School of Business and Management Department of Information Systems, Business Statistics and Operations Management 188 Department of Information Systems, Business Statistics and Operations Management AI's Impact on the Financial Market Supervisor: CHEN Yanzhen / ISOM Student: WONG Yui Sing / IS ZHENG Bingyin / RMBI Course: UROP1100, Fall UROP1100, Fall Since 2014, AI-assisted or Auto-generated news has become more important in mainstream media, and the amount of financial news reported, and the number of companies covered in news increased. Since media is one of the most important resources for individual traders to receive information, we looked into the impacts on financial markets after the rise of Robo-Journalism through this project and learn the methods of data manipulation at the same time. Our project can be divided into two steps. The first one is data collection and the second one is linear regressionmodelling. This semester, we completed the data collection part. We first downloaded articles from Factiva and then prof Chen helped grab the key information, which shows whether the articles were generated by AI or human beings. Secondly, we classified the articles into 3 categories: hybrid, robot, and human. Lastly, we compared the companies’ names collected from Factiva with the names used for merge. Next, we will build linear regression models based on the data collected. Applicability of Deep Learning Solutions to Business Problems Supervisor: KWOK James Sai Ho / ISOM Student: JIA Tongyao / ELEC Course: UROP1100, Fall This report aims to investigate the selection of specifications of convolutional neural networks (CNNs) for binary classification, including distinct types of input datasets and model structure hyperparameters. In this report, test accuracies are recorded and optimized by trying different parameters mainly based on the structure provided by keras_tuner on 14 different datasets. It is found that the number of layers, number of neurons, activation functions, and data spilt ratio will contribute to the final test accuracies. These results will provide a guideline for the CNN model building for binary classification problems.
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