The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach)

Subject Areas : Financial engineering

Amir  Sharif far ¹ (PhD Student, Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Maryam  Khalili Araghi ² (Assistant Professor, Department of Business Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Iman  Raeesi Vanani ³ (Department of industrial management. Faculty of Management and Accounting, Allameh Tabataba'i University. Tehran. Iran.)
Mirfeiz  Fallah ⁴ (Department of Financial Management, central Tehran Branch, Islamic Azad University. Tehran, Iran and member of Modern Financial Risk Research Group)

Received: 2021-04-05 Accepted : 2021-05-01 Published : 2021-09-23

Keywords: Recurrent neural network, stock price prediction, deep learning, Long Short-Term Memory,

Abstract :

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models.