Deep learning algorithms for classification of financial time series data - Lim, Kai Wei (Nanyang Technological University, 2020)
Stock trading markets are infamous for being unstable and complicated, and there is much enthusiasm by many to search for a dependable, unerring model that can be used to trade the stock markets. Long short-term memory (LSTM) networks are a variant of recurrent neural networks (RNN) and are effective in modelling time series data. Specifically, LSTM is able to circumvent the issue of long-term dependency as it has a unique unit structure for storage making it a suitable choice for financial time series classification. This paper publishes the results of training a LSTM network to classify the daily movement of 10 technology sector stocks over 5 years. The results were contrasted with Gated Recurrent Unit (GRU) networks and Recurrent Neural Networks (RNN). From the results, LSTM persistently surpasses GRU and RNN and obtains higher classification accuracies.