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Predicting share price by using Multiple Linear Regression.
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
##### Abstract [en]

The aim of the project was to design a

multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were created and their functionality was tested, but only seven models were better than chance i.e. more than 50 % in the right direction. To determine the most suitable model out of the remaining seven, Akaike’s Information Criterion (AIC), was applied. The covariates used in the final model were; Dow Jones closing price, Shanghai opening price, conjuncture, oil price, share’s opening price, share’s highest price, share’s lowest price, lending rate, reports, positive/negative insider trading, payday, positive/negative price target, number of completed transactions during one day, OMX Stockholm closing price, TCW index, increasing closing price three days in a row and decreasing closing price three days in a row.

The maximum average deviation between the predicted closing price and the real closing price of all the

44 shares predicted were 6,60 %. In predicting the correct direction (increase or decrease) of the 44 shares an average of 61,72 % were achieved during the time period 2012-02-22 to 2013-02-20. If investing 50.000 SEK in each company i.e. a total investment of 2.2 million SEK, the total yield when using the regression model during the year 2012-02-22 to 2013-02-20 would have been 259.639 SEK (11,80 %) compared to 184.171 SEK (8,37 %) if the shares were never to be traded with during the same period of time. Of the 44 companies analysed, 31 (70,45 %) of them were profitable when using the regression model during the year compared to 30 (68,18 %) if the shares were never to be sold during the same period of time. The difference in yield in percentage between the model and keeping the shares for the year was 40,98 %.

2013. , 96 p.
##### National Category
Engineering and Technology
##### Identifiers
OAI: oai:DiVA.org:kth-140645DiVA: diva2:692167
##### Examiners
Available from: 2014-01-30 Created: 2014-01-30 Last updated: 2014-01-30Bibliographically approved

#### Open Access in DiVA

Gustaf Forslund & David Åkesson kandidatex.jobb. A Bachelor Thesis in Mathematical Statistics(2018 kB)6800 downloads
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Aeronautical and Vehicle Engineering
##### On the subject
Engineering and Technology

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Cite
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