About This Specialization - thecads.org · Wharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational

About This SpecializationWharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational courses will introduce you to spreadsheet models, modeling techniques, and common applications for investment analysis, company valuation, forecasting, and more. When you complete the Specialization, you'll be ready to use your own data to describe realities, build scenarios, and predict performance.

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4 weeks of study, 3-5 hours/week

English, Russian

About the CourseHow can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structur-ing your own models. These building blocks will be put to use in the other courses in this Specialization.

Week 1Module 1: Introduction to ModelsIn this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.

Video · 1.1 Course Introduction

Video · 1.2 Definition and Uses of Models, Common Functions

Video · 1.3 How Models Are Used in Practice

Video · 1.4 Key Steps in the Modeling Process

Video · 1.5 A Vocabulary for Modeling

Video · 1.6 Mathematical Functions

Video · 1.7 Summary

Quiz · Module 1: Introduction to Models Quiz

Reading · PDF of Lecture Slidess

Week 2Module 2: Linear Models and OptimizationThis module introduces linear models, the building block for almost all modeling. Through close examination of the common uses together with examples of linear models, you’ll learn how to apply linear models, including cost functions and produc-tion functions to your business. The module also includes a presentation of growth and decay processes in discrete time, growth and decay in continuous time, together with their associated present and future value calculations. Classical optimization techniques are discussed. By the end of this module, you’ll be able to identify and understand the key struc-ture of linear models, and suggest when and how to use them to improve outcomes for your business. You’ll also be able to

Video · 2.1 Introduction to Linear Models and Optimization

Video · 2.2 Growth in Discrete Time

Video · 2.3 Constant Proportionate Growth

Video · 2.4 Present and Future Value

Video · 2.5 Optimization


Quiz · Module 2: Linear Models and Optimization Quiz

Reading · PDF of Lecture Slides

Week 3Module 3: Probabilistic ModelsThis module explains probabilistic models, which are ways of capturing risk in process. You’ll need to use probabilistic models when you don’t know all of your inputs. You’ll examine how probabilistic models incorporate uncertainty, and how that uncertainty continues through to the outputs of the model. You’ll also discover how propagating uncertainty allows you to determine a range of values for forecasting. You’ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilis-tic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps the most important of all of the statistical distributions, the normal distribution, characterized by mean and standard deviation. By the end of this module, you’ll be able to define a probabilistic model, identify and understand the most commonly used probabilistic models, know the components of those models, and determine the most useful probabilistic models for capturing and exploring risk in your own business.

Video · 3.1 Introduction to Probabilistic Models

Video · 3.2 Examples of Probabilistic Models

Video · 3.3 Regression Models

Video · 3.4 Probability Trees

Video · 3.5 Monte Carlo Simulations

Video · 3.6 Markov Chain Models

Video · 3.7 Building Blocks of Probability Models

Video · 3.8 The Bernoulli Distribution

Video · 3.9 The Binomial Distribution

Video · 3.10 The Normal Distribution

Video · 3.11 The Empirical Rule


Quiz · Module 3: Probabilistic Models Quiz


Week 4Module 4: Regression ModelsThis module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You’ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You’ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You’ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you’ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them so that you can discuss your model and convince others that your model makes sense, with the ultimate goal of implementation.

Video · 4.1 Introduction to Regression Model

Video · 4.2 Use of Regression Models

Video · 4.3 Interpretion of Regression Coefficients

Video · 4.4 R-squared and Root Mean Squared Error (RMSE)

Video · 4.5 Fitting Curves to Data

Video · 4.6 Multiple Regression

Video · 4.7 Logistic Regression

Video · 4.8 Summary of Regression Models

Quiz · Module 4: Regression Models Quiz


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English, Portuguese (Brazilian)

About the CourseThe simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have know how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required.

Week 1Spreadsheets: A Tool for Thinking with NumbersThis module was designed to introduce you to the history of spreadsheets, their basic capabilities, and how they can be used to create models. You'll learn the different types of data used in spreadsheets, spreadsheet notations for mathemati-cal operations, common built-in formulas and functions, conditional expressions, relative and absolute references, and how to identify and correct circular references. By the end of this module, you'll understand the context of spreadsheets, be able to navigate a spreadsheet, use built-in formulas and functions in spreadsheets, create your own simple formulas, and identify and correct common errors so you can put spreadsheets to work for you.

Video · 2.0 Module Introduction

Video · 2.1 Using assumptions and decision variables in spreadsheet models

Video · 2.2 Structuring a spreadsheet to model variables, objectives, and objective functions

Video · 2.3 Constructing simple cashflow model

Video · 2.4 What-if analysis and sensitivity analysis

Video · 2.5 Limits to simple, deterministic models

Quiz · Module 2 Quiz: Classic Models

Reading · PDF of Module 2 Lecture Slides

Reading · Module 2 examples

Week 2Addressing Uncertainty and Probability in ModelsThis module was designed to introduce you to how you can use spreadsheets to address uncertainty and probability. You'll learn about random variables, probability distributions, power, exponential, and log functions in model formulas, models for calculating probability trees and decision trees, how to use regression tools to make predictions, as well as multiple regres-sion. By the end of this module, you'll be able to measure correlations between variables using spreadsheet statistical functions, understand the results of functions that calculate correlations, use regression tools to make predictions, and improve forecasts with multiple regression.

Video · 3.0 Introduction

Video · 3.1 Random variables and probability distributions

Video · 3.2 Changes in discrete and continuous time

Video · 3.3 Power, exponential, and log functions

Video · 3.4 Probability trees and decision trees

Video · 3.5 Correlation and Regression

Quiz · Module 3 Quiz: Probability, Correlation, and Regression



Reading · Additional reading on exponential and other functions

Week 3Simulation and OptimizationIn this module, you'll learn to use spreadsheets to implement Monte Carlo simulations as well as linear programs for optimization. You'll examine the purpose of Monte Carlo simulations, how to implement Monte Carlo simulations in spread-sheets, the types of problems you can address with linear programs and how to implement those linear programs in spreadsheets. By the end of this module, you'll be able to model uncertainty and risk in spreadsheets, and use Excel's solver to optimize resources to reach a desired outcome. You'll also be able to identify the similarities and differences between Excel and Sheets, and be prepared for the next course in the Business and Financial Modeling Specialization.

Video · 4.0 Introduction

Video · 4.1 Monte Carlo Simulations

Video · 4.2 Linear Programming

Video · 4.3 Next Steps, and Differences between Excel and Sheets

Quiz · Module 4 Quiz: Simulations, Scenarios, and Optimization



Reading · Links and other resources for further study

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About the CourseUseful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. In this course, you can learn how to create quantitative models to reflect complex realities, and how to include in your model elements of risk and uncertainty. You’ll also learn the methods for creating predictive models for identifying optimal choices; and how those choices change in response to changes in the model’s assumptions. You’ll also learn the basics of the measurement and management of risk. By the end of this course, you’ll be able to build your own models with your own data, so that you can begin making data-informed decisions. You’ll also be prepared for the next course in the Specialization.

Week 1Week 1: Modeling Decisions in Low Uncertainty SettingsThis module is designed to teach you how to analyze settings with low levels of uncertainty, and how to identify the best decisions in these settings. You'll explore the optimization toolkit, learn how to build an algebraic model using an advertising example, convert the algebraic model to a spreadsheet model, work with Solver to discover the best possible decision, and examine an example that introduces a simple representation of risk to the model. By the end of this module, you'll be able to build an optimization model, use Solver to uncover the optimal decision based on your data, and begin to adjust your model to account for simple elements of risk. These skills will give you the power to deal with large models as long as the actual uncertainty in the input values is not too high.

Video · Course Introduction

Video · 1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign

Video · 1.2 Optimizing with Solver, and Alternative Data Inputs

Video · 1.3 Adding Risk: Managing Investments at Epsilon Delta Capital

Reading · PDFs of Slides for Week 1

Reading · Excel Files for Week 1

Quiz · Week 1: Modeling in Low Uncertainty Quiz

Week 2Week 2: Risk and Reward: Modeling High Uncertainty SettingsWhat if uncertainty is the key feature of the setting you are trying to model? In this module, you'll learn how to create models for situations with a large number of variables. You'll examine high uncertainty settings, probability distributions, and risk, common scenarios for multiple random variables, how to incorporate risk reduction, how to calculate and inter-pret correlation values, and how to use scenarios for optimization, including sensitivity analysis and the efficient frontier. By the end of this module, you'll be able to identify and use common models of future uncertainty to build scenarios that help you optimize your business decisions when you have multiple variables and a higher degree of risk.

Video · 2.1 High Uncertainty Settings, Probability Distributions, Uncertainty and Risk

Video · 2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values

Video · 2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier

Reading · PDFs of Lecture Slides for Week 2


Quiz · Week 2: Modeling in High Uncertainty Quiz

Week 3Week 3: Choosing Distributions that Fit Your DataWhen making business decisions, we often look to the past to make predictions for the future. In this module, you'll exam-ine commonly used distributions of random variables to model the future and make predictions. You'll learn how to create meaningful data visualizations in Excel, how to choose the the right distribution for your data, explore the differences between discrete distributions and continuous distributions, and test your choice of model and your hypothesis for good-ness of fit. By the end of this module, you'll be able to represent your data using graphs, choose the best distribution model

Video · 3.1 Data and Visualization: Graphical Representation

Video · 3.2, pt 1: Choosing Among Distributions: Discrete Distributions

Video · 3.2, pt 2: Choosing Among Distributions: Continuous Distributions

Video · 3.3 Hypothesis Testing and Goodness of Fit

Reading · PDFs of Lecture Slides for Week 3


Quiz · Week 3: Choosing Fitting Distributions Quiz

Week 4Week 4: Balancing Risk and Reward Using SimulationThis module is designed to help you use simulations to enabling compare different alternatives when continuous distribu-tions are used to describe uncertainty. Through an in-depth examination of the simulation toolkit, you'll learn how to make decisions in high uncertainty settings where random inputs are described by continuous probability distributions. You'll also learn how to run a simulation model, analyze simulation output, and compare alternative decisions to decide on the most optimal solution. By the end of this module, you'll be able to make decisions and manage risk using simulation, and more broadly, to make successful business decisions in an increasing complex and rapidly evolving business world.

Video · 4.1: Modeling Uncertainty: From Scenarios to Continuous Distributions

Video · 4.2 Connecting Random Inputs and Random Outputs in a Simulation

Video · 4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results

Video · Course Conclusion

Reading · PDFs of Lecture Slides

Reading · Excel files for Week 4

Quiz · Week 4: Using Simulations Quiz

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English

About the CourseThis course is designed to show you how use quantitative models to transform data into better business decisions. You’ll learn both how to use models to facilitate decision-making and also how to structure decision-making for optimum results. Two of Wharton’s most acclaimed professors will show you the step-by-step processes of modeling common business and financial scenarios, so you can significantly improve your ability to structure complex problems and derive useful insights about alternatives. Once you’ve created models of existing realities, possible risks, and alternative scenarios, you can determine the best solution for your business or enterprise, using the decision-making tools and techniques you’ve learned in this course.

Week 1Evaluation Criteria: Net Present ValueThis module was designed to introduce you to the many potential criteria for selecting investment projects, and to explore the most effective of these criteria: Net Present Value (NPV). Through the use of concrete examples, you'll learn the key components of Net Present Value, including the time value of money and the cost of capital, the main utility of NPV, and why it is ultimately more accurate and useful for evaluating projects than other commonly used criteria. By the end of this module, you'll be able to explain why net present value analysis is the appropriate criteria for choosing whether to accept or reject a project, and why other criteria, such as IRR, payback, ROI, etc. may not lead to decisions which maximize value.

Video · Course Introduction & Overview

Video · 1.1 Introduction: Criteria for Evaluating Projects

Video · 1.2 Time Value of Money

Video · 1.3 NPV Analysis of Projects

Video · 1.4 Other Evaluation Techniques

Video · 1.5 The Cost of Capital

Quiz · Evaluation Criteria: Module 1 Quiz


Reading · Excel Spreadsheets: Module 1

Week 2Evaluating ProjectsIn this module, you'll learn how to evaluate a project with emphasis on analyzing the incremental after-tax cash flows associated with the project. You'll work through a concrete example using alternative scenarios to test the effectiveness of this method. You'll also learn why only future cash flows are relevant, why to ignore financial costs, include all incidental effects, remember working capital requirements, consider the effect of taxes, forget sunk costs, remember opportunity costs, use expected cash flows, and perform sensitivity analysis. By the end of this module, you'll be able to evaluate projects more thoroughly and effectively, with emphasis on how to model the change in the company’s after-tax cash flows, so that you can make more profitable decisions.

Video · 2.1 Introduction and Analyzing Incremental After-Tax Cash Flows - Initial Investment Phase

Video · 2.2 Analyzing Incremental After-Tax Flows - Operating Phase

Video · 2.3 Analyzing Incremental After-Tax Flows - Terminal Phase

Video · 2.4 Example: New Production Machine

Video · 2.5 Key Considerations in Evaluations

Quiz · How to Evaluate Projects: Module 2 Quiz


Week 3Expressing Business Strategies in Financial TermsThis module was designed to give you the opportunity to learn how business activities, transactions and events are trans-lated into financial statements, including balance sheets, income statements, and cash flow statements. You'll also learn how these three statements are linked to each other, and how balance sheets and income statements can help forecast the future cash flow statements. By the end of this module, you'll be able to explain how accounting systems translate business activities into financial terms, and how to use this to better forecast future cash flows, so that you can express your business strategies in these financial terms, and show "the bottom line" for your proposed plan of action.

Video · 3.1 Introduction to Financial Statements

Video · 3.2 Balance Sheets and Income Statements

Video · 3.3 Cash Flow Statements

Quiz · Financial Statements and Forecasting: Module 3 Quiz


Week 4New Product ValueIn this module, you'll apply what you’ve been learning to an analysis of a new product venture. You’ll learn how to map out a plan of the business activities, transactions and events that need to happen to implement the new venture, including their timing. You'll also learn how to set up a spreadsheet to help with forecasts, and to re-calculate things automatically as we re-think our plans. You'll see how to forecast out the implied financial statements, and calculate the Net Present Value (NPV). By the end of this module, you'll be able to use spreadsheets to explore different risks a venture may face, and analyze the implications of these scenarios for NPV, so that you can make the most profitable, data-driven decision possi-ble.

Video · 4.1 Introduction and Speadsheet Setup

Video · 4.2 Forecasting Future Cash Flows

Video · 4.3 NPV and IRR Calculations

Video · 4.4 Formulation and Evaluation of Alternative Scenarios

Video · 4.5 Expanding Beyond the Time Horizon

Video · Course Conclusion

Quiz · Calculating Value: Module 4 Quiz


Reading · Excel Spreadsheets: Module 4

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About the CourseIn this Capstone you will recommend a business strategy based on a data model you’ve constructed. Using a data set designed by Wharton Research Data Services (WRDS), you will implement quantitative models in spreadsheets to identify the best opportunities for success and minimizing risk. Using your newly acquired decision-making skills, you will structure a decision and present this course of action in a professional quality PowerPoint presentation which includes both data and data analysis from your quantitative models.

Wharton Research Data Services (WRDS) is the leading data research platform and business intelligence tool for over 30,000 corporate, academic, government and nonprofit clients in 33 countries. WRDS provides the user with one location to access over 200 terabytes of data across multiple disciplines including Accounting, Banking, Economics, ESG, Finance, Insurance, Marketing, and Statistics.

Week 1Getting StartedWelcome! This opening module was designed to give you an overview of the Business and Financial Modeling Capstone, in which you will be working with historical financial data to calculate individual returns and summary statistics on those returns. The project has multiple steps, which are outlined below in the "Project Prompt", and culminates in a recommenda-tion for portfolio allocation that you will prepare a presentation on. You will draw on elements from all courses to complete this project, and you can use your final presentation as a work sample to improve your current job or even find a new one. Before moving on, complete the "Project Scope Quiz." The work you do this week enables you to understand the steps needed to successfully complete your final project.

Reading · Project Description - Read me first!

Reading · Project Prompt

Reading · Historical Stock Data

Quiz · Project Scope Quiz

Other · Module 1 Discussion: Introductions

Other · Questions about the Project

Week 2Steps 1 and 2: Yahoo FinanceIn this module, which correlates to Steps 1 and 2 in the Project Prompt, you'll be working with a historical data set to calculate performance data and to provide summary statistics on that data. These calculations will allow you to practice using Spreadsheets for financial calculations, and provides the foundational skills and numbers for the next steps of the project. First, you'll use the set to calculate daily returns on a set of securities. You'll then use your Spreadsheet skills to calculate summary statistics. You'll be given the opportunity to test your knowledge with a sample return to see if your calculations are correct. And you may want to refresh your recollection of the content from the Specialization with the lectures included here. The work you complete this week allows you to form the basis for comparing stock performance,

Reading · More on Close Price versus Adjusted Close Price

Reading · More on the Sharpe Ratio

Reading · Sample Returns Spreadsheet (AAPL)

Quiz · Daily Returns Quiz

Quiz · Summary Statistics Quiz

Other · Questions about Calculating Performance and Summary Statistics

Video · Definition and Uses of Models, Common Functions (Fundamentals of Quantitative Modeling)

Video · How Models are Used in Practice (Fundamentals of Quantitative Modeling)

Video · Mathematical Functions (Fundamentals of Quantitative Modeling)

Video · Navigating a Spreadsheet and Crafting Formulas (Introduction to Spreadsheets)

Video · How To Build an Optimization Model: Hudson Readers Ad Campaign (Modeling Risk and Realities)

Video · Data and Visualization: Graphical Representation (Modeling Risk and Realities)

Reading · PDFs of Refresher Video Slides

Week 3Step 3: Creating an optimal risky portfolio on the efficient frontierIn this module, you'll go beyond calculating simple returns to tackle the more advanced task of finding the minimum variance and "optimal risk portfolio" weights for a portfolio of selected securities (note, the "optimal risky portfolio" is also known as an "optimal portfolio" or "tangent portfolio"). You'll follow the tasks in Step 3 in the Project Prompt and use the resources below to calculate the portfolio weights for two securities that results in the portfolio with the minimum variance; then, you'll calculate the "optimal risky portfolio" on the efficient frontier for these same two securities, then for all 10 stocks in the pool. You'll be quizzed on your calculations and other insights that emerge from this exercise. The work you complete this week gives you practice in creating an optimal risky portfolio, which is a key component of your final project. Note: There are a number of resources available on the internet providing step-by-step instructions on how to use Excel to create an "optimal risky portfolio" on the efficient frontier given a certain set of available assets. We encourage you to attempt to use the skills you gained during the Specialization to work through these steps independently; you are, however, permitted to utilize third-party resources if you find it necessary. We've included some lectures from the underly-ing Specialization courses concerning Solver, optimization, and other relevant topics.

Reading · Videos Explaining the Efficient Frontier andOptimal Risky Portfolio

Other · Third party resources for calculations

Reading · More on Portfolio Variance

Reading · More on the Efficient Frontier

Reading · More on Short Selling

Quiz · The Minimum Variance and Optimal Risky Portfolio

Other · Questions about minimum variance and "optimal risky portfolio" weights

Video · Introducton to Linear Models and Optimization (Fundamentals of Quantitative Modeling)

Video · Present and Future Value (Fundamentals ofQuantitative Modeling)

Video · Optimization (Fundamentals of Quantitative Modeling)

Video · Linear Programming (incl. Solver) (Introduction to Spreadsheets)

Video · Optimizing with Solver, and Alternative Data Inputs (Modeling Risk and Realities)

Video · Adding Risk: Managing Investments at Epsilon Delta Capital (Modeling Risk and Realities)

Video · Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier (Modeling Risk and Realities)


Week 4 Step 4: Optional exercise using CAPM tablesThe Capital Asset Pricing Model, or CAPM, is another tool used by investors to weigh the risks and rewards of potential investments. In this optional module covering Step 4 in the Project Prompt, you can use CAPM as a vehicle to further strengthen your financial modeling skills, including using regression concepts. You may revisit the Specialization lectures below touching on regression. To test whether you've grasped the concepts in the CAPM model, this module includes a short quiz. This assessment is formative, meaning your score will not count towards your final grade. The work you do this week may inform how you build the mixed asset portfolio of your final project, but it is not necessary to complete the final project.

Reading · Video on the Capital Asset Pricing Model (CAPM)

Reading · More on the Capital Asset Pricing Model

Practice Quiz · Capital Asset Pricing Model

Other · Questions, comments, and helpful resources for CAPM

Video · Introduction to Regression (Fundamentals of Quantitative Modeling)

Video · Use of Regression Models (Fundamentals of Quantitative Modeling)

Video · Correlation and Regression (Introduction to Spreadsheets)


Week 5 Step 5: Creating Your Asset Allocation & Final PresentationIn this final module you are asked to move beyond a stock-only portfolio to one utilizing more diversified assets and to prepare a short presentation summarizing your findings. As explained in Step 5 of the Project Prompt, you have $5 million to invest in the Vanguard Total Bond Market Index Fund (ticker: VBTLX) and Vanguard 500 Index (ticker: VFIAX) investment vehicles. There are two assessments in this module. First, you'll complete a short quiz on the characteristics of your optimal risky portfolio. Then, in the peer review component of this Capstone, you are tasked with preparing a short presentation that (i) explores how your portfolio of mixed asset class of funds compares to a single security (AAPL) and (ii) uses that comparison to discuss the importance of portfolio diversification.

Reading · VBTLX and VFIAX Monthly Returns

Quiz · Working with a Diversified Portfolio

Other · Module 5 Discussion - Reflect on your experience and share your insights

Peer Review · Portfolio Performance Presentation

Other · How did you create a Mixed Asset Portfolio?

Other · Arguments for and against mixed asset portfolios

Documents

About This Specialization - thecads.org · Wharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational