Forecasting The JSE Top 40 Using Long Short-Term Memory Networks

TL;DR

This paper demonstrates that Long Short-Term Memory (LSTM) networks outperform traditional seasonal ARIMA models in forecasting the JSE Top 40 index's intraday movements and closing prices, highlighting the effectiveness of neural networks in financial time series prediction.

Contribution

It introduces the application of LSTM networks to JSE Top 40 forecasting and compares its performance against seasonal ARIMA models, providing empirical evidence of neural networks' superiority.

Findings

LSTM outperforms seasonal ARIMA in intraday movement prediction

LSTM achieves better accuracy in closing price forecasting

Neural networks show promise for financial time series forecasting

Abstract

As a result of the greater availability of big data, as well as the decreasing costs and increasing power of modern computing, the use of artificial neural networks for financial time series forecasting is once again a major topic of discussion and research in the financial world. Despite this academic focus, there are still contrasting opinions and bodies of literature on which artificial neural networks perform the best and whether or not they outperform the forecasting capabilities of conventional time series models. This paper uses a long-short term memory network to perform financial time series forecasting on the return data of the JSE Top 40 index. Furthermore, the forecasting performance of the long-short term memory network is compared to the forecasting performance of a seasonal autoregressive integrated moving average model. This paper evaluates the varying approaches presented in the existing literature and ultimately, compares the results to that existing literature. The paper concludes that the long short-term memory network outperforms the seasonal autoregressive integrated moving average model when forecasting intraday directional movements as well as when forecasting the index close price.