Natural Language Financial Forecasting: The South African Context - Katende, Simon (Department of Statistical Sciences, 2021)
The stock market plays a fundamental role in any country's economy as it efficiently directs the flow of savings and investments of an economy in ways that advances the accumulation of capital and the production of goods and services. Factors that affect the price movement of stocks include company news and performance, macroeconomic factors, market sentiment as well as unforeseeable events. The conventional prediction approach is based on historical numerical data such as price trends and trading volumes to name a few. This thesis reviews the literature of Natural Language Financial Forecasting (NLFF) and proposes novel implementation techniques with the use of Stock Exchange News Service (SENS) announcements to predict stock price trends with machine learning methods. Deep Learning has recently sparked interest in the data science communities, but the literature on the application of deep learning in stock prediction, especially in emerging markets like South Africa, is still limited. In this thesis, the process of labelling announcements, the use of a more statistically relevent technique called the event study was used. Classical textual preprocessing and representation techniques were replaced with state-of-the-art sentence embeddings. Deep learning models (Deep Neural Network (DNN)) were then compared to Classical Models (Logistic Regression (LR)). These models were trained, optimized and deployed using the Tensorflow Machine Learning (ML) framework on Google Cloud AI Platform. The comparison between the performance results of the models shows that both DNN and LR have potential operational capabilites to use information dissemination as a means to assist market participants with their trading decisions.