Show simple item record

dc.contributor.authorLarikka, Jyri Egil
dc.date.accessioned2011-01-27T11:47:07Z
dc.date.available2011-01-27T11:47:07Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11250/183815
dc.descriptionMaster's thesis in Financeen_US
dc.description.abstractMy intension with this thesis is to present three different kinds of models to analyze stock market and to find good buy candidates. They use different methodology as the first is using pair-trading, the second is using technical analysis and the third is using regression analysis. The first model uses momentum strategy and adaptive market hypothesis in a pair trading context to dynamically generate good pairs of stocks based on their log return and correlation between each other. At first I generate a log return overview with a correlation matrix for all the stocks at Oslo Stock Exhange for a period of 3-12 months. Then I use the accumulated log return and correlation between the stocks in a sertain way to pick pairs of stocks and generate so called algo sheets. Both of the stocks must have higher log return than a user specified limit and on the other side I want the correlation to be lower than a user defined limit. I believe that this will give good switching opportunities between the pair of stocks since the individual stocks in the pair move differently from each other. This differs quite radically in the use of correlation compared to CAPM model where the beta represents correlation of the individual stocks return compared against the market return. In CAPM a high correlation with the market gives higher returns. This model has an order book-, order book history-, budget- and portfolio-sheet integrated in to it. While the benchmark (OSEAX index) has declined by 6.72% in the period from 14.5.2010 unntil 07.07.2010 has mine algorithm increased by 2.64%. This is 9,36% better than the benchmark in a period of 35 trading days. This is documented in the real time simulation logged in order book history. The second model I use a technical analysis tool called Moving Average Convergence and Divergence to calculate Exponential Moving Average and to find stocks which have momentum to rise fastest based on the fastest increasing difference between MACD and 9-day EMA of MACD from the bottoming during the last three days. This model produces MACD sheets for all the stocks on Oslo stock exchange and summarizes it in a momentum sheet with Buy, Hold or Sell recommendations. This model does not have order book history jet and cannot therefore document its performance from real time simulation. The third model which contains five sub models I use regression analysis to look at oil prices, S&P100, FTSE100 and GDAXI indexes descriptive power concerning the 10 year monthly development of ACY (Agercy) stock. I summarize the models performance at the end. This concludes that the DL model with all the four independent variables and their lagged values gives best R2adjusteden_US
dc.language.isoengen_US
dc.publisherUniversity of Stavanger, Norwayen_US
dc.relation.ispartofseriesMasteroppgave/UIS-SV-IØL/2010;
dc.subjectøkonomien_US
dc.subjectadministrasjonen_US
dc.subjectanvendt finansen_US
dc.titleEvaluation of different estimating techniques to generate best possible total return on investing on individual stocks on Oslo Stock Exchangeen_US
dc.typeMaster thesisen_US
dc.subject.nsiVDP::Social science: 200::Economics: 210::Economics: 212en_US
dc.source.pagenumber205 p.en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record