Anderson Rioba Ondieki^1,, George Onyango Okeyo¹, Ann Kibe¹

Stock Exchange Prediction using neural networks has been an interesting research problem whereby many researchers have developed a keen interest in prediction of future values and trends. Little research has been done to apply and improve prediction models based on newer and impactful variables to show that mining opinions and sentiments from the information shared in Twitter platform can be converted into statistical values and applied as inputs in a neural network together with other inputs to facilitate an improvement in the accuracy of predictions of stock prices and movements. In this research, two stocks were selected on the basis of their social media communication in twitter and this information was used as additional feature by deploying a supervised learning approach to compute daily company twitter sentiment score for improving prediction purposes in neural networks. The daily twitter sentiment scores were computed in a supervised learning algorithm by use of WordNet and Sentiwordnet lexicons for classification and scoring. Through experimentation with different sets of hidden layers and 70% training set. 15% validation set and 15 % test set, the research applied two Non Linear Autoregressive Neural Network with Exogenous Inputs (NARX) models which were trained using Levenberg-Marquadt back propagation. The results showed that adding lexicon based twitter sentiment scores as additional inputs to other company stock variables for stock price prediction improved the prediction accuracy and resulted to a more accurate NARX model.

artificial neural networks, accuracy, sentiment scores, Sentiwordnet, Wordnet