The Impact Of Twitter And News Count Variables On Stock Price Prediction Via Neural Networks

 This study examines how Twitter and News Count variables generated by Bloomberg L.P. when utilized as inputs impact the stock price prediction accuracy of two distinct neural network types. The neural network types that are examined are Multi-Layer Perceptron neural networks and Long Short-Term Memory neural networks. Besides, all models were tested on two distinct periods, one without any market panic, the other including a prolonged period of market panic. The results suggest that the inclusion of Twitter and News Count variables significantly improve Multi-Layer Perceptron networks, but no significant improvement occurred for Long-Short Term Memory networks. Regarding periods of panic and no panic, the inclusion of the variables improved stock price prediction via neural networks in both scenarios.