Abstract
Computer researchers and economic experts have been attracted to estimate stock values for many years. As external variables such as inflation and currency rates, socio-economic conditions, and market attitudes are regularly affected by inventory price variations, this problem is highly difficult, nonlinear, and dynamic. Neural network architectures have shown great promise toward solving this problem and have outperformed classical approaches toward stock price forecasting. In the methodology proposed, the stock price is forecasted using two unique architectures—one based on LSTM-RNNs, and the other based on transformers and time embeddings—that deal with time series sequences that are augmented by an NLP-based market sentiment-analyzing plugin that utilizes FinBERT. Stocks that are listed on the NYSE that have been considerably affected by recent developments in their respective sectors are used. Experiments show that this framework shows considerable improvement in stock price prediction compared with other standard methods in time series forecasting.
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Vora, V., Shah, M., Chouhan, A., Tawde, P. (2023). Stock Market Prices and Returns Forecasting Using Deep Learning Based on Technical and Fundamental Analysis. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2021). Lecture Notes in Networks and Systems, vol 401. Springer, Singapore. https://doi.org/10.1007/978-981-19-0098-3_68
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