Forecasting Dynamics of Stock Based on Sparse Neural Networks and Large Language Models

Abstract

In the context of modern economic challenges, the effective functioning of the stock market becomes crucial for the sustainable development of the Russian economy. Attracting investments to the real sector requires creating effective tools for market information analysis, which involves processing large volumes of heterogeneous data under conditions of high volatility and geopolitical instability. The aim of the research is to develop an algorithm for building a sparse neural network. This model automatically eliminates insignificant connections between neurons for predicting stock market dynamics. The proposed approach
is based on the method of solving a single-point inverse problem with a minimization of the sum of absolute parameter values, which allows reducing the model’s dimensionality. The scientific novelty of the research comprises two aspects. First, the work explores the possibility of using new factors generated by a large language model (an artificial intelligence system for text processing) for predicting stock market dynamics. Second, an original algorithm for constructing a sparse neural network has been developed. The research tested two main hypotheses. The first hypothesis aimed to verify the advantages of sparse neural networks over fully connected architectures in prediction accuracy. The second hypothesis investigated the effectiveness of using features extracted by large language models from unstructured text sources for financial forecasting.
Experimental verification on three tasks of stock price and dividend forecasting confirmed both hypotheses. The sparse architecture demonstrated an advantage over fully connected models in prediction accuracy and computational efficiency. Automatic feature selection revealed the relevance of macroeconomic characteristics extracted by the large language model, confirming the promise of integrating modern natural language processing technologies into financial forecasting. The obtained results can be used to form effective strategies of stock market behavior and create intelligent decision support systems. In addition, sparse models can be used in solving other economic problems, including portfolio optimization and
financial performance management.