2009
DOI: 10.1016/j.physa.2008.11.017
|View full text |Cite
|
Sign up to set email alerts
|

Weather effects on returns: Evidence from the Korean stock market

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

10
65
0

Year Published

2010
2010
2024
2024

Publication Types

Select...
3
3
1

Relationship

0
7

Authors

Journals

citations
Cited by 69 publications
(77 citation statements)
references
References 23 publications
10
65
0
Order By: Relevance
“…However, they used raw temperature data only. Yoon and Kang (2009) argue that, before financial crisis 1997, extremely low temperatures positively influenced the returns, while extremely high humidity negatively affected the returns. Keef and Roush (2002) study the relationship of stock market returns with wind speed and direction.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…However, they used raw temperature data only. Yoon and Kang (2009) argue that, before financial crisis 1997, extremely low temperatures positively influenced the returns, while extremely high humidity negatively affected the returns. Keef and Roush (2002) study the relationship of stock market returns with wind speed and direction.…”
Section: Literature Reviewmentioning
confidence: 99%
“…These mood proxy variables include, among others, temperature (e.g., Cao & Wei, 2005;T. Chang, Nieh, Yang, & Yang, 2006;Kang, Jiang, Lee, & Yoon, 2010;Lu & Chou, 2012), daylight savings time changes (e.g., Dowling & Lucey, 2008;Kamstra, Kramer, & Levi, 2000), humidity, wind speed, visibility (e.g., Kang et al, 2010;Lu & Chou, 2012;Yoon & Kang, 2009) and the 'Seasonal Affective Disorder ', SAD, (e.g., Garrett, Kamstra, & Kramer, 2005;Kamstra et al, 2003). Kamstra et al (2003) contend that people become less tolerant to risk when days shorten -SAD effect.…”
Section: Introductionmentioning
confidence: 99%
“…In this study, I follow the procedure of previous studies (e.g., Worthington, 2009) to relate the stock return linearly to M weather variables on day t as in Equation (1) Saunders, 1993;Yoon & Kang, 2009). ρ is the autocorrelation coefficient.…”
Section: Methodology the Model Estimation And Hypothesis Testsmentioning
confidence: 99%
“…Finally, et is the regression error. The model in Equation (1) Previous studies, e.g., Yoon and Kang (2009), considered various weather variables but estimated the effect for each variable one at a time. I do not follow this approach because weather variables tend to be correlated (Worthington, 2009).…”
Section: Methodology the Model Estimation And Hypothesis Testsmentioning
confidence: 99%
See 1 more Smart Citation