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Stock market prediction using the K NearestNeighbours algorithm and a comparison withthe moving average formula
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The stock market has a large impact on the economy of a nation, thisis why it is an interesting matter to see how stock market prediction canbe used and whether or not the predicted results are valid. This reportwill compare the prediction methods, the K Nearest Neighbour algorithmand the moving average formula using the closing prices of four Swedishequities that are based on the Stockholm stock exchange OMX. To geta proper familiarization into the background of stock markets and theutilized formulas, the report explains these theoretical concepts for thereader. A proper distribution of the results is given of the data with appropriatecharts and tables. Lastly a discussion explains the implicationsof the results and the conclusion that the K Nearest Neighbour algorithmproduced more accurate data when compared to the moving average formula.

Abstract [sv]

Aktiemarknaden har en stor inverkan på en nations ekonomi, varför det är intressant att se om förutsägelser på aktiemarknaden kan användas samt om det förväntade resultatet är trovärdigt. Denna rapport kommer attjämföra slutkurser på fyra aktier frön Stockholmsbörsen med hjälp av K Närmaste Grannar algoritmen och det glidande medelvärdet formeln. För att ordentligt kunna sättas in i bakgrunden för aktiemarknaden och de valda formlerna, förklarar rapporten dessa villkor för läsaren. En lämplig fördelning av resultaten ges av det samlade datat med lämpliga diagramoch tabeller. Slutligen ges en diskussion som förklarar varför slutsatsen äratt K Närmaste Granne algoritmen ger ett mer exakt värde jämfört medden glidande medelvärde formeln.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157696OAI: oai:DiVA.org:kth-157696DiVA: diva2:771141
Examiners
Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2014-12-12Bibliographically approved

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