Abstract

Deep learning has emerged as a cornerstone in diverse scientific domains, demonstrating profound implications both in academic research and conventional applications. The utilization of deep learning has branched to the financial sector for stock prediction, management of assets, credit score analysis, etc. During the end of the financial fiscal year, top executives present their companies’ growth and losses in earnings calls. These earnings calls invariably webcasted, furnish stakeholders with insights into a firm’s fiscal performance during a given period. These audios along with transcripts are published on the company website for the general public. If these audio and transcripts are collected over time, they are capable of forming an extensive audio and sentiment analysis database, which could be utilized to train a model effectively. The quality and quantity of the dataset are essential for a machine-learning model. The availability of these discussions in both audio and text formats on corporate portals presents an invaluable repository for longitudinal audio and sentiment analysis. The clarity in the statistics of the earnings calls would also predict the bona fide outlook of the companies’ financial prospects. This thesis work will focus on adding and extending the earnings call data for the companies for MAEC dataset and create a diverse, updated machine learning dataset. This would be further followed by the proposal of a sentiment analysis tool inspired by the deep neural network to label the data collected and form the baseline method for future work to compare. The established algorithm would provide a pathway for a dataset and data annotation that would further be utilized by the models out there, to perform financial analysis and factual correctness of the data presented, and draw appropriate conclusions. The addition to this dataset and the creation of an algorithm would involve rigorous empirical investigation and the iterative design of a methodological framework conducive to the systematic accrual and annotation of earnings call records.

Library of Congress Subject Headings

Artificial intelligence--Financial applications; Corporate profits--Data processing; Deep learning (Machine learning)

Publication Date

12-2023

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering

College

Kate Gleason College of Engineering

Advisor

Dongfang Liu

Advisor/Committee Member

Michael Zuzak

Advisor/Committee Member

Andres Kwasinski

Campus

RIT – Main Campus

Plan Codes

CMPE-MS

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