This paper discusses the possibilities of predicting changes in stock pricing at a high frequency applying a multi-level neural network without the use of recurrent neurons or any other time series analysis, as suggested in a paper byChen et al. [2017]. The paper tries to adapt the model presented in a paper by Chen et al. [2017] by making the network deeper, feeding it data of higher resolution and changing the activation functions. While the resulting accuracy is not as high as other models, this paper might prove useful for those interested in further developing neural networks using data with high resolution and to the fintech business as a whole.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-210929 |
| Date | January 2017 |
| Creators | Törnqvist, Eric, Guan, Xing |
| Publisher | KTH, Skolan för datavetenskap och kommunikation (CSC) |
| 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 |
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