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Aktiemarknaden och vädret i New York: En multipel linjär regressionsanalys
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2018 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
The stock market and weather in New York : A multiple linear regression analysis (English)
Abstract [sv]

Tidigare studier har visat att det lokala vädret i viss grad påverkat utvecklingen på New York-börsen, alltså föreligger tecken på irrationaliteter i marknaden. I denna studie har tidsperioderna 1962-2017, 1962-1989 samt 1990-2017 studerats. Data för kursindex till Dow Jones Industrial Average och S&P 500 har använts samt lokala väderobservationer från väderstationen i Central Park (KNYC). Vi har även i denna studie sett tecken på att det lokala vädret, mer precist molnighetsgrad, i viss grad har påverkan på den dagliga utvecklingen på börsen i New York. I 15 av 20 indelningar har signifikans kunnat påvisas mellan graden av molnighet och börsutvecklingen. även säsongsvariationer såsom veckodag och månad har påvisats. Detta är likartade resultat som tidigare studier givit vid handen. Resultaten är förenade med viss osäkerhet p.g.a. autokorrelation.

Abstract [en]

Previous studies have shown that local weather have had some influence on daily capital gain or loss on the New York stock exchange, thus a sign of irrationality in the market. In this study we have examine the time periods from 1962-2017, 1962-1989 and 1990-2017. Data on index from Dow Jones Industrial Average and S&P 500 and observations on local weather from Central Park weather station (KNYC). Also in this study the results indicates that weather, more precisely cloud cover, has some effect on the market outcome. Cloud cover showed significant correlation with the market in 15 of 20 regressions. Seasonality, such as day of the week and month, has been shown as well. This is similar results as previous studies. The results has some degree of uncertainty due to autocorrelation.

Place, publisher, year, edition, pages
2018. , p. 50
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:kth:diva-231725OAI: oai:DiVA.org:kth-231725DiVA, id: diva2:1229765
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Examiners
Available from: 2018-07-02 Created: 2018-07-02 Last updated: 2018-07-02Bibliographically approved

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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