Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction
arXiv:1603.07893
Abstract
The effectiveness of long short term memory networks trained by backpropagation through time for stock price prediction is explored in this paper. A range of different architecture LSTM networks are constructed trained and tested.
6 pages
References in corpus (1)
Cited by in corpus (6)
- Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
- Mining Illegal Insider Trading of Stocks: A Proactive Approach
- Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
- A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models
- Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network
- Using Machine Learning and Alternative Data to Predict Movements in Market Risk