Econometric Valuation Models for Investment Analysts Training Course

Course overview

Investment analysts are expected to turn incomplete market, company and macroeconomic evidence into defensible valuation views. Yet many models rely on static multiples, untested assumptions or regression outputs that are not translated into investment decisions. This course addresses the practical gap between econometric analysis and valuation work: estimating relationships, checking whether they are credible, and using the results to revise target prices, scenario ranges and investment recommendations.

Participants build valuation models that combine discounted cash flow analysis, relative valuation, factor exposures and econometric forecasting. They learn to prepare market and financial statement data; specify cross-sectional, time-series and panel regressions; interpret coefficients, statistical significance and economic significance; test for multicollinearity, heteroskedasticity, autocorrelation and unstable relationships; and incorporate model results into DCF, residual income and comparable-company valuation frameworks. The programme also covers beta estimation, cost of equity, macroeconomic sensitivity analysis, event-study methods and valuation uncertainty.

Teaching is centred on an integrated listed-company valuation case using Microsoft Excel and market-data extracts. Instructor demonstrations are followed by guided modelling labs, peer review of investment assumptions and analyst-style challenge sessions. Participants leave with a completed Excel valuation workbook containing data checks, regression outputs, forecast drivers, sensitivity tables, target-price scenarios and an investment-committee summary that explains the evidence, limitations and recommendation.

The course is designed for analysts who already work with financial models or investment research and need a more rigorous way to quantify valuation drivers. It is equally relevant to managers seeking more consistent assumptions, clearer model governance and better challenge of analyst recommendations.

Course objectives

By the end of this course, participants will be able to:

• Build an integrated Excel valuation model linking operating forecasts, DCF assumptions and relative valuation outputs
• Estimate cross-sectional regressions to test the relationship between company characteristics and valuation multiples
• Construct time-series models for revenue, inflation, interest-rate and earnings-driver forecasts
• Apply panel-data regression methods to compare valuation drivers across companies, sectors and periods
• Calculate and interpret beta, factor exposures and cost of equity inputs for CAPM-based valuation
• Diagnose multicollinearity, heteroskedasticity, autocorrelation and structural breaks in regression models
• Run an event study to measure abnormal returns around earnings announcements or corporate actions
• Produce an investment-committee valuation pack with target-price scenarios, sensitivity analysis and model limitations

Benefits of attending

Target competencies

Who should attend

• Equity Research Analysts — who need to defend target prices and investment recommendations with tested evidence
• Investment Analysts — who translate company, market and macroeconomic data into valuation decisions
• Buy-Side Analysts — who assess securities, factor risks and expected returns for portfolio decisions
• Corporate Finance Analysts — who prepare valuation cases for acquisitions, capital allocation or strategic reviews
• Credit Analysts — who evaluate how macroeconomic and operating drivers affect issuer value and downside risk
• Investment Managers — who challenge analyst assumptions and require transparent evidence behind valuation views

Requirements and prerequisites

Participants should be comfortable with core financial statements, basic corporate-finance concepts and common valuation terms such as enterprise value, equity value, WACC, free cash flow, beta and trading multiples. They should be able to work with formulas, charts, filters and pivot tables in Microsoft Excel, although advanced Excel programming is not required. Some prior exposure to statistics—means, variance, correlation and linear regression—is helpful but will be refreshed on Day 1. Coding, calculus, prior econometrics coursework and experience with Bloomberg or Refinitiv are not required; guided templates and data extracts are provided.

Training methodology

The programme combines short instructor-led explanations with daily Excel modelling labs based on a listed-company investment case. Participants work from financial statements, price data, macroeconomic series and peer-company observations to build forecasts and test valuation relationships. Demonstrations show each method before participants apply it in guided pairs or small groups. Case discussions focus on whether a statistically valid result is economically useful for an investment view. Each day closes with a model review, and the final session includes an investment-committee presentation and individual workplace application plan.

Course outline

Day 1: Valuation evidence, data and model foundations

• Linking investment theses to measurable valuation drivers
• Enterprise value, equity value and return-based valuation frameworks
• Market, accounting and macroeconomic data definitions
• Cleaning price series, financial statement data and peer observations
• Descriptive statistics, distributions and outlier treatment
• Correlation analysis versus causal economic interpretation
• Excel model architecture, audit checks and assumption registers

Workshop: Participants construct a clean company-and-peer data set and produce an audited valuation-input sheet with documented source assumptions.

Day 2: Regression analysis for relative valuation

• Ordinary least squares regression mechanics and coefficient interpretation
• Cross-sectional models for P/E, EV/EBITDA and price-to-book multiples
• Selecting explanatory variables for growth, profitability, leverage and risk
• Statistical significance, confidence intervals and economic materiality
• Dummy variables for sector, geography and accounting differences
• Multicollinearity diagnosis using correlation matrices and variance inflation factors
• Heteroskedasticity, residual plots and robust interpretation

Workshop: Participants estimate a peer-multiple regression in Excel and derive an implied valuation multiple for the case company.

Day 3: Forecasting cash flows and discount rates

• Time-series patterns, stationarity and trend identification
• Moving averages and exponential smoothing for operating forecasts
• Autoregressive forecasting of revenue, margins and macroeconomic variables
• Forecast-error measures including MAE, RMSE and bias
• CAPM beta estimation from return data
• Interest rates, inflation and country-risk inputs to cost of capital
• Integrating econometric forecasts into DCF and residual income models

Workshop: Participants forecast a key operating driver and update the case company DCF with data-supported cash flow and WACC assumptions.

Day 4: Panel data, factors and market events

• Panel-data structures across firms, sectors and reporting periods
• Fixed-effects and random-effects concepts for valuation analysis
• Factor models for market, size, value and momentum exposures
• Separating systematic risk from company-specific return variation
• Event-study design around earnings releases and corporate announcements
• Abnormal returns, cumulative abnormal returns and event windows
• Structural breaks and regime changes in valuation relationships

Workshop: Participants complete a peer-company event study and assess whether the observed price reaction changes their investment thesis.

Day 5: Decision-ready valuation and model governance

• Reconciling DCF, comparable-company and econometric valuation outputs
• Scenario design for macroeconomic, operational and multiple-risk cases
• Sensitivity tables, valuation ranges and probability-weighted outcomes
• Model validation, back-testing and forecast-versus-actual review
• Data lineage, version control and assumption-change documentation
• Communicating uncertainty without weakening the investment recommendation
• Investment-committee narrative, challenge questions and decision criteria

Workshop: Participants finalise and present an investment-committee valuation pack containing their target-price range, supporting model outputs and recommendation.

Tools & standards covered

Microsoft Excel, Bloomberg Terminal, Refinitiv Workspace, IFRS 13 Fair Value Measurement

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions