Open AccessStock Price Prediction using Fractional Gradient-Based Long Short Term Memory
Author(s) -
Narinderjit Singh Sawaran Singh,
Sugandha,
Trilok Mathur,
Shivi Agarwal,
Kamlesh Tiwari
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1969/1/012038
Subject(s) - autoregressive conditional heteroskedasticity , backpropagation , autoregressive integrated moving average , artificial neural network , computer science , stock market index , econometrics , heteroscedasticity , stock (firearms) , time series , moving average , volatility (finance) , artificial intelligence , stock market , machine learning , mathematics , engineering , paleontology , mechanical engineering , horse , computer vision , biology
Deep Learning is considered one of the most effective strategies used by hedge funds to maximize profits. But Deep Neural Networks (DNN) lack theoretical analysis of memory exploitation. Some traditional time series methods such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) work only when the entire series is pre-processed or when the whole data is available. Thus, it fails in a live trading system. So, there is a great need to develop techniques that give more accurate stock/index predictions. This study has exploited fractional-order derivatives’ memory property in the backpropagation of LSTM for stock predictions. As the history of previous stock prices plays a significant role in deciding the future price, fractional-order derivatives carry the past information along with itself. So, the use of Fractional-order derivatives with neural networks for this time series prediction is meaningful and helpful.