Introduction

In today’s fast-paced financial landscape, professionals need the ability to model and analyze data to make informed decisions that drive profitability and minimize risk. Financial data modeling and analysis involve using statistical, mathematical, and computational techniques to predict financial outcomes, assess risks, and evaluate investment opportunities. This course will provide participants with hands-on experience in building financial models, performing advanced financial analysis, and visualizing financial data using tools such as Excel, Python, and Power BI. Through real-world examples, participants will gain a comprehensive understanding of financial modeling techniques used by financial analysts, investment professionals, and corporate finance teams.

Objectives

By the end of this course, participants will:

• Understand the fundamentals of financial data modeling, including building models for forecasting, budgeting, and valuation.
• Gain proficiency in Excel for financial analysis and modeling, including advanced functions, formulas, and tools like Power Pivot and Power Query.
• Learn to construct and evaluate financial models using Python for regression analysis, optimization, and Monte Carlo simulations.
• Understand key financial concepts such as discounted cash flow (DCF), net present value (NPV), and internal rate of return (IRR).
• Master techniques for creating financial forecasts, budgeting models, and investment analysis.
• Develop skills to present financial data and insights using Power BI and other visualization tools.

Who Should Attend?

This course is ideal for:

• Financial analysts and accountants who want to improve their financial modeling and data analysis skills.
• Investment professionals and portfolio managers looking to use data-driven methods for investment analysis and forecasting.
• Corporate finance teams and managers seeking to enhance their understanding of financial data analysis and decision-making.
• Anyone interested in mastering financial modeling techniques and learning how to leverage data analysis tools to optimize financial decision-making.

Day 1: Introduction to Financial Data Modeling

Morning Session: Overview of Financial Modeling and Analysis

• Key concepts in financial modeling: Definitions, uses, and types of financial models (forecasting, budgeting, valuation, etc.).
• The financial modeling process: Building assumptions, developing financial statements, and forecasting future performance.
• Overview of financial statements: Income statement, balance sheet, and cash flow statement.
• Hands-on: Examine and analyze financial data from a publicly traded company to build a simple financial model.

Afternoon Session: Financial Analysis with Excel

• Introduction to key Excel functions for financial analysis: NPV, IRR, PMT, and FV.
• Advanced Excel tools for financial analysis: PivotTables, Power Query, and Power Pivot.
• Building a basic financial forecast model: Revenue projections, cost estimates, and margin analysis.
• Hands-on: Develop a simple financial model for forecasting revenue and expenses based on historical data.

Day 2: Building Advanced Financial Models in Excel

Morning Session: Creating Financial Models for Forecasting and Budgeting

• The components of a financial forecasting model: Assumptions, historical data, and projected financials.
• Building income statement, balance sheet, and cash flow models.
• Techniques for adjusting assumptions and creating different financial scenarios (e.g., best-case, worst-case, and base-case forecasts).
• Hands-on: Create a detailed 3-year financial forecast, including income statement, balance sheet, and cash flow projections.

Afternoon Session: Valuation Models and Investment Analysis

• Introduction to Discounted Cash Flow (DCF) modeling: Calculating the present value of future cash flows.
• Valuation techniques: DCF, comparable company analysis (CCA), and precedent transaction analysis (PTA).
• Calculating key investment metrics: NPV, IRR, payback period, and profitability index.
• Hands-on: Build a DCF model for valuing a company, using forecasted cash flows and discount rates.

Day 3: Financial Data Modeling with Python

Morning Session: Introduction to Python for Financial Data Analysis

• Introduction to Python libraries for financial data analysis: pandas, NumPy, matplotlib, and SciPy.
• Collecting financial data: Using APIs and scraping data from financial websites.
• Performing basic statistical analysis: Mean, median, variance, and standard deviation.
• Hands-on: Use Python to analyze historical stock price data, calculating basic financial metrics such as returns and volatility.

Afternoon Session: Advanced Financial Modeling with Python

• Building regression models for forecasting financial data: Linear regression and multiple regression analysis.
• Optimization techniques: Using Python to optimize financial portfolios and calculate optimal asset allocations.
• Introduction to Monte Carlo simulations: Running simulations to model financial risk and uncertainty.
• Hands-on: Build a regression model to forecast a financial metric and perform a Monte Carlo simulation to assess portfolio risk.

Day 4: Advanced Topics in Financial Data Modeling

Morning Session: Risk Analysis and Management

• Introduction to financial risk analysis: Value at Risk (VaR), expected shortfall, and scenario analysis.
• Using Monte Carlo simulations for risk modeling: Simulating market conditions and portfolio performance under different scenarios.
• Measuring risk-adjusted returns: Sharpe ratio, Sortino ratio, and alpha.
• Hands-on: Use Python to perform risk analysis on an investment portfolio and calculate risk-adjusted returns.

Afternoon Session: Integrating Financial Models with Power BI

• Using Power BI for financial data visualization: Creating interactive financial reports and dashboards.
• Connecting financial models in Excel or Python to Power BI for real-time analysis and reporting.
• Best practices for presenting financial data: Ensuring clarity, accuracy, and actionable insights.
• Hands-on: Build an interactive financial performance dashboard in Power BI using data from your financial models.

Day 5: Financial Decision Making and Presentation

Morning Session: Financial Decision Making

• Using financial models to support decision-making: Evaluating investment opportunities, mergers and acquisitions, and capital budgeting.
• Sensitivity analysis: Understanding how changes in assumptions affect financial outcomes.
• Decision trees and scenario analysis: Tools for making decisions under uncertainty.
• Hands-on: Use scenario analysis to assess the impact of changes in key assumptions on a financial model (e.g., changes in revenue growth or interest rates).

Afternoon Session: Presenting Financial Analysis to Stakeholders

• Communicating financial insights effectively: Creating compelling presentations for executives and stakeholders.
• Best practices for data storytelling: Presenting financial models and analysis in a clear, concise, and engaging manner.
• Hands-on: Prepare a financial analysis report and presentation for a fictional company, highlighting key insights and recommendations.

Materials and Tools:

• Required tools: Microsoft Excel, Python, Power BI
• Sample datasets: Financial statement data, stock price data, company forecasts, and investment analysis data
• Access to Python libraries (pandas, NumPy, matplotlib, etc.)
• Recommended resources: Financial modeling textbooks, Python documentation, and online financial analysis guides