Deep learning algorithms for classification of financial time series data
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 effec...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140312 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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