Abstract The stock market is a crucial component of the financial market. In recent years, with its vigorous development, research on stock price prediction and quantitative investment strategies has attracted scholars from various fields. With the advancement of Artificial Intelligence (AI) and Machine Learning (ML) in recent years, researchers have shifted from traditional statistical models to AI algorithms. Particularly after the deep learning boom, neural networks have achieved remarkable results in stock price prediction and quantitative investment strategy research. The objective of deep learning is to learn multi-level features, constructing abstract high-level features by combining low-level ones, thereby mining the distributed feature representations of data. This approach enables complex nonlinear modeling to accomplish prediction tasks. Recurrent Neural Networks (RNNs) have been widely applied to sequential data, such as natural language and speech. Daily stock prices and trading information are sequential data, leading many researchers to use RNNs for stock price prediction. However, basic RNNs suffer from gradient vanishing issues when the number of layers is excessive. The advent of Long Short-Term Memory (LSTM) networks addressed this problem, followed by variants such as Gated Recurrent Units (GRUs), Peephole LSTMs, and Bidirectional LSTMs (BiLSTMs). Traditional stock prediction models often overlook temporal factors or only consider unidirectional temporal relationships. Therefore, this paper employs the BiLSTM model for stock price prediction. From a model principle perspective, the BiLSTM model fully leverages the contextual relationships in both forward and backward temporal directions of time series data. It also avoids gradient vanishing and explosion problems in long sequences, enabling better learning of information with long-term dependencies.
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