Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study

Deep learning model for predicting stock prices in Tanzania

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and Technologye prediction models help to provide investors with tools for making better data driven decisions. Machine learning and deep learning techniques have been successively utilized in various countries to develop these models. However, there is a shortage of literature on the efforts to exploit these techniques to forecast stock prices in Tanzania. Hence, this study was conducted to address this gap. The study selected active companiesfrom the Dar es Salaam Stock Exchange (DSE) and developed Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models to forecast the next day closing prices of the companies. Long Short-Term Memory was the overall best model with a Root Mean Square Error (RMSE) of 4.1818 and Mean Absolute Error (MAE) of 2.1695. Findings revealed that it was significant to account for the number of outstanding shares of each company when developing a joint model for forecasting the stock prices of multiple companies. Specifically, LSTM attained an RMSE of 10.4734 before accounting for outstanding shares and 4.7424 after accounting for outstanding shares, showing an improvement of 54.72%. Furthermore, findings showed that investors’ participation attributes helped to improve prediction accuracy. Specifically, LSTM realized an RMSE of 4.1818 when these attributes were appended from that of 4.7424 without them, showing an improvement of 11.8%. The resulting model was deployed in a web-based prototype, whereby, end-user validation results indicated that 76% of respondents rated the system as High in terms of its forecasting ability. In future, the study recommends exploration of more features