Robust global mood influences in equity pricing

Transcription

1 Available online at J. of Multi. Fin. Manag. 18 (2008) Robust global mood influences in equity pricing Michael Dowling 1, Brian M. Lucey Institute for International Integration Studies, Trinity College Dublin, Dublin 2, Ireland Received 21 September 2005; accepted 27 June 2007 Available online 5 July 2007 Abstract This paper investigates the relationship between seven mood-proxy variables and a global equity dataset using a variety of group tests. The mood-proxy variables are constructed from weather data (precipitation, temperature, wind, geomagnetic storms) and biorhythm data (seasonal affective disorder, daylight savings time changes, lunar phases). This study contributes a greater understanding of the relationship between mood and equity pricing through testing the strength of the relationship between groups of mood-proxy variables and both returns and variance. Using a large and globally diverse equity dataset, robust econometric testing approaches, and testing deseasonalised and regular weather variables, we conclude that seasonal affective disorder and low temperatures show the greatest relationship with equity pricing Elsevier B.V. All rights reserved. JEL classification: G12; G14 Keywords: Mood; Market efficiency; Seasonal variation 1. Introduction Recent research in behavioural finance has tested for evidence of mood misattribution influencing investor decision-making, that is, investors allowing irrelevant mood states to influence their investment decisions. The approach adopted is to test for a relationship between widely experienced mood-proxy variables and equity returns. Variables ranging from weather, to seasonal affective disorder (SAD), to sporting events, have all been tested in the process of these investigations (e.g. Hirshleifer and Shumway, 2003; Kamstra et al., 2003). Corresponding author. Tel.: ; fax: addresses: Michael.Dowling@tcd.ie (M. Dowling), blucey@tcd.ie (B.M. Lucey). 1 Tel.: X/$ see front matter 2007 Elsevier B.V. All rights reserved. doi: /j.mulfin

2 146 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) These mood misattribution studies rely on the hypothesis that individuals making decisions involving risk and uncertainty allow their emotional state to influence their decisions (Loewenstein et al., 2001). Even irrelevant temporary mood states at the time of decision-making have been shown to influence decisions involving the weighing of long-term risks and benefits (Schwarz and Clore, 1983). This paper conducts one of the most comprehensive analyses on the influence of mood-proxy variables on equity pricing. We use a global range of 37 national equity market indices and 21 national small capitalisation indices. Our study tests the mood-proxy variables of SAD, daylight savings time changes, temperature, wind, precipitation, lunar phases, and geomagnetic storms. In addition to the breath of equity markets and mood-proxy variables tested; our study makes a number of important contributions to this area of research. (1) We test the influence of the mood-proxy variables on both returns and variance. Previous research has tended to concentrate solely on returns. (2) We address econometric robustness issues arising in some of the previous research. (3) We investigate whether there is a differential effect between the influence of mood-proxy variables on small capitalisation equities versus large capitalisation equities. (4) We test groups of mood-proxy variables, thus allowing us to investigate whether the previously discovered relationships in individual tests still exist when a mood-proxy variable is just one of a group of variables being tested. Our findings indicate that SAD is the most important mood-proxy variable, while a number of the other variables are dismissed as being good proxies for mood influences on investor decisionmaking. We also find evidence of a relationship between some mood-proxy variables and equity return variance. The outline of the paper is as follows. Section 2 reviews the important literature on mood influences on equity pricing. Section 3 outlines the data and the testing approach. Section 4 reports and analyses the findings. Section 5 offers some conclusions. 2. Mood influences on equity pricing The theory of why investor mood might influence equity pricing draws on findings and theoretical work in the fields of emotion psychology, decision-making, and behavioural finance. This section reviews some of the most pertinent literature from these areas Mood and decision-making The seminal work of Damasio (1994) showed that emotions play a vital role in decision-making by studying people with an impaired ability to experience emotion. People with impaired ability to experience emotions have difficulty making decisions and tend to make suboptimal decisions. Some specific decision-making models which incorporate mood into the decision-making process are the risk-as-feelings model and the mood-as-information model. The risk-as-feelings model was developed by Loewenstein et al. (2001) primarily to incorporate the fact that the emotions people experience at the time of making a decision influence their eventual decision. The model, which is developed based on a meta-analysis of over 500 studies of mood and decisionmaking, argues that every aspect of the decision-making process is influenced by the feelings of the decision-maker. Schwarz and colleagues have developed a similar model which they term 2 In this section, the terms emotion, mood and feelings are used interchangeably as there is no firm distinction in the psychological literature.

3 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) mood-as-information. The mood-as-information model states that when we are making a decision we ask ourselves How do I feel about it? and this guides our eventual decision (Schwarz, 1990). Generally, it can be stated that factors that induce positive mood in people lead them to make more optimistic judgements than if they were in a neutral mood, while factors that induce negative mood in people lead them to make more pessimistic judgements than if they were in a neutral mood. A key element of the mood-as-information hypothesis, is that mood tends to inform decisions even when the cause of the mood is unrelated to the decision being made. This phenomenon is labelled mood misattribution Mood and investor decision-making Weather is a comprehensively researched source of misattributed mood in equity pricing studies. Howarth and Hoffman (1984) summarise research on the weather and mood as; weather variables affect an individual s emotional state or mood, which creates a predisposition to engage in particular behaviours (p. 15). The essential finding of this area is that, across a wide range of weather variables good weather induces positive mood states and bad weather induces negative mood states. Some specific findings on the relationship between weather and mood of relevance to this paper include the following. Sunshine, cloud cover, and precipitation (which are normally grouped together as both sunshine levels and precipitation are a function of cloud cover) are related to good moods in times of high amounts of sunshine and low cloud cover levels, while bad moods and depression are caused by low sunshine, high cloud cover and the presence of precipitation (e.g. Eagles, 1994). Good moods are associated with optimism, while depression is associated with pessimism. High and low temperatures are related to aggression (Rotton and Cohn, 2000). The negative emotion of aggression leads to some of the same action tendencies as positive emotions (Lerner and Keltner, 2001), thus high and low temperatures cannot be classified in the same category as (say) high cloud cover which is related to the negative emotion of depression. Geomagnetic storms are associated with increased level of depression and anxiety (Nastos et al., 2006). The relationship between wind and mood is not researched as widely as other weather variables, but it seems to be negatively related to mood (Cooke et al., 2000). Studies on the relationship between sunshine, cloud cover, precipitation and equity returns have indicated the presence of such a relationship. Saunders (1993) found, for a study of cloud cover in New York and New York traded equities, that days with 100 percent cloud cover were related to below average equity returns, while low cloud cover percentages (0 20 percent) were associated with above average equity returns. Hirshleifer and Shumway (2003) replicate the study of Saunders over the index returns of 26 international stock exchanges from 1982 to Using a deseasonalised 3 measure of cloud cover that accounts for the seasonal variation in levels of cloud cover they find that 25 out of 26 indices are found to be negatively related, 9 of them significantly, 3 The deseasonalised measure of cloud cover is calculated through first calculating the average cloudiness for each week for each city over the full sample period, and then subtracting this value from the actual daily cloudiness figures to give the deseasonalised cloud cover value for each day. Loughran and Schultz (2004) note some possible criticisms of this approach: (1) there is no psychological literature showing that people seasonalise weather before reacting to it, and (2) it introduces a look-ahead bias, i.e. investor decisions at the beginning of the time period are assumed to condition weather reactions on future weather.

4 148 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) to increases in cloud cover. A large body of replication studies have attempted to confirm the Hirshleifer and Shumway (2003) and Saunders (1993) findings in different markets and using alternatives to aggregate equity prices, with mixed, but generally favourable results. Cao and Wei (2005) study the relationship between temperature and equity returns. They test for a relationship between returns on 27 national equity indices and low and high temperatures (low and high temperatures are defined relative to a comfortable temperature of degrees Celsius). They find that low temperatures are related to high returns, while high temperatures are weakly related to lower returns. This supports their hypothesis of low temperatures being associated with aggression, and thus equities being bid higher, while high temperatures are associated with both aggression and apathy, and thus there are no strong expectations of a relationship in any direction. Some difficulties with this research include: no adjustment is made for possible ARCH effects in the equity data, and both amount of daylight and temperature variables are included in the same regression despite the close relationship between these two variables and thus the possibility of multicolinearity (Jacobsen and Marquering, 2004). There have been some tests of a relationship between wind and financial instrument pricing (Keef and Roush, 2005; Limpaphayom et al., 2005). Limpaphayom et al. (2005) test the relationship between wind speed and the trading performance of traders on the Chicago Mercantile Exchange. They find that the bid-ask spread widens on windy days, while morning high wind speed is associated with trade imbalance and lower trader income in the afternoon. Krivelyova and Robotti (2003) test whether there is a relationship between geomagnetic storms and equity returns. The researchers test whether the depression and anxiety caused by high levels of geomagnetic activity is linked to lower equity returns. They conduct their study over 12 equity indices in 9 countries, and find that returns are significantly lower (at 5 percent level of significance) for 4 of the 9 countries, and also lower for small capitalisation equities. A second area of research on mood misattribution studies in equity pricing relates to biorhythms. Biorhythms are the body s natural biological cycles. These cycles have been linked to mood moderation and fluctuation in a large body of psychological research. Three biorhythms are studied in this paper: seasonal affective disorder (SAD), daylight savings time changes (DSTC), and lunar phases. SAD is a circannual (yearly) cycle. The seasonal variation in mood throughout the year caused by the variation in the hours of daylight in the day has been linked to depression in the period between the autumn and spring solstice (Rosenthal, 1998). SAD varies based on latitude, with more extreme latitudes experiencing more extreme seasonal depression (e.g. Heerlein et al., 2006). For example, a survey of a sample of the Swedish population found that 19.3 percent of people believed their lives were negatively affected by winter depression (Rastad et al., 2005), while no significant pattern of seasonal depression was found in Melbourne, Australia (Murray, 2004). DSTC is a circadian (daily) cycle, where the sleep disruption caused by DSTC is associated with increased anxiety and depression (e.g. Coren, 1996). Lunar phases have been popularly linked to mood variation, with the full moon being associated with increased levels of manic behaviour. Most psychologists, however, dismiss such a link (Rotton and Kelly, 1985). The continued presence of a popular belief in the negative mood relationship with full moons (e.g. Vance, 1995) might be a cause of mood variation based on a lunar phases cycle.

5 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) Kamstra et al. (2003) investigate whether there is a relationship between the seasonal variation in mood caused by SAD and equity pricing. They hypothesise that the reduction in the hours of daylight from the autumn equinox will be associated with lower returns as investor depression leads them to avoid risk, while the increase in hours starting from the winter solstice through to the spring equinox will be associated with higher returns as investor mood heightens and they become more willing to reassume risk in their investments. Their constructed variable to measure SAD accounts for some latitude-based variation in SAD influence, but assumes a linear relationship. They find supportive evidence for their hypotheses, including a SAD relationship that is reversed in the Southern Hemisphere compared to the Northern Hemisphere, which is in accordance with the seasons in the two hemispheres being reversed. Kelly and Meschke (2005) criticise the SAD findings of Kamstra et al. (2003), and others, due to the excessive reliance of the findings on the positive returns in the period from December 21st to January 20th, where there is an already known positive returns anomaly (Hawawini et al., 2000). Kamstra et al. (2000) investigate the relationship between DSTC and equity returns using data for the US, Canadian, German and UK markets (for at least ) to test whether the mean of the returns for the two daylight savings time changes weekends is significantly different from regular weekend returns. The results confirmed that returns for daylight savings time changes weekends are significantly more negative than regular weekend returns (with the exception of Germany). However, Pinegar (2002) argues that the DSTC effect is significant only for the autumn change, and, even at that, the effect is driven by two major outliers. In particular, the 1987 DSTC change was temporally close to the 1987 stock market crash, leading Pinegar to suggest that the effect may be a data artefact. Kamstra et al. (2002) refute this argument, based on methodological and international evidence. Despite the weak psychological evidence of a relationship between lunar phases and mood, some researchers (Dichev and Janes, 2003; Yuan et al., 2006) have investigated whether equity returns are higher in the days surrounding new moon dates than in the days surrounding full moon dates. These studies find broadly supportive evidence of new moon dates being associated with higher returns than full moon dates, but tend to concentrate on basic OLS methods of testing. 3. Data and testing approach 3.1. Equity data We collected daily stock returns for 37 country indices from Datastream from 12th December 1994 to 10th November Each index has 2588 observations. We also collected daily stock returns for the 21 available MSCI small capitalisation indices for the same period. Summary statistics (which are unreported due to space constraints) show the data to be non-normal. Further (unreported) tests, including an examination of Autocorrelation Function/Partial Autocorrelation Function tables, Ljung-Box Q-statistics, and Engle s ARCH test, lead to a diagnosis of ARCH effects in the equity pricing data. This leads us to adopt an approach of identifying an appropriate GARCH specification for each equity index. To specify the most appropriate GARCH specification for each index, we test each equity pricing series against a range of 13 GARCH specifications and select the most appropriate based on Log Likelihood Ratio tests (LLRT). The LLRT allows the selection of the best GARCH specification, taking into account the principle of parsimony. Three basic GARCH(1,1) specifications are tested including the addition of an Arch-in-Mean term (Engle et al., 1987), and an ARMA(1,1). Five Leveraged-GARCH specifications are tested following Glosten et al. (1993) which include relaxing the assumption of normal distribution

6 150 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) of errors (Student s t-distribution and generalised error distribution (GED) are the alternatives tested), and, once again, the addition of an Arch-in-Mean term. The final group of five GARCH specifications follow the Exponential-GARCH of Nelson (1991), with the sub-specifications being the same as those tested for Leveraged-GARCH. The (unreported) LLRTs show that either L-GARCH or E-GARCH is the preferred GARCH specification for all the indices, but that there is a wide distribution of preference between the sub-specifications. This indicates that studies that use only one GARCH specification over a large number of equity indices run the risk of not optimally specifying all of their equity pricing series Mood-proxy data Seven mood-proxy variables are tested. These include the weather variables of temperature, precipitation, wind and geomagnetic storms, and the biorhythm variables of SAD, DSTC, and lunar phases. The mood-proxy variables are collected and calculated using a variety of approaches. Daily temperature, wind, precipitation, and geomagnetic storms data are obtained from the National Oceanic and Atmospheric Administration. We use the Global Summary of Day database to extract the wind, precipitation and temperature data, and the AP Index to construct the geomagnetic storm variable. The following variables are constructed: Basic wind, temperature, precipitation: These three variables simply use the raw weather files in their construction. Deseasonalised wind, temperature, precipitation: We follow Hirshleifer and Shumway (2003) in constructing deseasonalised measures of each of the three weather variables. The approach adopted is to calculate an average value for a weather variable for a particular country for a each month over the whole dataset, and then subtract the average from the actual value on a given day to get the deseasonalised value. Extreme temperature: Two variables are constructed of high and low temperatures. The high temperature variable is a dummy variable that equals 1 when temperature is in the top 10 percent of temperatures for a country for the time period. The low temperature variable covers days when temperature is in the bottom 10 percent. Geomagnetic storms: The variable constructed is a dummy variable that has a value of 1 if there has been an AP Index value above 29 (indicating a mild geomagnetic storm) in the past 1 6 days. The SAD variable is calculated based on a formula in Kamstra et al. (2003). This formula gives a measure of the reduction in the hours of daylight in the day from the Autumn Equinox to the Winter Solstice, and the lengthening of the day after the Winter Solstice up to the Spring Equinox. 5 The SAD variable is calculated as: [ ( ) ] 2πδ arcos tan tan(λ) (1) All details of specifications and unreported tests detailed in this section are available from the authors on request. 5 The actual dates of the Autumn Equinox (AE), Winter Solstice (WS) and Spring Equinox (SE) depend on whether a country is based in the Northern or Southern Hemisphere due to the reversal of seasons. In the Northern Hemisphere AE is September 21st, WS is December 21st, and SE is March 20th. In the Southern Hemisphere AE is March 21st, WS is June 21st, and SE is September 20th.

7 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) where δ represents the latitude north of the location from the equator; and λ represents the sun s declination angle; the angle between the sun s rays and the earth s surface on any given day throughout the year. λ is calculated as: sin [( 2π 365 ) (julian 80.25) ] where julian stands for the number of the day in the year numbered from 1 to 365. These calculations give a figure for the number of hours of night for each day. This figure is subtracted from 12 to give the deviation from the normal number of hours in the night. The SAD variable takes a value according to the Kamstra et al. (2003) formula for periods between the Autumn Equinox and the Spring Equinox, and takes a value of 0 otherwise (this variable is called SAD Winter ). To capture possible asynchronous effects of SAD between Autumn and Winter, a dummy variable is also created which takes a value of 1 for periods between the Autumn Equinox and the Winter Solstice, and 0 otherwise (called SAD Fall ). DSTC occurs on two occasions per year. Once in the Spring, when clocks go forward, and once in the Autumn, when clocks go back 1 h. The actual date of DSTC varies from country to country and does not occur in some countries. Accurate details of these changes were obtained from The DSTC variable is a dummy variable, taking a value of 1 on a Monday following a DSTC, and 0 otherwise. We collected data on lunar phases from This gave us full moon dates. Based on this data and a formula in Yuan et al. (2006) we were able to give a value to each day in our dataset based on how close the day is to the full moon. The variable varies between 1 (full moon) and 1 (new moon). Each day has a value based on how close it is to a full moon. The formula is: ( ) 2πd cosine (3) where d is the number of days since the previous full moon Testing approach The testing approach consists of group tests of the mood-proxy variables using Maximum Likelihood Estimates of the GARCH specifications discussed in Section 3.1. We also include in each country regression a dummy variable to deal with the known Monday effect, and the Datastream World index returns variable. The World Index is included to remove some of the effects of global economic changes from the local index movement. We expect that the effect of local mood factors (if present) will be most likely to affect the local component of returns. The mood factors are included both as regressors in the mean equation as well as the variance equation. This allows us to test for the relationship between the mood factors and both returns and variance. An issue including all mood-proxy variables in the same group test is the possibility of multicolinearity, especially between the SAD variable and the weather variables of temperature, wind and precipitation. Unreported tests confirm the presence of this correlation issue in our mood-proxy dataset. To address this issue the weather and SAD variables are run in separate group tests. This gives the following group tests: (2)

8 152 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) Basic Wind, Basic Temperature, Basic Precipitation, Geomagnetic, DSTC, Lunar. 2. Deseas Wind, Deseas Temperature, Deseas Precipitation, Geomagnetic, DSTC, Lunar. 3. Top 10% Temperature, Bottom 10% Temperature, Geomagnetic, DSTC, Lunar. 4. SAD Fall, SAD Winter, DSTC, Lunar. 4. Results and analysis The results from the group tests are reported in Tables 1 4. For ease of discussion, the results for the individual mood-proxy variables are presented in separate tables. The geomagnetic, DSTC and lunar variables were tested in multiple group tests, but only the results for DSTC and lunar from Group Test 4 are reported, and only the geomagnetic results from Group Test 1 are reported Temperature The temperature results are reported in Tables 1a and 1b. These results cover the tests of the regular temperature, deseasonalised temperature, and top 10 percent/bottom 10 percent of temperature days. For regular temperature there is some evidence of a negative relationship between temperature and equity returns. In the main indices, 62 percent of coefficients for the relationship with returns are negative and 11 of the 34 coefficients are significantly negative at the 10 percent level. This negative direction is more pronounced for the small capitalisation indices, where 80 percent of coefficients are negative and 10 of the 20 coefficients are significantly negative. It would be expected that any relationship with a mood-proxy variable would be more pronounced in small capitalisation equity pricing due to the greater influence of small investors in the pricing of these equities (Gompers and Metrick, 2001), and the greater likelihood of investors with limited knowledge, such as small investors, allowing mood to play a role in their decision-making (Forgas, 1995). This negative direction of relationship might be caused by low temperatures being associated with high returns, high temperatures being associated with low returns, or a combination of these two factors. The results from the high temperature and low temperature dummy variables provide some further information on the reason for the negative relationship found for the regular temperature variable. There is no evidence of any relationship between the Temp High variable and main indices equity returns, however the small capitalisation indices show some trend towards high temperatures being associated with lower returns (65 percent of coefficients negative). The Temp Low variable indicates a mild positive relationship between low temperatures and equity returns (59 percent main indices, 3 significant; 60 percent small capitalisation indices, 4 significant). This suggests that the negative relationship suggested by the regular temperature results is more likely to be caused by low temperatures being positively related to returns, than being caused by a relationship between high temperatures and equity returns. The deseasonalised temperature results do not show any trend, which indicates that if investors are psychologically affected to any extent by temperature, then it is only by raw temperature and not by a seasonally adjusted measure. Similarly, there is no strong direction evident in the results for the relationship between temperature and variance. These results are quite similar to the direction of the findings of Cao and Wei (2005), although, our findings are not as strong as Cao and Wei s. This is possibly because of the inclusion of the GARCH specifications or the World Index.

9 Table 1a Temperature findings for main country indices Index Returns Variance Temp Temp High Temp Low Temp DE Temp Temp High Temp Low Temp DE Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Argentina Austria Belgium Canada Chile China Denmark Finland France Germany Greece Hong Kong Indonesia Ireland Italy Japan Korea Luxm borg Malaysia Mexico Netherland N. Zealand Australia Philippines Portugal South Africa Singapore Spain Sweden Switzerland Taiwan Turkey UK US See notes to Table 1b for further information on this table. M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008)

10 Table 1b Temperature findings for small capitalisation indices Index Returns Variance Temp Temp High Temp Low Temp DE Temp Temp High Temp Low Temp DE Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig AustriaSml BelgiumSml CanadaSml DenmarkSml FinlandSml FranceSml GermanySml HongKongSml IrelandSml ItalySml JapanSml NetherlndSml N.ZealandSml AustraliaSml SingaporeSml SpainSml SwedenSml SwitzerlndSml UKSml USSml Tables 1a and 1b provide the findings for the group tests involving the temperature variables. These group tests consisted of including the most appropriate GARCH specification for each equity index as identified in the pre-tests on each index, and the following variables: World Index, Monday dummy, DSTC, and Lunar Phases, and a variety of weather variables depending on the specific temperature variable. The Temp variable is regular temperature, and is also tested with regular wind, regular precipitation, geomagnetic storms (Tables 2 and 3). Temp High is a dummy variable for the top 10 percent of temperature days, Temp Low is a dummy variable for the bottom 10 percent of temperature days, and is also tested with geomagnetic storms. Temp DE is a deseasonalised temperature variable, and is also tested with deseasonalised wind and precipitation and with geomagnetic storms (Tables 2 and 3). 154 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008)

11 Table 2a Wind and precipitation findings for main country indices Index Returns Variance Wind Wind DE Precip Precip DE Wind Wind DE Precip Precip DE Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Argentina Austria Belgium Canada Chile China NA NA NA NA NA NA NA NA Denmark Finland France Germany Greece Hong Kong NA NA NA NA NA NA NA NA Indonesia Ireland Italy Japan NA NA NA NA NA NA NA NA Korea Luxm borg Malaysia Mexico Netherland N. Zealand NA NA NA NA NA NA NA NA Australia Philippines Portugal NA NA NA NA NA NA NA NA South Africa Singapore Spain Sweden NA NA NA NA NA NA NA NA Switzerland Taiwan Turkey UK US See notes to Table 2b for further information on this table. M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008)

12 Table 2b Wind and precipitation findings for small capitalisation indices Index Returns Variance Wind Wind DE Precip Precip DE Wind Wind DE Precip Precip DE Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig AustriaSml BelgiumSml CanadaSml DenmarkSml FinlandSml FranceSml GermanySml HongKongSml NA NA NA NA NA NA NA NA IrelandSml ItalySml JapanSml NA NA NA NA NA NA NA NA NetherlndSml N.ZealandSml NA NA NA NA NA NA NA NA AustraliaSml SingaporeSml SpainSml SwedenSml NA NA NA NA NA NA NA NA SwitzerlndSml UKSml USSml Tables 2a and 2b provide the findings for the group tests involving the wind and precipitation variables. These group tests consisted of including the most appropriate GARCH specification for each equity index as identified in the pre-tests on each index, and the following variables: World Index, Monday dummy, DSTC, and Lunar Phases, and a variety of weather variables depending on the specific group test. Wind and Precip are the regular wind and precipitation variables, and are tested with the regular temperature and the geomagnetic storms variables (Tables 1 and 3). Wind DE and Precip DE are the deseasonalised wind and precipitation variables and are tested with deseasonalised temperature and geomagnetic storms (Tables 1 and 3). 156 M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008)

13 Table 3a Lunar phases, DSTC, geomagnetic findings for main country indices Index Returns Variance Luncycle DSTC Geomagnetic Luncycle DSTC Geomagnetic Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Argentina NA NA NA NA Austria Belgium Canada Chile China NA NA NA NA Denmark Finland France Germany Greece Hong Kong NA NA NA NA Indonesia NA NA NA NA Ireland Italy Japan NA NA NA NA Korea NA NA NA NA Luxm borg Malaysia NA NA NA NA Mexico Netherland Norway NA NA NA NA New Zealand Australia Philippines NA NA NA NA Portugal South Africa NA NA NA NA Singapore NA NA NA NA Spain Sweden Switzerland Taiwan NA NA NA NA Thailand NA NA NA NA NA NA NA NA Turkey NA NA NA NA UK US Venezuela NA NA NA NA NA NA NA NA See notes to Table 3b for further information on this table. M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008)

14 Table 3b Lunar phases, DSTC, geomagnetic findings for small capitalisation indices Index Returns Variance Luncycle DSTC Geomagnetic Luncycle DSTC Geomagnetic Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig Coeff Sig AustriaSml BelgiumSml CanadaSml DenmarkSml FinlandSml FranceSml GermanySml HongKongSml NA NA NA NA IrelandSml ItalySml JapanSml NA NA NA NA NetherlndSml NorwaySml NA NA NA NA N.ZealandSml AustraliaSml SingaporeSml NA NA NA NA SpainSml SwedenSml SwitzerlndSml UKSml USSml M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) Tables 3a and 3b provide the findings for the group tests involving the Lunar Phases, DSTC, and geomagnetic storms variables. These variables were tested over multiple group tests, so only the results from the regular weather group test are reported for geomagnetic storms, while only the results from the SAD group test are reported for Lunar Phases and DSTC.

15 Table 4a SAD findings for main country indices M. Dowling, B.M. Lucey / J. of Multi. Fin. Manag. 18 (2008) Index Return Variance SAD Fall SAD Winter SAD Fall SAD Winter Coeff Sig Coeff Sig Coeff Sig Coeff Sig Singapore Malaysia Indonesia Venezuela Thailand Philippines Mexico Hong Kong Taiwan South Africa China Chile Australia Argentina Japan Greece Korea Portugal Spain Nearest Equator (%) Turkey US New Zealand Canada Italy Switzerland Austria France Luxm borg Belgium Germany UK Netherland Ireland Denmark Norway Sweden Finland Furthest Equator (%) See notes to Table 4b for further information on this table Wind, precipitation, and geomagnetic storms The findings for wind and precipitation from the group tests are contained in Tables 2a and 2b. The findings for geomagnetic storms are contained in Tables 3a and 3b. The results for wind are broadly dismissive of any relationship with returns, although in the variance results there is