Predicting Commentaries on a Financial Report with Recurrent Neural Networks

Abstract

Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose an encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and decoder respectively provides a higher accuracy. Conclusion: We observe that the LSTM highly depends on long term memory particularly in learning from real commentaries.