Based on Research into Deep Learning in Financial Investment Decision-Making
With the rapid development of deep learning technology, a growing body of research is directing its focus toward its application in financial investment decision-making (Liu et al., 2020). For instance, the transformer model, a deep learning model based on self-attention mechanisms, was initially introduced in the field of natural language processing. In the financial domain, researchers have applied the transformer model to time series forecasting. It improves modeling capabilities for market dynamics by capturing global dependencies within sequences. Despite its advantage in simultaneous long-range dependency handling, its drawback lies in its relatively high computational complexity, especially when confronted with large-scale financial data. Secondly, echo state networks (ESN) is a variant of recurrent neural networks specialized in dynamic systems with time delays (Georgopoulos et al., 2023). In finance, ESNs are used to model nonlinear relationships and long-term dependencies in time series data. These networks capture dynamic patterns in financial markets through the construction of dynamic internal states. However, ESNs may be influenced by local optima when dealing with certain complex financial scenarios (Wang et al., 2022).
Capsule networks (CapsNets), on the other hand, are a different approach to image processing compared to traditional convolutional neural networks (Bian et al., 2021). They attempt to better capture hierarchical feature structures. In finance, CapsNets are used for the classification and prediction of time series data (Li et al., 2021). This model can better model postures and relationships of features but still faces challenges when handling complex relationships in large-scale financial data. Lastly, generative adversarial networks (GANs) are a category of generative models consisting of a generator and a discriminator, trained through adversarial training to generate fake samples that resemble real ones. In finance, GANs are used to generate synthetic financial time series data with similar features. This provides more training samples, helping to improve the model's generalization performance. However, further research is needed to ensure the quality and authenticity of the generated financial time series data (Shah et al., 2023).
These deep learning models provide diverse tools and methods for the financial domain, offering investors more accurate and practical decision support (Han & Yuan, 2023). However, researchers still need to deepen their understanding of the application of deep learning in financial investment to better adapt to the dynamic changes in the market. In the future, continuous innovation and refinement of deep learning models hold the promise of providing more reliable and efficient solutions for financial investment decision-making (Cheng et al., 2019).