Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

—The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories

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

2023 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium