Sample Economics Paper on Does Weather Affect The Stock Market In Hong Kong?

There is a direct relationship between weather and people’s mood. Certain weather conditions foster positive moods while others lead to negative moods. Subsequently, people’s moods play a significant role in their decision-making process particularly when it comes to investments. The literature review evaluates the impact of weather variables on people’s mood. It further evaluates the impact of the each weather variable on stock returns.

  1. Relationship between weather and mood

Watson proposed the two-dimensional model stating that an individual’s mood is either measured or defined as a positive or negative state of mind that determines how people respond to various stimuli. According to Watson (2000), from a psychological perspective, someone with a negative mood either through stress or through depression is most likely to react negatively to stimuli while a more happy or gleeful person (positive mood) is likely to respond positively to stimuli. In terms of the effect of weather, people are likely to be in a negative mood when the weather is gloomy or unfavorable. On the other hand, people feel happier (positive mood) when the weather is sunny, clear and favorable. Schwarz (1990) developed the Mood-as-Information hypothesis states that psychologically, feelings and mood determine the decision-making process. The hypothesis is measured through risk perceptions and optimism.

There are multiple studies on the relationship between weather and mood and its ability to affect an individual’s mood either positively or negatively. Denissen, Butalid, Penke, and VanAken (2008) through their study analyzed the effect of six weather parameters, which are precipitation, air pressure, wind power, sunlight, temperature, and photoperiod. According to the study, there was no significant relationship between the parameters and the mood of the study participants. There was a wide variation on the mood of the participants when exposed to the six weather parameters. Denissen (2008) however found that there was a significant effect of wind power, sunlight and temperature on mood with temperature having a direct impact on tiredness and subsequently negative mood. The study found that sunlight mediated the effects of air pressure and precipitation on tiredness. The authors, therefore, concluded that lack of sunlight combined with precipitation and air pressure negatively affect mood by aiding the level of tiredness on people. The study also found out that wind negatively affects mood more in spring and summers than in autumns and winters. This means that the season is a determining factor on the impact of weather on mood irrespective of the weather parameter. According to (Li, Wang & Hovy, 2014), individual characteristics are a major determinant of the impact of the weather of mood and hence a variation in people’s mood to similar weather conditions. Some of the individual characteristics that account for the mood variations include age, gender, and personal traits.

Keller et al. (2005) undertook a study to determine the relationship between the number of hours people spent outside and its effect on cognition and mood. The study established that to the people who spent more than half an hour outside, higher pressure and temperatures were linked to positive moods. On the other hand, individuals spending less than half an hour outside had lower moods. According to Keller et al. (2005), spending less time in a favorable or pleasant environment makes people perceive the indoor and mundane environment as unfriendly and dreary, which may lead to negative moods. The study also found out that exposure to high temperatures especially during spring leads to positive moods. On the other hand, higher temperatures during summer lead to negative moods especially to individuals living in the southern hemisphere where the temperatures are normally high. Keller et al. (2005) therefore concluded that season is a determining factor when it comes to the effect of weather on mood. The findings concur with Denissen et al. (2008) who found out that external factors (personal traits) are a factor in determining the effect of weather on mood. However, Keler et al. (2005) only used two weather parameters while Denissen et al. (2008) used six weather parameters hence more dependable to test three mood aspects while also taking into consideration biological and personality traits like gender and age.

Klimstraet al. (2011) also undertook a study to evaluate types of weather reactivity through the examination of self-reported moods daily across 30 days. The study findings were concurrent to those of Keller et al. (2005) and Denissen et al. (2008) by finding that even though there is a relationship between weather and mood, the effect is minimal. The study classified the participant’s moods into three types of weather reactivity. They were summer lovers (people who experience good moods when temperatures are high and precipitation is low), summer haters (people who experience bad moods when temperatures are high and precipitation is low) and rain hater (people who experience bad mood with high precipitation). The finding from the study found that the effect of weather on summer lovers was directly opposite to summer haters. This demonstrates different weather reactivity types between individuals may be the reason why past studies found little correlation between weather and mood. Klimstraet al. (2011) therefore concluded that individual characteristics and precisely weather reactivity types are a major determinant on an individual’s perception of weather and its impact on their moods. Huibers, DeGraaf, Peeters, and Arntz (2010) on the other hand found out that men have seasonal peaks of sad mood and depression during summers while women have season peaks of sad mood and depression during fall.

There is a direct relationship between weather and positive/negative mood. However, the relationship between the two is minimal due to the major variances between the reactions of the participants. Personal traits and seasons are the main recurring factor that plays a vital role as a moderator of the impact of weather on mood.

  1. How Mood Affect Decision-making and Risk Tolerance

Worthy, Byrne & Fields (2014) undertook a study on the impact of mood and emotion in the process of decision-making. The study examined the impact of negative mood such as anxiety, stress and worry and that of positive mood such as excitement during decision-making. According to Apergis, Gabrielsen & Smales (2016), negative moods lead to individuals being averse towards the unforeseen circumstances linked with the possibility of gaining future rewards. Negative moods, therefore, lead to low prospection during the decision-making process and favor short-term gains over long-term achievements. The study presented two-dynamic choices with one decreasing option having short-term gain and an increasing option having long-term gains. Individuals with negative moods were associated with short-term gains while those with positive moods were associated with long-term gains. The study introduced the reinforcement-learning model that integrates state-based and reward-based information. Additionally, Worthy, Byrne & Fields (2014) found that negative emotion was associated with higher delay discounting based on the standard delay-discounting task. Individuals with negative moods prefer to maximize immediate return rather than improvement of future returns.

Jennifer Lerner (2015), undertook a study on the effect of anger and fear on risk tolerance. Anger and fear greatly influence decision-making and the level of risk tolerance and individual is willing to undertake. According to Learner (2015), fear leads to uncertainty and therefore an individual is risk-averse. Individuals with fear are likely to prefer immediate gains compared to long-term gain even in situations where the long-term gains are more. On the other hand, anger instills confidence among individuals and may lead to higher risk tolerance. Individuals with anger are likely to make risky decisions than clam individuals. Anger is an activating emotion that relies on stereotypes that makes individuals eager to rush decisions. In the case of poor decision-making, angry people are more likely to blame other people for their misfortunes (Keller et al. 2005). Anger is a trigger-happy impulse that makes individuals to desire rewards intensely.

On the contrary, Lerner (2015) with positive moods such as happiness are likely to take more risks and choose higher long-term rewards at the expense of small immediate rewards. Their decisions are based on the quality, likability, and attractiveness of the option. Moods therefore not only affect the nature of decisions and level of risk tolerance but also the speed.

Keltner et al (2014) state that moods play an adaptive coordination role that triggers a set of stimuli (Communication, experience, behavior, and physiology) that helps individuals handle, with opportunities and problems. For example, a participant with anger was linked with the need to change a situation and make a decision against a factor that is against the choice. The need to make decisions with anger manifests psychologically but also experientially. It is also associated with approach motivation neural activation traits and peripheral physiology changes that encourage individuals to make hasty decisions.

According to Jacobsen and Marquering (2008), behavioral finance considers the impact of psychological and cognitive characteristics when investors are making investment decisions. There are multiple factors such as mood that limit an investor’s ability to make rational financial decisions through the analysis of the relevant available information. There is a gap between the expected/rational behavior and actual/normal behavior. (Schwarz & Bless, 1991) states that mood plays an adaptive role by indicating when an investment decision requires extra attention and subsequently more consideration. It is therefore imperative that negative moods signal risk and therefore necessitates increased systematic processing and vigilance before making an investment decision. On the other hand, positive moods signal a secure and safe option that most likely leads to faster and heuristic processing and subsequently risk tolerance decisions.

A study by Bodenhausen et al. (2000) on the other hand states that systematic processing associated with people with negative moods is not necessarily desirable compared to automatic processing associated with people with positive moods. The study found that systematic processing associated with negative moods may heighten anchoring effects by increasing emphasis on the anchor. Correspondingly, negative moods reduce the accuracy of judgments due to increased deliberative processing. The study also established that dysphoric people demonstrate extreme rumination

Shahzad (2019) in his study established that positive mood when making finance decisions embody trustworthiness and cooperativeness during the negotiation process. They elicit concession, trust, and cooperation from the other parties. On the other hand, negative emotions embody recklessness competitiveness and aggressiveness. Shahzad (2019) explained that the difference between positive moods and negative moods is the time is taken and depth of processing. Positive mood traits such as happiness entail high certainty appraisals. On the other hand, negative traits such as sadness lead to low certainty, which also reduces the rate of risk tolerance. Shim, Kim, and Ryu (2015) found o that high certainty moods such as happiness, disgust, and anger lead to high heuristic process through increasing reliance on expertise rather than the quality of argument and stereotype.

  1. Weather and Stock Return

The behavior Finance Theory states that weather has a great effect on human behavior particularly moods, which subsequently affect investor-trading decisions. Investors with positive moods make different decisions from those with negative moods, which directly affect stock returns (Kim, Ryu & Seo, 2014). Under normal circumstances, positive moods foster optimistic investment decisions while negative moods lead to negative returns.

  1. Relationship between cloud cover and stock return

Hirshleifer & Sumway (2003) undertook a study on the effect of cloud cover on stock return from 26 countries over 15 years. The study established that low cloud cover leads to sunshine and subsequently positive moods with investors evaluating their prospects more optimistically when in good moods compared to when in negative moods. The nature of an investor’s mood affects behavior and judgment. The study also found out that when people are in positive moods, they warm up to good things and find them more psychologically available and salient that when in negative moods.  Therefore dreary, dull, dim and depressing days with a high amount of cloud cover leads to low stock returns. On the other hand, warm, bright and cheery days with a low amount of cloud cover leads to high stock returns. Chang et al (2006) in their study on the effect of cloud cover in the Taiwan Stock Market established that there is a negative effect of cloud cover of stock returns.

Sanders (1993) in the study of the New York Stock Market established that cloud cover significantly correlates to stock returns when simultaneously considered with other non-weather variables. An analysis by month indicates that the stock prices increased by about 74% during sunny January days and by 68% during January cloudy days. Ana analysis of the other months except for January shows that there is a strong negative relationship between cloud cover and stock returns. However, Sanders (1993) stated that the day of the week was a major determinant of stock returns. There was a high level of stock return on weekends irrespective of the cloud cover due to the “weekend effect”. On the other hand, the stock returns on Monday were always negative irrespective of the cloud cover. The negative return on Monday was however less on sunny days.

  1. Relationship between humidity and stock return

Yoon & Kang (2009) undertook a study on the Korean Stock Market to establish the effect of humidity on stock returns. The study established that humidity has an insignificant effect on stock returns based on the insignificance of the test coefficients. However, the study established that extremely high humidity conditions lead to low stock return. Extremely low humidity led to high stock returns. However, there was no correlation between humidity and stock returns during the post-crisis period. Dowling & Lucey (2005) also undertook a study to evaluate the effects of weather on stock prices. The study found on extreme humidity conditions affected stock returns. Therefore, mild humidity conditions had no impact on stock returns. However, other external forces like the 1997 financial crisis significantly affect the effect of humidity when its effect became insignificant due to the adoption of an electronic trading system and removal of foreign restriction investors. While extreme humidity affects investor moods, market efficiency is the main determinant of stock returns.

Chang et al (2006) used a threshold model to study the relationship between stock return and humidity. His study was undertaken in Taiwan between 1997 and 2003 and found that the impact of humidity on stock returns is insignificant. Farrukh (2019), in his study, stated that humidity negatively affects the volume of trading at the stock exchange. The study found that normal levels of humidity do not affect stock prices. However, under extremely high levels of humidity, higher humidity negatively affects the prices of returns wile extreme low levels of having a positive effect on stock prices. However, he noted that calendar effects are a determining factor when it comes to financial markets. There are high stock returns during the spring and winter months irrespective of the level of humidity. The level of stock activity is also high in January

  1. Relationship between air pressure and stock return

Farrukh (2019) studied the effect of weather parameters on stock returns including air pressure. The study established that air pressure has a marginal correlation on stock returns at 10% significance level. This means that high air pressure leads to high stock returns while low air pressure marginal leads to a decrease in stock returns. However, the study established other controlling variables like Halloween dummies, January and Mondays. The study specifically established that January and Halloween Dummies have a positive effect on the trading volume at the stock market irrespective of the level of air pressure.

Goldstein (1972) demonstrates that there is a positive correlation between air pressure and stock returns. Air pressure is one of the most mood-inducing weather variables. Exposure to high air pressure is likely to prompt positive moods while low air pressure induces negative moods. The impact of air pressure is more prevalent in technology stocks compared to blue-chip stocks due to the high impact of moods on the process of decision-making due to the abstract, risky and uncertain nature of the stocks. Likewise, Baker and Wirgler (2006) add that new, unprofitable, volatile and smaller companies with a high possibility of growth are more affected by positive or positive mood induced by high and low air pressure consecutively.

Schneider (2010) states that air pressure is more likely to affect stock return more than any other weather variable. The effect of air pressure is more significant since almost all the investors in the stock market are exposed to the same air pressure. When many investors are exposed to similar health conditions, the weather effect is more profound. Air pressure unlike other weather variables like temperature, cloud cover, and wind, is available to both outdoor and indoor investors. In most situations, private and professional investors make investment decisions indoors meaning that there is no exposure to outdoor weather conditions.

  1. Relationship between temperature and stock return

From a psychological perspective, high temperatures are associated with positive and upbeat moods during winter and negative moods during summer. Yoon & Kang (2009) undertook a study on the Korean Stock Market on the impact of the temperature of stock returns and established that during the pre-crisis period, there is a positive influence between low temperatures high stock returns. The findings imply that stock returns are usually high when there are low temperatures and low when the temperatures are high. On the other hand, high temperatures harm stock returns, which means that high temperatures led to low stock returns. A similar study during the post-crisis period indicates that there is no relationship between temperature and stock returns. The study, however, indicates that weather had no effect on stock returns during the post-crisis period due to the removal of foreign investment restrictions, which encouraged foreign investments. It, therefore, implies that local weather did not have a direct effect on a stock investment, which made the market more effectively.

Cao and Wei (2005) also undertook an experiment on the effect of weather and stock returns. The study established that there is a negative relationship between temperature and stock returns. Higher temperatures translated to low stock returns while low temperature led to high stock returns. Low temperatures lead to aggression, which leads to risk-taking while high temperatures lead to apathy, which leads risks to risk averseness. Chang et al (2006) who undertook a study on the Taiwan stock market presented a similar view. The study also found that there is a negative correlation between temperature and stock returns in Taiwan. Jacobsen & Marquering (2008) on the other hand stated that the effect of temperature on stock returns is spurious with summer and winter seasonality a major determinant of its effect. According to the study, high temperatures during winter are likely to lead to high stock returns while low temperatures during winter leading to low stock performance. On the other hand, high temperatures during the summer are likely to lead to low stock returns while low temperatures during the summer leading to high stock returns.

  1. Relationship between wind speed and stock return

There is a correlation between the speed of wind and mood, which subsequently affects the decision-making process. Farrukh (2019) undertook a study on the effect of wind speed on stock returns. According to the study, there is a pervasive and significant wind effect on stock activity. The speed of wind directly affects comfort and mood, which consequently affect decision-making and judgment in situations involving risk and uncertainty. This concurs with the misattribution argument. Bouteska & Regaieg (2018) in their study established that the effect of wind speed is more significant compared to that of other weather variables like temperature and sunshine which implies that wind speed has a stronger impact on emotions and mood than the two.

Similarly, Olha Zadorozhna (2009) states that the speed of wind affects stock returns negatively. Strong winds are a nuisance and disruptive and often lead to negative moods. On the other hand, a calm environment with little or no wind disruption brings tranquility, which leads to positive moods. The study states that hot, wild and exhilarating or bothersome wind blows through an individual’s mind as strong as it blows on the surface. Therefore, strong winds are associated with low stock returns while low speed or lack of winds may lead to high stock return.

Rehse et al (2019) stated that the negative effect of strong winds on moods and subsequently stock returns is higher during summer and spring season that during the winter and fall season. Besides, hot and dry winds have high levels of positive ions formed through friction with strong winds. They often result in irritability, allergies hyperactivity, and listlessness (negative moods). Wind may not be necessarily fierce and strong but maybe constant, which also leads to low stock returns. This, therefore, demonstrates that, as is the case with other weather variables, there are external factors that determine the extent to which weather affects stock returns.

  1. Alternate views for the above results do not hold

While the weather is a key determinant to the mood of individuals, which affect their decision-making process and risk tolerance, other factors affect the hypothesis. According to Sanders (1993), the day of the week may affect the impact of weather on stock returns. For example, the level of stock return is high on weekends irrespective of the weather condition compared to other days of the week. Contrary, Monday is always associated with low stock returns regardless of the weather condition.

While high temperatures negatively affect stock prices, the condition depends on the season of the year. Under normal circumstances, high temperatures during the summer are likely to lead to low stock returns while low temperatures during the summer leading to high stock returns. For example, high temperatures during winter are likely to lead to high stock returns, which mean that the above results do not hold. On the other hand, low temperatures during winter leading to low stock performance.

Other factors that affect the results above are government regulations and the economic climate of a specific country. For example, external forces like the 1997 financial crisis in South Korea significantly nullified the impact of weather conditions on the stock crisis. While weather conditions affected the stock process normally during the pre-crisis period, the situation was different during the post-crisis. While temperature reacted normally during the post-crisis period, the effect of cloud cover and humidity was insignificant due to the adoption of an electronic trading system and removal of foreign restriction investors. The decision allowed an influx of foreign investors who were not affected by local weather.

According to Dowling & Lucey (2005), extreme humidity conditions affect stock returns. Extreme humid conditions led to low stock prices while extremely low humidity led to high stock prices. Mild humidity conditions, on the other hand, had no impact on the stock returns. While extreme humidity affects investor moods, market efficiency is the main determinant of stock returns (Shim, Kim & Ryu, 2015). With an efficient market, unfavorable weather conditions may not necessarily lead to a decrease in stock returns, conversely, inefficient market conditions may lead to poor performance even when the weather conditions are favorable.

The impact weather conditions are more prevalent in technology stocks relative to the conventional blue-chip stocks. The significance is mainly because of the substantial role moods play during the decision-making process when it comes to nonfigurative, risky and uncertain stocks (Klimstra et al 2011). Additionally trading with stocks from new, unprofitable, volatile and smaller companies are more affected by mood than established profitable stocks.

  1. Other Mood proxies and Stock Returns

Other mood proxies other than weather affect moods and subsequently stock returns. Some of the proxies include seasons, personal characteristics, and mental conditions among others. One of the main factors is a seasonal affective disorder (SAD). The seasonal affective disorder is a type of distress that takes places simultaneously every year, mainly during winter. SAD can influence an individual’s state of mind, mood, and levels of energy negatively affecting major life aspects including the decision-making process (Bassi, Colacito & Fulghieri, 2013). It leads to feeling irritable, stress, anxiety, loss of pleasure and interest, low self-esteem, depression, and loss of mood. This condition often leads to a decrease in stock returns.

Kamstra, Kramer, and Levi (2003) states that there is an uncommon type of seasonal affective disorder that starts in late spring or pre-summer until the fall. Generally, seasonal affective disorder begins in winter or fall until early summer or spring.  With the level of winter, light changing the more one moves away from the equator, SAD is prevalent among individuals living about 30 degrees latitude south or north including the United States and Asia. The seasonal affective disorder is caused by less sunshine during winter and fall, which prompts the mind to release less serotonin, which is a concoction, connected to cerebrum pathways that determine an individual’s mood. The condition affects around 1% to 2% of people, especially young children and women. However, the milder winter depression affects between 10% and 20 % of individuals (Kamstra, Kramer & Levi, 2003). This means that during the winter, stock returns are likely to decrease to between 1 and 2% and between 10% and 20% during milder winter conditions.

The lunar phase is also a mood proxy that affects people behavior in the stock market. Lunar phase refers to the different phases of the moon during the year, which affects people’s moods and behavior across the year. During the full moon, people are most likely to be angry and violent which may lead to swift decisions without consideration (Kim, 2017). Additionally, the full moon may lead to lack of sleep, which may make an individual behave in an inexplicable way or erratically (Floros & Tan, 2013). Lack of enough sleep during full moon causes negative moods, which may negatively affect the stock returns. Additionally, new moon lacks sufficient light and energy, which leads to negative moods and subsequently low stock returns. On the other hand, the first half of the lunar cycle makes an individual feel motivated, therefore foster positive moods, and subsequently high stock returns.



Apergis, N., Gabrielsen, A. and Smales, L.A., 2016. Unusual weather and stock returns—I am not in the mood for mood: further evidence from international markets. Financial Markets and Portfolio Management30(1), pp.63-94.

Baker, M. and Wurgler, J., 2006. Investor sentiment and the cross‐section of stock returns. The journal of Finance61(4), pp.1645-1680.

Bassi, A., Colacito, R., and Fulghieri, P., 2013. ’O sole mio: An experimental analysis of weather and risk attitudes in financial decisions. The Review of Financial Studies26(7), pp.1824-1852.

Bodenhausen, G.V., Gabriel, S. and Lineberger, M., 2000. Sadness and susceptibility to judgmental bias: The case of anchoring. Psychological Science11(4), pp.320-323.

Bouteska, A. and Regaieg, B., 2018. Loss aversion, overconfidence of investors and their impact on market performance evidence from the US stock markets. Journal of Economics, Finance and Administrative Science.

Cao, M. and Wei, J., 2005. Stock market returns A note on temperature anomaly. Journal of Banking & Finance29(6), pp.1559-1573.

Chang, T., Nieh, C.C., Yang, M.J. and Yang, T.Y., 2006. Are stock market returns related to the weather effects? Empirical evidence from Taiwan. Physica A: Statistical Mechanics and its Applications364, pp.343-354.

Denissen, J.J., Butalid, L., Penke, L. and Van Aken, M.A., 2008.