Forecasting The JSE Top 40 Using Long Short-Term Memory Networks
This addresses the challenge of financial time series forecasting for investors and researchers, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of forecasting the JSE Top 40 index using a long short-term memory (LSTM) network, finding that it outperformed a seasonal autoregressive integrated moving average (SARIMA) model in predicting intraday directional movements and index close prices.
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.