Screening models are tools for predicting which stock are the most likely to perform well on a stock market. They do so by examining the financial ratios of the companies behind the stock. The ratios examined by the model are chosen according to the personal preferences of the particular investor. Furthermore, an investor can apply different weights to the different parameters they choose to consider, according to the importance they apply to each included parameter. In this thesis, it is investigated whether a screening model can beat the market average in the long term. It is also explored whether parameter-weight-optimization in the context of equity trading can be used to improve an already existing screening model. More specifically, a starting point is set in a screening model currently in use at a successful asset management firm, through data analysis and an optimization algorithm, it is then examined whether a programmatic approach can identify ways to improve the original screening model by adjusting the parameters it looks at as well as the weights assigned to each parameter. The data set used in the model contains daily price data and annual data on financial ratios for all stocks on the Stockholm Stock Exchange as well as the NASDAQ-100 over the time period 2004-2018. The results indicate that it is possible to beat the market average in the long term. Results further show that a programmatic approach is suitable for optimizing screening models.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-244761 |
| Date | January 2018 |
| Creators | Alpsten, Edward, Holm, Henrik, Ståhl, Sebastian |
| Publisher | KTH, Skolan för elektroteknik och datavetenskap (EECS) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-EECS-EX ; 2018:445 |
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