End-to-End Optimization of High-Frequency ETF Trading with BiLSTM and FinBERT-Driven Sentiment Analysis
- 1 School of Mathematical Sciences, University of Nottingham, Nottingham, UK
- 2 Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA
Abstract
This paper presents a comprehensive approach to developing effective trading strategies for Exchange-Traded Funds (ETFs) by leveraging Long Short-Term Memory (LSTM) networks and sentiment analysis. LSTM networks are well-suited for modeling sequential data and capturing temporal dependencies, making them ideal for predicting stock market trends based on historical data. To further enhance prediction accuracy, sentiment analysis is integrated into the model, allowing for the evaluation of market sentiment derived from financial news and social media. This paper employs FinBERT, a specialized financial language model, pre-trained with masked language modeling (MLM) and next sentence prediction (NSP) tasks, to analyze financial text data. The study examines the effectiveness of various LSTM and Bidirectional LSTM (BiLSTM) architectures in predicting ETF price movements and evaluates their performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, two trading strategies, ultra-high frequency and high frequency, are implemented to assess the practical applicability of the proposed models. The results demonstrate that the combination of LSTM networks and sentiment analysis provides more accurate predictions and leads to profitable trading strategies. The findings suggest that this integrated approach offers a robust framework for developing advanced trading systems in financial markets.
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