Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917771803099136 |
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| author | Gowani, Rifa Kanjiani, Zaryab |
| author_facet | Gowani, Rifa Kanjiani, Zaryab |
| contents | Trading and investing in stocks for some is their full-time career, while for others, it's simply a supplementary income stream. Universal among all investors is the desire to turn a profit. The key to achieving this goal is diversification. Spreading investments across sectors is critical to profitability and maximizing returns. This study aims to gauge the viability of machine learning methods in practicing the principle of diversification to maximize portfolio returns. To test this, the study evaluates the Long-Short Term Memory (LSTM) model across nine different sectors and over 2,200 stocks using Vanguard's sector-based ETFs. The R-squared value across all sectors showed promising results, with an average of 0.8651 and a high of 0.942 for the VNQ ETF. These findings suggest that the LSTM model is a capable and viable model for accurately predicting directional changes across various industry sectors, helping investors diversify and grow their portfolios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques Gowani, Rifa Kanjiani, Zaryab Computational Engineering, Finance, and Science Machine Learning Neural and Evolutionary Computing Trading and investing in stocks for some is their full-time career, while for others, it's simply a supplementary income stream. Universal among all investors is the desire to turn a profit. The key to achieving this goal is diversification. Spreading investments across sectors is critical to profitability and maximizing returns. This study aims to gauge the viability of machine learning methods in practicing the principle of diversification to maximize portfolio returns. To test this, the study evaluates the Long-Short Term Memory (LSTM) model across nine different sectors and over 2,200 stocks using Vanguard's sector-based ETFs. The R-squared value across all sectors showed promising results, with an average of 0.8651 and a high of 0.942 for the VNQ ETF. These findings suggest that the LSTM model is a capable and viable model for accurately predicting directional changes across various industry sectors, helping investors diversify and grow their portfolios. |
| title | Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques |
| topic | Computational Engineering, Finance, and Science Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2409.05778 |