Stochastic modeling using machine learning and stochastic differential equations
| dc.contributor.author | Johansson, Olof | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Cohen, David | |
| dc.contributor.supervisor | Pereira, Mike | |
| dc.date.accessioned | 2022-07-08T10:29:30Z | |
| dc.date.available | 2022-07-08T10:29:30Z | |
| dc.date.issued | 2022 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Due to the presence of (unpredictable) fluctuations in the financial market, stochastic models have long been used in various financial applications. In particular, a common application is the forecasting of a given financial time series, for example stock prices. Stock prices are often assumed to follow a Geometric Brownian Motion (GBM), a specific type of stochastic differential equation. Recent studies have demonstrated promising results of using neural networks to parameterize SDEs (referred to as a neural SDE framework). Further studies have demonstrated how machine learning, specifically Recurrent Neural Networks (RNNS), can be used for predicting the future values of time-dependent data. The aim of this thesis was to investigate the possibility of combining a RNN with a neural SDE framework to forecast stock prices. In particular, three different RNNs were used, namely a Long Short-Term Memory (LSTM) model, an Echo State Network (ESN) and a Long- Short Echo State Network (LS-ESN). The results of this thesis showed that the three models considered in this thesis achieved more accurate predictions of stock prices when compared to both a traditional LSTM model and a GBM model. This was showed for both a single stock and also for 100 different stocks, where the latter also was tested for different numbers of predicted time steps ahead. | sv |
| dc.identifier.coursecode | MVEX03 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/305146 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Stochastic differential equations, machine learning, stochastic modeling, echo state networks, recurrent neural networks, financial time series | sv |
| dc.title | Stochastic modeling using machine learning and stochastic differential equations | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Engineering mathematics and computational science (MPENM), MSc |