Advanced Accounting: Strategic Financial Analysis and Predictive Modeling #252002

Upon successful completion of this course, participants will be able to:

• Integrate advanced financial statement analysis techniques to uncover underlying business performance and risks.

• Apply time series analysis and regression techniques to identify trends, patterns, and relationships in financial data.

• Design, build, and interpret sophisticated predictive financial models for forecasting, valuation, and scenario planning.

• Utilize Monte Carlo simulation and other methods to model uncertainty and assess potential financial outcomes.

• Evaluate the strategic implications of M&A, capital allocation, and investment decisions using data-driven models.

• Communicate complex financial insights and model outputs effectively to executive leadership and stakeholders.

This advanced, technical course is designed for experienced finance and accounting professionals:

• CFOs, Finance Directors, and VPs of Finance

• Senior Financial Analysts and Managerial Accountants

• Corporate Finance Professionals and Treasury Managers

• Financial Controllers seeking a more strategic role

• Investment Analysts and Portfolio Managers

• Management Consultants specializing in finance

• FP&A (Financial Planning & Analysis) Managers and Specialists

Prerequisite: A strong understanding of financial accounting, corporate finance, and intermediate Excel proficiency is essential.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Advanced Financial Analysis & Diagnostic Foundations

• Morning Session: Beyond the Ratios – Deep Dive Analysis

  • Advanced Profitability Analysis: Deconstructing ROE and ROIC using the DuPont Pyramid.

  • Quality of Earnings Analysis: Identifying accounting distortions and sustainable earnings.

  • Cash Flow Analysis: Assessing financial health and liquidity through detailed cash flow statement dissection.

• Afternoon Session: Forecasting Fundamentals & Time Series Analysis

  • Introduction to Predictive vs. Descriptive Analytics.

  • Time Series Techniques: Moving averages, trend analysis, and seasonality decomposition for revenue and expense forecasting.

  • Workshop: Diagnose the financial health of a complex company and establish a baseline forecast.

Day 2: Building Predictive Models with Regression Analysis

• Morning Session: Correlation and Regression Fundamentals

  • Understanding Covariance, Correlation, and Causation.

  • Building and Interpreting Single and Multiple Linear Regression Models.

  • Identifying key operational and macroeconomic drivers of financial performance.

• Afternoon Session: Model Application and Validation

  • Using Regression to Forecast Key Line Items (e.g., sales as a function of marketing spend, GDP).

  • Testing Model Robustness: R-squared, p-values, and residual analysis.

  • Hands-On Lab: Build a multi-variable regression model to predict a company’s sales and validate its accuracy.

Day 3: Valuation Modeling & Scenario Analysis

• Morning Session: Advanced Discounted Cash Flow (DCF) Modeling

  • Building a Integrated Three-Statement Projection Model.

  • Calculating Free Cash Flows and Terminal Value.

  • Sensitivity Analysis: Using Data Tables to model impact of WACC and growth rate changes on valuation.

• Afternoon Session: Scenario and What-If Analysis

  • Building Best Case, Base Case, and Worst Case Scenarios.

  • Leveraging Excel’s Scenario Manager and Goal Seek for strategic planning.

  • Case Study: Value a company using DCF and analyze the valuation range under different strategic scenarios.

Day 4: Modeling Uncertainty & Risk with Monte Carlo Simulation

• Morning Session: Introduction to Probabilistic Modeling

  • The Limits of Deterministic Models and the Need for Probabilistic Outputs.

  • Defining Probability Distributions for Key Inputs (e.g., revenue growth, input costs).

• Afternoon Session: Implementing Monte Carlo Simulations

  • Step-by-Step Guide to Running Simulations in Excel (using plugins like @Risk or Crystal Ball).

  • Interpreting Output: Analyzing probability distributions of NPV, IRR, and other outputs.

  • Practical Workshop: Apply Monte Carlo simulation to a capital budgeting decision to assess the probability of project success.

Day 5: Advanced Applications & Capstone

• Morning Session: Modeling for M&A and Strategic Decisions

  • Building a Merger Model: Assessing accretion/dilution and synergies.

  • Real Options Analysis: Valuing strategic flexibility.

• Afternoon Session: Capstone Project & Communication

  • Capstone Challenge: Participants receive a complex dataset and must build a predictive model to solve a strategic business problem (e.g., evaluate a new market entry).

  • Data Visualization: Creating executive-level dashboards to present model findings.

  • Telling the Story: How to communicate the assumptions, limitations, and strategic implications of your model to the C-suite.

  • Course Recap and Action Plan.