Predicting stock market trends is a complex task due to the inherent volatility and unpredictability of financial markets. Nevertheless, accurate forecasts are of critical importance to investors, financial analysts, and stakeholders, as they directly inform decision-making processes and risk management strategies associated with financial investments. Inaccurate forecasts can lead to notable financial consequences, emphasizing the crucial and demanding task of developing models that provide accurate and trustworthy predictions. This article addresses this challenging problem by utilizing a long-short term memory (LSTM) model to predict stock market developments. The study undertakes a thorough analysis of the LSTM model's performance across multiple datasets, critically examining the impact of different timespans and epochs on the accuracy of its predictions. Additionally, a comparison is made with a support vector regression (SVR) model using the same datasets and timespans, which allows for a comprehensive evaluation of the relative strengths of the two techniques. The findings offer insights into the capabilities and limitations of both models, thus paving the way for future research in stock market prediction methodologies. Crucially, the study reveals that larger datasets and an increased number of epochs can significantly enhance the LSTM model's performance. Conversely, the SVR model exhibits significant challenges with overfitting. Overall, this research contributes to ongoing efforts to improve financial prediction models and provides potential solutions for individuals and organizations seeking to make accurate and reliable forecasts of stock market trends.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-60397 |
| Date | January 2023 |
| Creators | Nørklit Johansen, Mads, Sidhu, Jagtej |
| Publisher | Malmö universitet, Fakulteten för teknik och samhälle (TS) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| 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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