A hybrid artificial intelligence model that combines two well-established deep learning techniques has improved the accuracy of financial market forecasts across major stock indices and so-called cryptocurrency, according to work in the International Journal of Reasoning-based Intelligent Systems.
The researchers designed the model, CLSTM-HN, to address a long-standing problem in financial forecasting: balancing the detection of short-term market movements with the recognition of longer-term trends. The researchers tested the system on publicly available data and achieved a forecasting error 15% to 20% lower than that of conventional long-short-term memory (LSTM) models. They also saw an improvement in the accuracy of predicting whether prices would rise or fall by 10% to 14%.
Financial markets are difficult to predict because prices are volatile, noisy and subject to sudden structural shifts. Traditional statistical approaches often rely on assumptions about market behavior that break down during periods of instability.
The new system combines a convolutional neural network (CNN), which identifies local patterns within data, with an LSTM network, which retains information over long sequences. An adaptive alignment mechanism converts CNN outputs into a form the LSTM can process more effectively, while the training objective rewards both numerical accuracy and correct prediction of market direction.
