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About Predicting Stock Market Movement With Deep RNNS: Jason Poulos
This document describes applying deep recurrent neural networks (RNNs) to predict stock market movement based on news headlines. Specifically, it implements a 12-layer gated recurrent unit (GRU) model that achieves 54% test accuracy on a benchmark dataset, close to state-of-the-art performance. The document outlines the data and models, including a 3-layer GRU baseline model, describes hyperparameter tuning, and benchmarks model performance against other algorithms.
Detailed Information
| Author: | ['Ashu Gupta'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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