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Lopes, Jorden Anthon (2022) Empirical Study and Forecasting Tesla Stock prices using Sentiment analysis and deep learning methods. Masters thesis, Dublin, National College of Ireland.
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Abstract
The stock market has become one of the biggest source of income and nowadays many new people have started investing in the stock market. Investment depends on proper timing and in the selection of proper company, and to make this decision making easier this research has been implemented. In this study stock value analysis and prediction has been done for the Tesla Company. For time-series data analysis LSTM, RNN,BI-LSTM, CNN-RNN, CNN-LSTM and customized CNNBI- LSTM deep learning models have been executed in which the proposed model CNN-BI-LSTM gave better results as compared to other models. The impact of convolution layer and outliers are also being examined, to support this research, sentimental analysis has been carried out on Tweets related to Tesla Company with the help of NLTK VADER libraries. This is a secondary study to understand the impact of tweets on stock prices. The final intention behind the whole study was, before getting involved or investing in any company, investors would get some estimation, so that they could make decisions accordingly also results of implemented experiments could help new researchers to select techniques.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | LSTM; RNN; BI-LSTM; CNN-RNN; CNN-LSTM; CNN-BI-LSTM |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 22 Feb 2023 16:25 |
| Last Modified: | 02 Mar 2023 09:33 |
| URI: | https://norma.ncirl.ie/id/eprint/6216 |
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