Stock Volatility Forecasting with Transformer Network

• Published in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 90 - 96
• Main Authors: Asl, Golnaz Sababipour, Thulasiram, Ruppa K., Thavaneswaran, Aerambamoorthy
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05-12-2023
• Subjects: Computational modeling; Computer architecture; Feeds; Forecasting; LSTM; Measurement uncertainty; Predictive models; Time2Vec; Transformer Network; Transformers; Uncertainty; Volatility
• ISSN: 2472-8322
• DOI: 10.1109/SSCI52147.2023.10371830

Financial market is in general volatile with so many uncertainties and volatility is one of the main measures of uncertainty in the market among other measures. Hence, forecasting volatility is a critical component in risk management, optimizing portfolios, and in algorithmic trading among other financial problems. There have been few machine learning and artificial intelligence techniques used in the literature for the forecasting problem. Transformer Network (TN) architecture is one of newest such techniques proposed. In this work, we utilized this architecture with multi-head attention mechanism for volatility forecasting. To enhance the performance of the TN, we incorporated different variations of the feed forward layer. The performance of three distinct TN models was evaluated by implementing three different deep learning layers (CNN, LSTM, and a hybrid layer (CNN-LSTM)) in the encoder block of TN as the feed forward layer. The results clearly demonstrate that the TN model with the hybrid layer (CNN-LSTM) outperformed the other models, including a recently proposed data-driven approach.