An Experimental Study on the Effectiveness of Artificial Neural Network-Based Stock Index Prediction

Abstract

Artificial Neural Network (ANN) is a promising tool for solving many recognition problems and has been a popular choice for researchers during the last decade. Machine learning tools such as Multi-Layer Perceptron (MLP) have proven effective in solving classification problems. Long Short Term Memory (LSTM) has been deemed to be the state of the art of the ANN family, which is specialized in tracking time series related data. The capability of LSTM as a powerful tool for making profit has been reported, along with its reputation for stock market prediction. In this study, Keras was used as a neural network library on top of Tensorflow as a machine learning backend using the Dow Jones Index (DJI) as the data source for the MLP and LSTM analyses. Our experimental results reveal that the prediction ability of MLP and LSTM possesses similar accuracy to the benchmark when providing only trading price and volume as the input data. This paper further discusses some difficulties in training MLP and LSTM that may have reduced the system capability to reach its expected potential.