Information technologies for forecasting financial markets with neural networks - LUTIUK, LEV; HNATCHUK, YELYZAVETA; PONOCHOVNA, OLENA (Herald of Khmelnytskyi National University. Technical sciences, 2024)
Forecasting financial markets has long been one of the most important tasks for investors and traders. The dynamism and complexity of markets, numerous influencing factors such as economic news, geopolitical events and behavioral aspects of market participants, make this area extremely difficult to analyze using traditional methods. With the advent of artificial intelligence technologies, namely neural networks, it has become possible to more effectively analyze large volumes of data and find hidden patterns that cannot be detected by other methods.Neural networks have become one of the key technologies in modern financial analysis due to their ability to process nonlinear dependencies and work with large data sets. These models imitate the work of the human brain, which allows them to detect even the most complex relationships in financial time series, such as changes in asset prices, currency fluctuations or market risk analysis. Different types of architectures, such as recurrent neural networks, convolutional neural networks, temporal convolutional models, and transformers, allow for efficient solutions to time series analysis, pattern detection, and textual information analysis. In particular, recurrent networks, such as LSTM and GRU, are effective for modeling long-term dependencies, while CNNs can detect local patterns in price charts. Transformers and language models, such as GPT and BERT, provide the ability to process textual information, which allows for news analysis and market impact assessment.Due to their flexibility, adaptability and ability to self-optimize, neural networks have become a reliable tool for predicting market trends.In this article, we will take a detailed look at how neural networks are used to predict financial markets, what types of architectures are most effective for financial analysis, and what challenges researchers and practitioners face when implementing them.
Keywords: neural networks; financial time series; lag problem; hybrid models; neural language models