Stock Price Prediction Models using Neural Networks

Due to the nonlinearity and high volatility of stock prices, it is challenging to predict stock prices. Certainty in investment decisions is the main tool for evaluating stock markets. Hence, a reliable and accurate model for predicting stock prices is desperately needed since it could inform investors about stock prices, which could ultimately result in profitable investments. This thesis introduces three novel neural networks models: BiCuDNNLSTM-1dCNN, BiCuDNNGRU-1dCNN and BiCuDNN(SLSTM-GRU)-1dCNN for stock price prediction. The models' competitive performance will make them useful for predicting time series and used as an instance for most investors to constructively evade financial hazards in investments.