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Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling
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DOI：10.1002/isaf.70004.png)
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Abstract
En 中文
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.
Keywords:
DeepAR
ETFs
FinBERT
LSTM
probabilistic models
sampling techniques
sentiment analysis
Journal
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IF:
3.7
Papers:
92
Citations:
469
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Cited Papers