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The weather and stock returns
Název práce v češtině: Počasí a akciové výnosy
Název v anglickém jazyce: The weather and stock returns
Klíčová slova: Behavioral finance; Weather effect; Market efficiency; Anomaly, GARCH
Klíčová slova anglicky: Behavioral finance; Weather effect; Market efficiency; Anomaly, GARCH
Akademický rok vypsání: 2016/2017
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: PhDr. Mgr. Jiří Kukačka, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 15.06.2017
Datum zadání: 15.06.2017
Datum a čas obhajoby: 29.01.2019 09:00
Místo konání obhajoby: Opletalova - Opletalova 26, O105, Opletalova - místn. č. 105
Datum odevzdání elektronické podoby:04.01.2019
Datum proběhlé obhajoby: 29.01.2019
Oponenti: Mgr. Aleš Čornanič
Kontrola URKUND:
Seznam odborné literatury
HIRSHLEIFER, D. and T. SHUMWAY, 2003. Good Day Sunshine: Stock Returns and the Weather. The Journal of Finance, 58(3).
KRÄMER, W. and R. RUNDE, 1997. Stocks and the weather: An exercise in data mining or yet another capital market anomaly?. Empirical Economics, 22(4), 637–641.
MICHAEL, D. and B.M. LUCEY, 2004. Weather, Biorhythms and Stock Returns: Some Preliminary Irish Evidence.
TUFAN, E. and B. HAMARAT, 2004. Do Cloudy Days Affect Stock Exchange Returns: Evidence from Istanbul Stock Exchange. Journal of Naval Science and Engineering, 2(1), 117–126.
BABCOCK, B.A., 1990. The Value of Weather Information in Market Equilibrium. American Journal of Agricultural Economics, 72(1), 63–72.
DOWLING, M.M. and B.M. LUCEY, 2008, Are Weather Induced Moods Priced in Global Equity Markets?. Journal of Multinational Financial Management, September 2008 18(2)
CHANG, Shao-Chi, Sheng-Syan CHEN, Robin K. CHOU a Yueh-Hsiang LIN. Weather and intraday patterns in stock returns and trading activity. Journal of Banking and Finance [online]. 2008, 32(9), 1754-1766
WILLIAM N. GOETZMANN a NING ZHU. Rain or Shine: Where is the Weather Effect? European Financial Management [online]. 2005, 11(5)
LOUGHRAN, T., & SCHULTZ, P. (2004). Weather, Stock Returns, and the Impact of Localized Trading Behavior. Journal of Financial and Quantitative Analysis, 39(2), 343-364.
FRÜHWIRTH, Manfred a Leopold SÖGNER. Weather and SAD related mood effects on the financial market. Quarterly Review of Economics and Finance [online]. 2015, 57, 11-31
KAMSTRA, Mark J., Lisa A. KRAMER a Maurice D. LEVI. Winter Blues: A SAD Stock Market Cycle. The American Economic Review [online]. 2003, 93(1), 324-343
SAUNDERS. Stock Prices and Wall Street Weather. The American Economic Review [online]. 1993, 83(5), 1337-1345
Předběžná náplň práce v anglickém jazyce
Research question and motivation
In recent years, researchers came up with many behavioral finance theories, which contradict the typical efficient market approach. One of those theories, suggested by Saunders (1993), is the influence of weather on the stock market. So far, various theses have concluded that the only weather factor that has any significant effect on the market, of course, when we omit obvious events as natural catastrophes, is sunshine. It corresponds to the idea from the field of psychology, that people’s mood is influenced by sun which can imply a possible change of their behavior.
Closely related to sunshine is also Seasonal Affective Disorder (SAD), condition that affects people during the season with fewer hours of daylight, which was found to have an important effect on stock market returns by Kamstra, Kramer and Levi (2003).
It is clear, that it does not make much sense to study the impact of weather on huge stock exchanges where the traders are from all over the world and thus confront very different weather conditions, which is discussed by Loughran and Schultz (2004). On the other hand, the less important (in global impact) stock exchanges in smaller countries, where most traders are domestic and the weather conditions are more or less the same in the whole area, could bring interesting results.

This thesis will focus on developed and emerging markets according to MSCI classification and will compare the weather effect on each of them.

Preliminary working hypotheses are:
• There is no evidence of impact of sunshine on stock returns. (Saunders, 1993)
• Sunshine does not cause higher returns. (Hirschleifer, Shumway, 2003)
• SAD does not influence returns. (Kamstra, Kramer, Levi, 2003)
• The effect is not more significant in emerging markets.
• The effect is not more significant in countries with small land area. (Loughran and Schultz, 2004)

The purpose of this thesis is to give an updated research of the weather effect on stock returns by following the present literature and expanding the research to larger amount of markets with focus on emerging markets. The contribution will be a comparison of the weather effect between emerging and developed markets and the results could clarify larger presence of market inefficiency in the markets which are emerging opposite to the ones that are considered already developed. Findings of the thesis, the weather effect, could serve as another variable to consider when trading on a stock market.

Data about weather will be retrieved from “National Centers for Environmental Information”, which has been the source of most papers dealing with the same topic. Data contains hourly information about cloud cover, temperature, precipitation, etc. Cloud cover will be used as a proxy variable for sunshine.
Financial data will be obtained from Thomsom Reuters Eikon.

The analysis will follow the approach suggested by Hirschleifer, Shumway (2003), with later followed improvements, e.g. including dummy variables for January and Monday effects (Goetzmann and Zhu, 2005) and adding a SAD variable (Kamstra, Kramer, Levi, 2003). Preliminarily, the econometric regression should look like:

Rt = B0 + B1*SKCt + B2*SADt + B3*Rt-1 + B4*Temp + B5*Prec + D1*Mt + D2*Jt +et,

where SKC stands for sky cover (cloudiness), SAD for seasonal affective disorder, R for returns, R-1 for lagged returns, Temp for temperature, Prec for the amount of precipitation, M and J are dummy variables for Monday and January respectively, with values 1 if Monday or January, 0 otherwise. In the final work, the model may slightly differ.

1. Introduction
2. Literature review
3. Data description
4. Methodology
5. Discussion and results
6. Conclusion
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