The Application of Neural Networks in Stock-Exchange Investing
| Thesis title: | The Application of Neural Networks in Stock-Exchange Investing |
|---|---|
| Author: | Ogrodnik, Radoslaw |
| Thesis type: | Diploma thesis |
| Supervisor: | Buus, Tomáš |
| Opponents: | Poborský, František |
| Thesis language: | English |
| Abstract: | The experiment performed showed that predicting stock movements accurately with a neural networks is a very challenging task. Even obtaining low error in the training process, does not indicate that one will receive high quality predictions in the future. Similarly potential gain obtained be the network in the analysed period does not have to repeat in the next one. The reason behind, is that situations which occurred in the past, do not have to repeat. Moreover there are numerous factors that may influence index's future behaviour which simply cannot be even taken into account by an artificial model. However, even if it is difficult to create a network which would provide an output accurate enough to be used as an only indicator, neural networks are still an interesting tool and may perform well as a guidance for an investor. |
| Keywords: | data generalization ; data overfitting ; neural network creation; radial basis function; multilayer perceptron; stock-exchange forecasting; artificial neural network; market forecasting |
| Thesis title: | The Application of Neural Networks in Stock-Exchange Investing |
|---|---|
| Author: | Ogrodnik, Radoslaw |
| Thesis type: | Diplomová práce |
| Supervisor: | Buus, Tomáš |
| Opponents: | Poborský, František |
| Thesis language: | English |
| Abstract: | The experiment performed showed that predicting stock movements accurately with a neural networks is a very challenging task. Even obtaining low error in the training process, does not indicate that one will receive high quality predictions in the future. Similarly potential gain obtained be the network in the analysed period does not have to repeat in the next one. The reason behind, is that situations which occurred in the past, do not have to repeat. Moreover there are numerous factors that may influence index's future behaviour which simply cannot be even taken into account by an artificial model. However, even if it is difficult to create a network which would provide an output accurate enough to be used as an only indicator, neural networks are still an interesting tool and may perform well as a guidance for an investor. |
| Keywords: | artificial neural network; multilayer perceptron; data overfitting; data generalization; neural network creation; market forecasting; stock-exchange forecasting; radial basis function |
Information about study
| Study programme: | Finance and Accounting for Common Europe |
|---|---|
| Type of study programme: | Magisterský studijní program |
| Assigned degree: | Ing. |
| Institutions assigning academic degree: | Vysoká škola ekonomická v Praze |
| Faculty: | Faculty of Finance and Accounting |
| Department: | Department of Corporate Finance |
Information on submission and defense
| Date of assignment: | 13. 5. 2015 |
|---|---|
| Date of submission: | 30. 9. 2015 |
| Date of defense: | 12. 10. 2015 |
| Identifier in the InSIS system: | https://insis.vse.cz/zp/53022/podrobnosti |