The prediction of financial time series data is a challenging task due to the unpredictable behaviours of investors that are influenced by a multitude of factors. In this paper, we present a novel deep Long Short-Term Memory (LSTM) based time-series data modelling for use in stock market index prediction. A dataset comprised of six market indices from around the world were chosen to demonstrate the robustness in varying market conditions with an aim to forecast the next day closing price. With experimental results showing an average annual profitability performance of up to 200%, our method demonstrates its feasibility and significant results in time-series modelling and prediction of financial markets.