By Topic:
Students become acquainted with a broad range of financial ratios using this sophisticated and intuitive visualization tool.
- Over 70 different ratios organized by category
- Compare to industry averages
- Fully interactive visualizations
Type: Interactive App
Duration: Less Than 1 Hour
How to do managers and investors determine a company's liquidity? The Firm Liquidity Ratios tool teaches students about different ratios commonly used to determine a company's ability to meet its financial obligations.
- Visually engaging format
- Firm and industry-level comparisons
Type: Interactive App
Duration: Less Than 1 Hour
Students learn the methods that financial managers use to increase ROE.
- Understand how managers deliver financial value to shareholders
- Learn about the return on equity (ROE) performance metric
- How Profit Margin, Asset Turnover, and Financial Leverage affect ROE
Type: Data Query
Duration: Less Than 1 Hour
Using a series of visualizations, students will see the progressive
impact of diversification on portfolio return and volatility.
- Understand the effects of diversification
- Generate and interpret graphs of the efficient frontier
- Understand concepts of returns, standard deviations and correlations
Type: Data Query
Duration: Less Than 1 Hour
WRDS' proprietary Beta Visualization application teaches students about beta, R^{2}, and how the two metrics represent stock risk.
- Select stocks individually or by industry
- Compare to industry averages
- Fully interactive and integrated charting of risk measures
Type: Interactive App
Duration: Less Than 1 Hour
Use the Beta Visualization application to learn about alpha and beta,
and how they relate to firm-specific and market risk. Gain exposure to:
- Alpha, beta, and measures of risk
- R^{2} and beta reliability
- Sophisticated visualization of metrics
Type: Interactive App
Duration: Less Than 1 Hour
How does diversification work? Developed in conjunction with Wharton Professor, Donald Keim, this application introduces students to to the mechanics of diversification in an engaging format using three interactive tools:
- Variance-covariance matrix
- Two-asset opportunity set
- Limits of diversification graph
Type: Interactive App
Duration: Less Than 1 Hour
In this teaching tool, students are introduced to the Fama-French three-factor model. Extending the CAPM, Fama-French adds both size and value factors in this sophisticated asset pricing model.
• Fama-French factors explained
• Step-by-step multiple regression
• Practical Excel skills
Type: Data Query
Duration: Less Than 1 Hour
Using MATLAB, students will learn about volatility clustering in stock prices and test the predictive power of the GARCH model.
- Identify volatility clustering
- Apply econometric methods (GARCH)
- Interpret statistical output and test model fit
Type: Data Query
Duration: 1 to 2 Hours
Students learn basic time value of money concepts and visualize growth of investments using an interactive compound interest graph.
- Change key parameters such as interest rate and compounding frequency
- Plot results on an interactive graph
Type: Interactive App
Duration: Less Than 1 Hour
How do we compare economies across countries? Students are introduced to the concept of purchasing power parity (PPP) and will complete a graphing activity using Penn World Table (7.1) data.
- GDP per capita for 189 countries
- 60 years of data
Type: Interactive App
Duration: Less Than 1 Hour
Using this multiplier calculation tool, students learn how fiscal policy – both government spending and tax policies – can impact aggregate demand.
- Visual representation of multiple rounds of spending
- Allows users to input custom values for initial demand and MPC
Type: Interactive App
Duration: Less Than 1 Hour
Students learn what the U.S. Treasury curve is, how it is plotted, and how it can be viewed as an indicator of economic health.
- 3-D model of U.S. Treasury yield curve, 1975-2015
- Introduction to federal funds rate as a tool of monetary policy
Type: Interactive App
Duration: Less Than 1 Hour
Many sentiment analysis tools rely on sentiment lexicons—lists of words scored for positive and negative connotations. Students learn different approaches to creating these lexicons.
- Demonstrates six sample sentiment lexicons
- Introduces domain-specific sentiment analyses of financial statements
Type: Interactive App
Duration: Less Than 1 Hour
Named-entity recognition (NER)—the process of finding and classifying named entities such as people, locations, and organizations in text—is central to many other natural language processing tasks. Students learn basic concepts and evaluate three approaches to NER:
- Rule-based
- Machine learning
- Neural network
Type: Interactive App
Duration: Less Than 1 Hour
In this supervised machine learning assignment, students are asked to adjust the training/testing parameters on a text classification tool and review results. Students will learn:
- Basic theory underlying machine learning
- Naive Bayes text classification
Type: Interactive App
Duration: Less Than 1 Hour
Students will become conversant in executing web queries to download financial data. They will also validate important components of the balance sheet, income statement, and statement of cash flows for a company of their choosing.
Students will become conversant in executing web queries to download financial data. They will also become familiar with the basic components of a company's income statement.
Students will become conversant in executing Classroom by WRDS web queries to download financial data. They will also validate the balance of assets and liabilities/equity in a company's balance sheet.
Students will become conversant in executing web queries to download financial data. They will also become familiar with key components of a company's statement of cash flows.
Publicly traded companies disclose their financial information in the form of a balance sheet, income statement, and statement of cash flows. How exactly are these financial statements related to each other? This teaching tool guides students through a basic exercise with a real company to discover just how the statements are interrelated.
Using the interactive application, students will learn how to generate and compare financial ratios to one another and to baseline industry measures. They will be introduced to ratios in the following categories: valuation, profitability, capitalization, financial soundness, solvency, liquidity, and efficiency. Students can select individual companies or entire industries from the S&P 500 universe.
Students will learn about four financial ratios used to determine liquidity: (1) the cash conversion cycle; (2) cash ratio; (3) current ratio; and (4) quick ratio (acid-test). While comparing ratios between companies from different industry sectors, students consider how different factors influence the analysis. The tool makes visual comparisons easy, as students use detailed graphs to consider ratios in the context of relevant industry sectors.
This exercise will acquaint students with the different methods that companies use to deliver financial performance to their shareholders. One of the most popular metrics for performance is ROE, which is a measure of how much the company earns per share invested.
Students will understand how diversification works and the necessary steps required for achieving a diversified portfolio. They will learn concepts such as return, variance, standard deviation, and correlation. Students will also develop knowledge of the portfolio efficient frontier. Finally, they will develop skills in handling data, writing basic functions, and producing graphs in Excel.
Two important and related measures that arise from the Capital Asset Pricing Model are beta and R^{2}. In this teaching tool, students conduct an exercise using the Beta Visualization application to examine, compare, and interpret these important measures of equity risk.
This teaching tool extends the Beta Visualization tool by focusing on alpha as defined in the Capital Asset Pricing Model. Students will learn how to interpret alpha and beta in the context of firm-specific and market risk. They will also become acquainted with R^{2} and the importance of model fit. The assignment guides students through the process of locating stocks with high/low alphas, and then interpreting the results.
Students will learn how to use returns and a measure of risk such as standard deviation to execute a stock-selection strategy. They will become conversant in building and executing web queries, and validating the output. Excel skills are also developed, as students are challenged to manage their data from a raw format to a more structured and presentable one.
This teaching tool includes three different activities designed to teach students about asset variance, covariance and the significance of positive and negative correlation. For example, students visualize a stock portfolio's total risk as they increase the number of stocks.
Students will gain a working knowledge and understanding of the Capital Asset Pricing Model (CAPM). They will also develop intermediate Excel skills by building a regression model in the software. Students can select to use either stock or Exchange-Traded Fund (ETF) returns. Both slide decks provide step-by-step instructions for running the regression using Excel.
Students learn how to perform a multiple linear regression using exchange-traded fund (ETF) returns and the Fama-French market, size, and value factors. Detailed instructions are provided to guide students through the process in Excel. Examining the data, students analyze how well fund excess returns are explained by the Fama-French factors.
In this exercise, students will learn to do the following:
- Identify volatility clustering using appropriate measures and graphs.
- Estimate the econometric model using MATLAB.
- Interpret model coefficients and graphs.
- Run statistical tests on the return series to investigate how well the model fits the data.
The objective of this exercise is to understand mutual fund turnover and its tax implications by conducting an Excel-based assignment.
Using the interactive platform, students will learn how risk and return characteristics change as stocks are added and removed from a hypothetical portfolio. They will be introduced to such concepts as the efficient frontier, capital market line, and indifference curve.
Learn how the internal rate of return (IRR) is defined and calculated, how to use it in comparison with cost of capital, and how to understand its shortcomings when comparing among different investment decisions. Only basic knowledge of corporate finance and budgeting tools is required. Students will also gain experience using Excel.
Students will understand the distinction between simple and compound interest, as well as how time, rates, and compounding frequency affect the future value of an investment.
Students will understand the relationship between present value and future value and learn to compare cash flows at different points in time.
Given a list of possible scenarios, students learn how each macroeconomic event shifts the aggregate-demand and aggregate-supply curves. Students are also asked to consider the effects of the changing equilibrium.
Students will learn the theory of purchasing power parity (PPP) and why economists use PPP GDP to compare GDP across countries. After completion of an assignment, they will also become familiar with some of the macroeconomic data available in the Penn World Table.
The Multiplier Effect tool depicts how the government-induced chain of spending accumulates and results in an amplified change in GDP. Students will learn how the multiplier is calculated and how it works over time. They will also understand how the marginal propensity to consume (MPC) relates to the multiplier, and how the government spending multiplier differs from the tax multiplier.
How is the U.S. Treasury yield curve used as a benchmarking and forecasting tool? Through the course of this assignment, students will learn to recognize different yield curve shapes and how these shapes may be viewed as indicators of macroeconomic conditions.
Designed to introduce basic concepts of text analysis, this teaching tool generates a word cloud as a visualization of word frequency distribution. Students learn about commonly used methods for preparing digital text for analysis by selecting different options on the interactive tool and seeing what happens to the word cloud as different text processing methods are applied.
How does sentiment analysis work? What criteria is used to determine text polarity? In an engaging exercise using song lyrics, students participate in a demonstration of how text can be classified as positive, negative or neutral.
While there are many advanced approaches to sentiment analysis, a basic understanding of the creation and use of sentiment lexicons is important foundational knowledge in the field. Students are asked to use an interactive application to investigate how selecting different sentiment lexicons changes the sentiment analysis of the sample text.
In this teaching tool, students learn what NER is, as well as some of its applications. Students will be introduced to some of the features that NER systems use in the decision making process, such as wordshape, part-of-speech (POS) tagging, and the use of neighboring words. Students will also be asked to consider some of the challenges faced in the NER process.
An overview of supervised machine learning is presented, followed by a step-by-step explanation of how a naïve Bayes classifier works for text classification. Topics covered include some of the parameters used for evaluating classifiers, as well as the tf-idf weighting strategy commonly used in text analysis.