Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016

TL;DR

This study assesses an ANN-based prediction system for Shanghai Stock Market prices during late 2016, demonstrating its ability to predict prices with low error rates compared to other methods.

Contribution

It evaluates the performance of a specific neural network configuration for stock prediction and compares its accuracy to traditional methods.

Findings

Neural network predicted prices with mean absolute percentage errors as low as 1.95%.

The optimal network configuration was identified as 5:21:21:1 with 80% training data.

ANN outperformed traditional trend-based methods in predicting actual stock prices.

Abstract

This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price prediction remains an important quest for investors and researchers. This research used an Artificial Intelligence system, being an Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation for prediction, unlike other methods such as technical, fundamental or time series analysis. While these alternative methods tend to guide on trends and not the exact likely prices, neural networks on the other hand have the ability to predict the real value prices, as was done on this research. Nonetheless, determination of suitable network parameters remains a challenge in neural network design, with this research settling on a configuration of 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction, as already determined in a previous research by the author. The comparative results indicate that neural network can predict typical stock market prices with mean absolute percentage errors that are as low as 1.95% over the ten prediction instances that was studied in this research.