Many investors use different types of data methods before making a decision, regardless of whether it is long or short term. The choice of which analysis method is generally determined by risk, removal of bias and the cost. One method that has been investigated is the use of machine lerning in data analysis. The advantage of machine lernig is that the method successfully handles comples, non-linear and non-stationary problems. In this essay, it will be investigated whether unattended machine learning, which uses the K-meaning method, which is a method that has not been investigated to any great extent either in practice or in theory to create a beneficial portfolio. The data used for the k-meaning method was historical data from the Swedish stock market between 1 January 2018 and 2 November 2020. The k-meaning analysis consists of the return of all shares included within OMX30 and the average deviation, which created a cluster of 11 shares that could generate a relatively high return compared to the remaining shares. To analyze whether the generated cluster were acceptable, an analysis of the sharpe-ratio and downward risk was preformed, which showed that the portfolio had a good risk-adjusted returnbut a worse result on downward risk.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:sh-43906 |
| Date | January 2020 |
| Creators | Larsson Olsson, Simon |
| Publisher | Södertörns högskola, Institutionen för samhällsvetenskaper |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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