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Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling

Waleed Mahmoud Soliman et al.

Intelligent Systems in Accounting, Finance and Management2025https://doi.org/10.1002/isaf.70004article
AJG 1
Weight
0.41

What the paper says

Changes in macroeconomic policies and market news have considerable influence over financial markets and subsequently impact their predictability. This study investigates whether incorporating sentiment analysis can enhance the accuracy of ETF price predictions. Specifically, we aim to predict ETF price movements using sentiment scores derived from news article summaries. Utilizing FinBERT for sentiment analysis, we quantify the sentiment of these summaries and integrate these scores into our predictive models. We employ DeepAR as a probabilistic model and compare its performance with LSTM in predicting ETF prices. The results demonstrate that DeepAR generally outperforms LSTM and that integrating sentiment scores significantly improves prediction accuracy. Given the promising outcomes, we also introduce a fixed “Seed” approach to ensure greater reliability and stability in our probabilistic predictions, addressing the need for robust sampling techniques in practical applications.

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https://doi.org/https://doi.org/10.1002/isaf.70004

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@article{waleed2025,
  title        = {{Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling}},
  author       = {Waleed Mahmoud Soliman et al.},
  journal      = {Intelligent Systems in Accounting, Finance and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/isaf.70004},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.