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Keehyung Kim | University of Oklahoma
<aside> <img src="/icons/megaphone_gray.svg" alt="/icons/megaphone_gray.svg" width="40px" /> Welcome to AFM 346: Applications of Predictive Analytics in Accounting and Finance! 😊
This course was taught by me during Spring 2024 for Undergraduate students enrolled at School of Accounting and Finance, University of Waterloo. Note that some materials were offered exclusively to the enrolled students only.
If you use any part of the materials, please kindly reference this site (http://keehyung.com) and the author (Keehyung Kim). Enjoy!
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Models help us to think better about the world: they provide insights into complex, large, or dynamic data. Sometimes models also predict the value of important variables - such as price, sales, and risk. "Prediction" here implies forecasting values that have not yet been categorized or that can only be measured in the future. For example, is this transaction fraudulent? As another example, how many products will be sold next quarter?
This course introduces machine learning and its applications for prediction in accounting and finance. We will cover a variety of supervised learning methods from linear regression through neural networks. A main theme of this course is building machine learning workflows - including data transformation, model training, prediction, and evaluation. We will also discuss methods for exploratory data analysis, feature engineering, and assessing results (including performance, potential overfitting, and bias vs. variance). Some application areas include loan defaults, product pricing, product sales, and market trend.
Please check “Course Outline” pdf file for more details.
The proposed course schedules here are tentative, which is subject to change based on progress.
All the course materials (e.g., slides, assignments) can be found here: https://www.dropbox.com/scl/fo/4mjf11xzeltxaka9yjuzg/ADMxZrckuM61Qso63rSHcPY?rlkey=bmpwqp7zkr47pe0i39mewppsh&st=vk7g59ac&dl=0