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Top Line Modeling

Virtual Seminar


Thursday, September 20 & 27, 2018
10:00 AM - 12:00 PM Eastern Standard Time

$525 | $625
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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

  • Estimate expected revenue growth rates

  • Conduct time series analysis and forecasting- soothing and seasonality

  • Conduct regression analysis and forecasting

  • Model revenue uncertainty using simulations


Participants should have an understanding of the basics of financial modeling 
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Bill Hu, Ph.D., CFA
Associate Professor of Finance, Arkansas State University

As the recipient of the Excellence in Teaching award from the College of Business at ASU, Dr. Hu has taught graduate and undergraduate level Corporate Finance, Financial Statement Analysis with Excel, International Finance, Global Investments, and others, at AACSB-accredited schools. Dr. Hu became a CFA charterholder in 2011.

Dr. Hu mentors entrepreneurs from various business areas and helps them formulate corporate financial strategies. Dr. Hu's research includes issues in market microstructure, corporate finance, investment, behavioral finance, and international finance. He has published articles in quality journals such as Journal of Financial Research, Journal of Behavioral Finance, Journal of Business Finance and Accounting, Pacific-Basin Finance Journal, Journal of Financial Education, and other refereed journals.