  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.
