

 

 

 

 

 

 

 

[38] 

 

Table I Stock Returns – Descriptive Statistics

This table displays some summary statistics concerning the U.S. stock market indices and the Asian market indices employed in the empirical analysis. Returns are expressed in percentage points.

 Market Market

Index Time Span Mean

Standard Deviation

Min

Max Skewness Obs.

U.S. Dow Jones Industrial

10/02/1928-12/31/2008 0.018 1.16 -25.63 14.27 -0.62 20151

S&P500 01/03/1930-12/31/2008 0.012 1.14 -22.90 15.37 -0.40 19840

NYSE Composite

01/03/1966-12/31/2008 0.022 0.98 -21.29 11.53 -1.22 10844

Dow Jones Composite

01/04/1965-12/31/2008 0.021 0.97 -22.47 10.09 -1.26 11103

China TOTMKCA 01/03/1991-12/31/2008 0.031 2.21 -17.98 29.79 1.49 3592

Hong Kong TOTMKHK 01/02/1973-12/31/2008 0.034 1.84 -41.42 15.53 -1.67 8882

India TOTMKIN 01/02/1990-12/31/2008 0.056 1.85 -18.25 25.72 -0.07 4475

Indonesia TOTMKID 04/03/1990-12/31/2008 0.012 2.22 -52.94 52.24 -0.19 4557

Japan TOTMKJP 01/04/1973-12/31/2008 0.010 1.12 -15.74 12.29 -0.37 8882

Malaysia TOTMKMY 01/03/1986-12/31/2008 0.031 1.48 -22.21 20.39 -0.21 5673

Philippines TOTMKPH 09/10/1987-12/31/2008 0.037 1.45 -11.21 14.81 0.39 5281

Singapore TOTMKSG 01/02/1973-12/31/2008 0.011 1.39 -26.40 13.99 -0.79 9004

Taiwan TOTMKTA 09/10/1987-12/31/2008 0.009 2.02 -10.29 12.74 0.01 5209

Thailand TOTMKTH 01/05/1987-12/31/2008 0.026 1.88 -17.80 12.12 0.05 5417

 

 

 

 

[39] 

 

Table II Control Variables – Descriptive Statistics

This table reports some descriptive statistics about the control variables employed in the empirical analysis. Daily average temperature, rain, pressure, and wind speed are measured in New York City (LaGuardia airport). Rain is a dummy variable that takes value 1 if rain has occurred on a given day and 0 otherwise. The geomagnetic index C9 measures disturbances in the Earth’s magnetic field and varies between 0 (calm) and 9 (highly disturbed). The yield spread measures the difference between the yields on 10-year U.S. Treasury bonds and 3-month Treasury bills, and is a proxy for the slope of the U.S. yield curve.

 Market Index

Time Span Mean

Standard Deviation

Min

Max Obs.

Temperature (°F) 7/1/1948-12/31/2008 54.87 17.09 2.3 93.6 22097

Rain (dummy) 7/1/1948-12/31/2008 0.36 0.48 0 1 22097

Pressure (mb) 7/1/1948-12/31/2008 1016.9 7.56 977.5 1050.4 22097

Wind Speed (knots) 7/1/1948-12/31/2008 10.41 3.56 1.8 34.8 22097

Geomagnetic Index C9 (0-9 scale) 7/1/1948-12/31/2008 2.86 2.15 0 9 22091

Yield Spread (percentage points) 1/2/1962-12/31/2008 1.42 1.27 -3.73 6.1 11741

 

 

 

 

 

 

 

 

 

 

 

 

[40] 

 

Table III Superstitious Events and Stock Returns – Basic Model

This table displays the results of the following regression:

∑=

− +++=J

jttEventjtjt EventRR

1εγβα

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which an eclipse takes place) and 0 otherwise. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively.

 Index

Dow Jones Industrial

S&P500

NYSE Composite

Dow Jones Composite

Period (10/02/1928-12/31/2008)

(01/03/1930-12/31/2008)

(01/03/1966-12/31/2008)

(01/04/1965-12/31/2008)

α 0.021** (0.008)

0.014* (0.008)

0.025*** (0.009)

0.024*** (0.009)

β1 0.076*** (0.019)

0.062*** (0.016)

β2 -0.053** (0.027)

-0.041* (0.023)

γEvent -0.065* (0.036)

-0.052 (0.034)

-0.072* (0.040)

-0.077* (0.041)

Obs. 20151 19840 10842 11101

F 3.16 2.19 9.27 8.18

Number of Eclipses 362 356 190 194

 

 

 

 

 

 

 

 

 

[41] 

 

Table IV Superstitious Events and Stock Returns – Excluding Global Shocks

This table displays the results of the following regression:

∑=

− +++=J

jttEventjtjt EventRR

1εγβα

where we exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which an eclipse takes place) and 0 otherwise. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively.

 Index

Dow Jones Industrial

S&P500

NYSE Composite

Dow Jones Composite

Period (10/02/1928-12/31/2008)

(01/03/1930-12/31/2008)

(01/03/1966-12/31/2008)

(01/04/1965-12/31/2008)

α 0.033*** (0.007)

.024*** (0.008)

.027*** (0.008)

0.026*** (0.008)

β1 0.059*** (0.012)

.051*** (0.013)

.112*** (0.012)

0.091*** (0.012)

β2 -0.029** (0.012)

-.027** (0.013)

γEvent -0.063* (0.034)

-.044 (0.034)

-.088** (0.038)

-0.094** (0.038)

Obs. 17371 17166 10422 10681

F 10.91 6.04 46.69 32.14

 

 

 

 

 

 

 

 

 

 

[42] 

 

Table V Superstitious Events and Stock Returns – Controlling for Behavioral Variables

This table displays the results of the following regression:

∑

∑ ∑

=−

= =−

++++++++

++++++++=

3

19876543

21

132

11

11

Prj

tjtjtFallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

εµµµµµµµ

µµγγγγβα

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place) and 0 otherwise. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. Some coefficients are not reported due to space limitations.

 Index

Dow Jones Industrial

S&P500

NYSE Composite

Dow Jones Composite

α 0.671 (1.100)

1.095 (1.089)

-0.016 (1.275)

β1 0.071*** (0.011)

0.076*** (0.011)

0.110*** (0.012)

0.088*** (0.012)

β2 -0.030***

(0.011) -0.031***

(0.011)

γEvent -0.076** (0.033)

-0.051 (0.033)

-0.087** (0.039)

-0.100*** (0.040)

γ2 -0.093***

(0.019) -0.123***

(0.019) -0.099***

(0.022) -0.064***

(0.022)

γ3 0.163** (0.073)

0.075 (0.073)

0.070 (0.089)

0.178** (0.089)

µ1 0.001

(0.001) 0.001

(0.001) 0.001

(0.001) 0.001

(0.001)

µ2 -0.031** (0.015)

-0.034** (0.015)

-0.037** (0.018)

-0.031* (0.018)

µ3 -0.001 (0.001)

-0.001 (0.001)

0.0001 (0.001)

0.0005 (0.001)

µ4 0.003

(0.002) 0.002

(0.002) 0.006* (0.003)

0.005* (0.003)

µ5 0.028* (0.017)

0.024 (0.017)

0.020 (0.021)

0.024 (0.021)

µ6 0.012

(0.018) 0.014

(0.018) 0.009

(0.021) 0.027

(0.021)

µ7 -0.084** (0.033)

-0.080** (0.033)

-0.091** (0.039)

-0.093** (0.040)

µ8 -0.028 (0.037)

-0.042 (0.037)

-0.023 (0.044)

-0.011 (0.044)

Obs. 14792 14792 10415 10673

F 5.32 5.53 5.59 4.53

[43] 

 

Table VI Superstitious Events and Stock Returns – Controlling for Economic News

This table displays the results of the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλ

µµµµµµµ

µµγγγγβα

+∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

21

3

19876543

21

132

11

11

Pr

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place) and 0 otherwise. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. Some coefficients are not reported due to space limitations.

 Index

Dow Jones Industrial

S&P500

NYSE Composite

Dow Jones Composite

α -0.595 (1.333)

-0.094 (1.293)

β1 0.061*** (0.012)

0.071*** (0.012)

0.107*** (0.012)

0.085*** (0.012)

β2 -0.020* (0.012)

-0.021* (0.013)

γEvent -0.106***

(0.039) -0.080** (0.039)

-0.093** (0.040)

-0.104*** (0.041)

γ2 -0.064***

(0.023) -0.096***

(0.022) -0.103***

(0.022) -0.072***

(0.022)

γ3 0.177** (0.089)

0.063 (0.088)

0.065 (0.090)

0.175** (0.089)

µ1 0.002

(0.001) 0.001

(0.001) 0.001

(0.001) 0.001

(0.001)

µ2 -0.035* (0.018)

-0.036** (0.018)

-0.035** (0.018)

-0.028 (0.018)

µ3 0.0005 (0.001)

0.0002 (0.001)

0.0003 (0.001)

0.0005 (0.001)

µ4 0.005* (0.003)

0.004 (0.003)

0.005* (0.003)

0.004 (0.003)

µ5 0.035* (0.021)

0.029 (0.021)

0.021 (0.021)

0.024 (0.021)

µ6 0.023

(0.022) 0.024

(0.021) 0.013

(0.021) 0.029

(0.021)

µ7 -0.105***

(0.039) -0.097** (0.039)

-0.085** (0.039)

-0.085** (0.040)

µ8 0.006

(0.044) -0.012 (0.044)

-0.011 (0.044)

0.0003 (0.044)

λ1 0.015* (0.007)

0.014** (0.007)

0.014** (0.007)

0.015** (0.007)

λ2 0.007

(0.072) -0.027 (0.071)

-0.062 (0.068)

0.014 (0.071)

Obs. 11177 11177 10175 10413

F 3.36 3.50 4.98 4.09

[44] 

 

Table VII Superstitious Events and Stock Returns - Sensitivity to Outliers

This table displays the results of the following regression:

tttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

ExtremeSpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλλ

µµµµµµµ

µµγγγγβα

++∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

321

3

19876543

21

132

11

11

Pr

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place) and 0 otherwise. The third column reports the estimated coefficients on the superstitious-event dummy when, in the regression equation above, the dummy variable Extremet takes value 1 for the 1% of observations with the most extreme positive and negative returns. A similar interpretation applies to the fourth and fifth columns. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. The remaining coefficients are not reported due to space limitations.

 

Market Index Coefficient

Excluding 1% of observations with most extreme positive and

negative returns

Excluding 1% of observations with

most extreme negative returns

Excluding 5 event observations with most

extreme negative returns

Dow Jones Industrial

γEvent  -0.099*** (0.039)

-0.113*** (0.040)

-0.086** (0.038)

S&P500 γEvent  -0.073* (0.039)

-0.089** (0.039)

-0.060 (0.037)

NYSE Composite γEvent  -0.093** (0.040)

-0.098*** (0.039)

-0.075** (0.039)

Dow Jones Composite

γEvent  -0.103*** (0.040)

-0.114*** (0.039)

-0.083** (0.039)

 

 

 

 

 

 

 

 

 

 

[45] 

 

Table VIII Alternative Definitions of the Superstitious Event

This table displays the results of the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλ

µµµµµµµ

µµγγγγβα

+∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

21

3

19876543

21

132

11

11

Pr

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on a given set of days, according to the rule specified in the first row of the table, and 0 otherwise. For example, the fourth column reports the estimated coefficient on the superstitious-event dummy when Eventt takes value 1 on the calendar days t-2, t-1, t, and t+1, and 0 otherwise (t represents the calendar day on which an eclipse occurs). We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Newey-West standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. The remaining coefficients are not reported due to space limitations.

 

Market Index Coefficient Event = 1 on t-1, t, t+1

Event = 1 on t-2, t-1, t, t+1

Event = 1 on t-1, t, t+1

Calendar days Calendar days Trading days; only

eclipses occurring on days when market is open

Dow Jones Industrial

γEvent  -0.089** (0.042)

-0.086** (0.038)

-0.131*** (0.043)

S&P500 γEvent  -0.063 (0.042)

-0.060* (0.037)

-0.112*** (0.042)

NYSE Composite γEvent  -0.076* (0.043)

-0.069* (0.038)

-0.123*** (0.044)

Dow Jones Composite

γEvent  -0.108*** (0.043)

-0.096*** (0.037)

-0.141*** (0.044)

 

 

 

 

 

 

 

 

[46] 

 

Table IX Superstitious Events and Stock Returns – GARCH model

This table displays the results of the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raint

J

jt

Taxt

Mondayt

j

MonthjtjtEventjtjt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventRR

ελλ

µµµµµµµ

µµγγγγβα

+∆++

++++++++

++++++++=

∑

∑ ∑

=−

= =−

21

3

19876543

21

132

11

11

Pr

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), and 0 otherwise. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. Some coefficients are not reported due to space limitations.

 Index

Dow Jones Industrial

S&P500

NYSE Composite

Dow Jones Composite

α -0.343 (1.259)

0.199 (1.201)

-.001 (1.198)

-0.049 (1.234)

β1 0.084*** (0.017)

0.095*** (0.018)

0.129*** (0.017)

0.093*** (0.017)

γEvent -0.092***

(0.037) -0.063* (0.037)

-0.096*** (0.038)

-0.098*** (0.037)

γ2 -0.060***

(0.020) -0.079***

(0.019) -0.081***

(0.020) -0.064***

(0.020)

γ3 0.142* (0.086)

0.042 (0.084)

0.053 (0.087)

0.142* (0.082)

µ1 0.002

(0.001) 0.001

(0.001) 0.001

(0.001) 0.001

(0.001)

µ2 -0.028* (0.017)

-0.030* (0.017)

-0.027 (0.017)

-0.025 (0.017)

µ3 0.0002 (0.001)

-0.0002 (0.001)

0.0001 (0.001)

.0001 (0.001)

µ4 0.005* (0.003)

0.004 (0.003)

0.004 (0.003)

0.005* (0.003)

µ5 0.031

(0.019) 0.018

(0.019) 0.018

(0.019) 0.024

(0.019)

µ6 0.016

(0.020) 0.011

(0.019) 0.009

(0.019) 0.027

(0.019)

µ7 -0.074** (0.036)

-0.067** (0.035)

-0.060* (0.035)

-0.068* (0.036)

µ8 0.042

(0.042) 0.015

(0.042) -0.002 (0.043)

0.009 (0.043)

λ1 0.010

(0.006) 0.007

(0.006) 0.006

(0.006) 0.010

(0.006)

λ2 -0.007 (0.007)

-0.042 (0.071

-0.067 (0.063)

0.0001 (0.068)

Obs. 11177 11177 10175 10413

Wald χ2 87.89 84.16 111.43 88.46

 

[47] 

 

Table X Superstitious Events and Stock Returns – Effects of Uncertainty (GARCH model)

This table displays the results of the following regression:

tttj

jtjtFallt

Fullmoont

NewMoonttt

Raintt

Taxt

Mondayt

j

Monthjtj

J

j

lHighControtlHighContro

LowControltLowControljtjt

SpreadSpreadGeostormSADDD

DWindessureDeTemperaturD

DDEventEventRR

ελλµµµµ

µµµµµγ

γγγγβα

+∆++++++

+++++++

++++++=

∑

∑∑

=−

==−

21

3

19876

543213

2

11

11

1

Pr

where eclipses are sorted into two equal-size groups based on the degree of uncertainty in the stock market. In particular, Eventt

LowControl is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) in times of high stock market uncertainty. It takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), provided the standard deviation of stock returns over the six months preceding the eclipse is greater or equal to the median standard deviation; it takes value 0 otherwise. Eventt

HighControl is constructed in a similar way, but it captures the occurrence of superstitious events in times of relatively low stock market uncertainty (i.e. the standard deviation of stock returns over the previous six months is below the median standard deviation). We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. The remaining coefficients are not reported due to space limitations.

 

Market Index γLowControl γHighControl

Dow Jones Industrial -0.163** (0.071)

-0.045 (0.038)

S&P500 -0.114 (0.079)

-0.036 (0.036)

NYSE Composite -0.156** (0.069)

-0.045 (0.040)

Dow Jones Composite -0.160*** (0.064)

-0.050 (0.041)

 

 

 

 

 

 

 

 

 

[48] 

 

Table XI Superstitious Events and Stock Returns – Effects of Press Coverage (GARCH model)

This table displays the results of the following regression:

tttj

jtjtFallt

Fullmoont

NewMoonttt

Raintt

Taxt

Mondayt

J

j j

MonthjtjtSmallEventtBigEventjtjt

SpreadSpreadGeostormSADDD

DWindessureDeTemperaturD

DDSmallEventBigEventRR

ελλµµµµ

µµµµµγ

γγγγβα

+∆++++++

+++++++

++++++=

∑

∑ ∑

=−

= =−

21

3

19876

543213

21

11

11

Pr

where eclipses are sorted into two equal-size groups based on their magnitude, a proxy for press coverage. In particular, BigEventt is a dummy variable that captures the occurrence of superstitious events widely covered by the media (i.e. large-magnitude eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), and 0 otherwise. SmallEventt is constructed in a similar way, but it captures the occurrence of superstitious events less widely covered by the media (i.e. small-magnitude eclipses). We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. The remaining coefficients are not reported due to space limitations.

 

Market Index γSmallEvent γBigEvent

Dow Jones Industrial -0.077 (0.053)

-0.106** (0.049)

S&P500 -0.042 (0.052)

-0.082* (0.050)

NYSE Composite -0.081 (0.052)

-0.110** (0.054)

Dow Jones Composite -0.085 (0.053)

-0.110** (0.051)

 

 

 

 

 

 

 

 

 

 

 

[49] 

 

Table XII Superstitious Events and Stock Returns – Reversal Effects (Nonparametric Tests)

This table displays the results of testing the following hypothesis:

H0: RReversal – REvent = 0 versus the alternative:

Ha: RReversal – REvent > 0

where REvent measures the average return over the superstitious-event window (i.e. trading days t-1, t, t+1, where t represents the day on which an eclipse occurs) and RReversal measures the average return over the reversal window (trading days t+2, t+3). For each type of nonparametric test, the confidence level (P-value) at which the null hypothesis is rejected in favor of the alternative is reported next to each stock market index.

 

Market Index Obs. (Pairs)

Wilcoxon Matched-Pairs Signed-Rank Test

(P-value)

Sign Test (P-value)

Dow Jones Industrial 362 0.18 0.04

S&P500 356 0.18 0.05

NYSE Composite 190 0.03 0.08

Dow Jones Composite 194 0.03 0.08

 

Table XIII Superstitious Events and Stock Returns – Reversal Effects (Nonparametric Tests)

This table displays the results of testing the hypothesis asserting that returns below the median (-) during the superstitious-event window (i.e. trading days t-1, t, and t+1, where t represents the day on which an eclipse occurs) are followed by returns above the median (+) over the reversal window (i.e. trading days t+2, t+3). If returns follow a random walk and are not affected by the superstitious-event, then the probability of observing the pattern (-,+) should be identical to the probability of observing the pattern (+,-). As such, we test the following null hypothesis of randomness:

H0: frequency of (-,+) = frequency of (+,-)

versus the alternative: Ha: frequency of (-,+) > frequency of (+,-)

The confidence level (P-value) at which the null hypothesis is rejected in favor of the alternative is reported next to each stock market index.

 

Market Index Number of (-,+) Number of (+,-) Frequency Test (P-value)

Dow Jones Industrial 96 79 0.11

S&P500 99 78 0.07

NYSE Composite 51 35 0.05

Dow Jones Composite 57 37 0.02

[50] 

 

Table XIV Superstitious Events and Stock Returns – Reversal Effects (GARCH model)

This table displays the results of the following regression:

tttj

jtjt

Fallt

Fullmoont

NewMoonttt

Raintt

J

j

Taxt

Mondayt

j

MonthjtjtversaltEventjtjt

SpreadSpreadGeostormSAD

DDDWindessureDeTemperatur

DDDversalEventRR

ελλµµ

µµµµµµµ

γγγγγβα

+∆++++

++++++++

+++++++=

∑

∑ ∑

=−

= =−

21

3

198

7654321

132

11

11Re

Pr

Re

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), and 0 otherwise. Reversalt is a dummy variable that measures the occurrence of reversal effects and takes value 1 on a given set of days, according to the rule specified in the first row of the table, and 0 otherwise. For example, the fourth column reports the estimated coefficient on the superstitious-event dummy and the reversal dummy when Reversalt takes value 1 on trading days t+2 and t+3, and 0 otherwise. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Stock returns are computed in percentage points. Robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. The remaining coefficients are not reported due to space limitations.

 

Market Index Coefficient Reversal = 1 on trading day t+2

Reversal = 1 on trading days

t+2, t+3 Reversal = 1 on calendar day t+2

Reversal = 1 on calendar days

t+2, t+3

Dow Jones Industrial

γEvent  -0.087** (0.037)

-0.087** (0.037)

-0.085** (0.037)

-0.085** (0.037)

γReversal 0.114* (0.059)

0.076* (0.040)

0.184*** (0.063)

0.118*** (0.046)

S&P500

γEvent  -0.058 (0.037)

-0.058 (0.037)

-0.056 (0.037)

-0.056 (0.037)

γReversal  0.112* (0.061)

0.089** (0.040)

0.186*** (0.064)

0.126*** (0.046)

NYSE Composite

γEvent  -0.091** (0.039)

-0.091** (0.039)

-0.089** (0.038)

-0.088** (0.038)

γReversal  0.112* (0.062)

0.082** (0.042)

0.193*** (0.065)

0.131*** (0.048)

Dow Jones Composite

γEvent  -0.095** (0.038)

-0.095** (0.038)

-0.093** (0.038)

-0.093** (0.038)

γReversal  0.090 (0.062)

0.065 (0.043)

0.165** (0.067)

0.110** (0.048)

 

 

 

[51] 

 

Table XV Superstitious Events and Trading Volume – Basic Model

This table displays the results of the following regression:

∑−=

+

= 11

11

1

jjt

tt

Volume

VolumeDV

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raintt

Taxt

Mondayt

j

MonthjtjtEventt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDDDEventDV

ελλ

µµµµµµµ

µµγγγγα

+∆++

++++++++

+++++++=

∑

∑

=−

=

21

3

19876543

2132

11

111

Pr

where Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), and 0 otherwise. Volumet measures trading volume in the U.S. stock market on day t. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Newey-West robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. Some coefficients are not reported due to space limitations.

 

Coefficient Dep. Variable: Trading Volume

α 0.878*** (0.308)

γEvent -0.021** (0.009)

γ2 -0.106***

(0.004)

γ3 0.043** (0.021)

µ1 0.001*** (0.0003)

µ2 -0.001 (0.004)

µ3 0.0001 (0.002)

µ4 0.0004

(0.0007)

µ5 0.002

(0.005)

µ6 -0.003 (0.005)

µ7 0.136*** (0.016)

µ8 -0.004 (0.009)

λ1 -0.0006 (0.002)

λ2 0.014

(0.010)

Obs. 8478

F 34.75

[52] 

 

Table XVI Superstitious Events and Trading Volume – Deseasonalized Volume (GARCH model)

This table displays the results of the following regression:

ttt

jjtjt

Fallt

Fullmoont

NewMoonttt

Raintt

Taxt

J

jjtjtttEventt

SpreadSpread

GeostormSADDDDWindessure

DeTemperaturDRSDREventDV

ελλ

µµµµµµµ

µµγδδδγα

+∆++

++++++++

++++++++=

∑

∑

=−

=−+

21

3

19876543

2111

2212

Pr

where DV2t is a detrended and deseasonalized measure of trading volume that has been defined in equation [7], and Eventt is a dummy variable that captures the occurrence of superstitious events (i.e. eclipses) and takes value 1 on trading days t-1, t, and t+1 (t is the day on which the eclipse takes place), and 0 otherwise. We exclude from the estimation the days on which the global shocks discussed in section VI.B took place. Robust standard errors are shown in parenthesis. One, two, and three asterisks denote statistical significance at the ten, five, and one percent level, respectively. Some coefficients are not reported due to space limitations.

 

Coefficient Dep. Variable: Trading Volume

α 0.220 (0.264)

γEvent -0.015** (0.008)

δ1 0.067*** (0.004)

δ2 -0.060***

(0.006)

δ3 0.012*** (0.003)

γ1 0.023

(0.025)

µ1 0.0001

(0.0002)

µ2 -0.003 (0.005)

µ3 -0.0002 (0.0003)

µ4 0.0001

(0.0006)

µ5 -0.0004 (0.005)

µ6 -0.001 (0.004)

µ7 0.018*** (0.007)

µ8 -0.003 (0.004)

λ1 -0.002 (0.002)

λ2 0.004

(0.008)

Obs. 8439

Wald χ2 495.81