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Topline Modeling

Normal

Educational

$295 | $395
8/12/2020
4.8
4.8
4.8
Finance
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Financial professionals are frequently tasked with creating effective models that are accurate and dynamic. Top-Line modeling or revenue modeling brings a specific set of challenges. The power of an effective revenue model comes from the ability to account for volatility and variable inputs. This course introduces the key techniques and best practices for building revenue specific financial models that account for seasonality and uncertainty. Through hands-on examples in Excel, participants build from scratch financial models that produce baseline and dynamic outputs in response to changes in real-world scenarios. Examples and illustrations utilize financial and treasury applications.

Learning Objectives

• Ability to estimate expected revenue growth rates

• Ability to conduct time series analysis and forecasting- soothing and seasonality

• Ability to conduct regression analysis and forecasting

• Ability to model revenue uncertainty using simulations

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Speaker

Bill Hu, PhD, CFA, CTP
Professor of Finance, Arkansas State University
Arkansas State University
As a recipient of the Excellence in Teaching award from the Neil Griffin College of Business at ASU, Dr. Hu keeps his classes current and relevant by reaching out to the finance industry onsite and online covering topics such as Revenue Forecasting, Financial Modeling, Integrated Financial Statements, Data Analytics and Visualization, Simulations, Big Data, and Excel. Over the years, he has trained thousands of finance professionals on these topics.

Dr. Hu's research includes pricing strategies, market microstructure, behavioral finance, etc. When writing doctoral essays in 2008, Dr. Hu applied machine learning (ML) to classify stock spam emails and disseminated the impacts of spam messages on financial markets in multiple research papers. Recently, he has started projects related to cryptocurrencies. Prior to the study of finance, Dr. Hu was a nanotechnologist and had a variety of scholarly articles and patents in the field of chemistry.