Predicting financial trends of stock indexes is important for investors to reduce risk on investment and efficient decision making if the prediction is made accurately. Researchers, in recent times have applied deep learning approaches in this field which have essentially beaten conventional machine learning approaches. To overcome the issue of overfitting we presented a new data augmentation approach in our GRU based StockNet model consisting of two modules. Injection module to prohibit overfitting and Investigation module for stock index forecasting. The proposed approach has been validated on Indian stock market (CNX-Nifty). Proposed StockNet-c model produces 65.59%, 27.30% and 14.89 % less test loss in terms of RMSE, MAE and MAPE respectively, in comparison to TargetNet model where overfitting prohibition injection module is missing. •Stock index prediction with data augmentation approach to prohibit overfitting.•StockNet model with GRU network with injection and investigation modules.•Proposed models shows the best result in CNX Nifty dataset.•The prediction results of the proposed model have statistical significance.