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Financial forecasting is a sequential process that is dependent on the development of valid assumptions and reviewing data integrity as the model is developed and evaluated. A systematic approach to assembling and evaluating the data based on the design objectives and assumptions are critical. In short, forecasting is the foundation of your valuation. What happens when your initial perceptions and observations require revision during the process to achieve the design objectives of the model? Mark Shirley will illustrate a systematic and ordered approach to developing a financial forecast model and advise on ways to reduce the occurrence of systemic design errors. Model design begins with identifying the objectives and developing the assumptions fundamental to the financial model. Learn to assemble the data, incrementally evaluate, and document each stage of your model development. With practical Excel-based examples, see the development of a simple financial model based on this systematic approach.
Program Agenda
Getting started:
Define the modeling objectives
Developing the assumptions
Sufficient relevant data
Sample size
Evaluate data integrity and reliability
Building the model
Designing the model to address the objectives or stated problem
Continuity with historic data
Integrated progressive sub-models
Movable and scalable models
Evaluating precision
Precision vs. Reasonable certainty
Establishing a baseline
Minimizing forecasting errors
Identifying issues
Data based errors
Calculation Errors
Outlier values
Anomalies and random events
Common sense and professional judgement
Learning Objectives
Define the modeling objectives and develop forecast assumptions.
Describe the critical importance of sufficient relevant data (quantity and quality of sample)
Illustrate the approach to financial modeling as a series of progressive sub-models
Identify outliers, anomalies, and random elements in the assembled data and output
List the elements of constructing a movable and scalable financial forecast which is an extension of the historic data
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