•Our model learns high-level features from super-high dimensional time-series data.•All companies’ price data in the relevant country’s open market are used as input.•The trend sampling mini-batch sampling method enhances forecasting performance.•Experimental results show that our model adapts to real-time patterns.•The model outperforms others with the same training and testing conditions. Forecasting stock market indexes is an important issue for market participants, because even a small improvement in forecast accuracy may lead to better trading decisions than those of other participants. Rising interest in deep learning has led to its application in stock market forecasting. However, it is still challenging to use market-size time-series data to predict composite index prices. In this study, we propose a new stock market forecasting framework, NuNet, which can successfully learn high-level features from super-high dimensional time-series data. NuNet is an end-to-end integrated neural network framework consisting of two feature extractor modules, a super-high dimensional market information feature extractor and a target index feature extractor. In addition, we propose a mini-batch sampling technique, trend sampling, which probabilistically samples more recent data when training. Furthermore, we propose a novel regularization method, called column-wise random shuffling, which is a data augmentation technique that can be applied to convolutional neural networks. The experiments are comprehensively carried out in three aspects for three indexes, namely S&P500, KOSPI200, and FTSE100. The results demonstrate that the proposed model outperforms all baseline models. Specifically, for the S&P500, KOSPI200, and FTSE100, the overall mean squared error of our proposed model NuNet(DA, T) is 60.79%, 51.29%, and 43.36% lower than that of the baseline model SingleNet(R), respectively. Moreover, we employ trading simulations with realistic transaction costs. Our proposed model outperforms the buy-and-hold strategy being an average of 2.57 times more profitable in three indexes.