How does diversification work? Developed in conjunction with Wharton Professor, Donald Keim, this application introduces the mechanics of diversification in an engaging format using three interactive tools:
- Variance-covariance matrix
- Two-asset opportunity set
- Limits of diversification graph
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
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
What happens when the cash flows from an investment cannot be reinvested at the original rate of return? Callable bonds and fluctuating interest rates, for example, both present potential reinvestment risk.
- Compare yield to maturity with realized compound yield
- Investigate exposure to reinvestment risk
How do changing interest rates affect bond investment decisions? After a brief introduction to the concept of term structure, students learn:
- The relationship between spot rates, implied forward rates, and yield to maturity
- How to determine the implied forward rate given a series of spot rates
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
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:
- Machine learning
- Neural network
Learn about clustering as an unsupervised machine learning task and become familiar with how the k-means algorithm works for text classification.
- Customize input parameters, such as number of clusters
- Compare k-means cluster results with human-annotated results
Learn about topic modeling in an engaging, hands-on activity. We present an introduction to a method of topic modeling known as latent Dirichlet allocation (LDA).
- Recognize benefits and challenges of using topic modeling
- Visualize the process using simplified set of “documents”
- Students may upload their own datasets
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.
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.
Two important and related measures that arise from the Capital Asset Pricing Model are beta and R2. In this teaching tool, students conduct an exercise using the Beta Visualization application to examine, compare, and interpret these important measures of equity risk.
Watch the video for a guided tour.
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 R2 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 executing WRDS 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.
Requires subscription to CRSP.
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 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.
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 use the tool to access 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.
Using the interactive platform, learn how risk and return characteristics change as stocks are added and removed from a hypothetical portfolio. Students will be introduced to such concepts as the efficient frontier, capital market line, and indifference curve.
Learn important concepts in diversification and portfolio optimization using a rich, interactive application.
- Visualize dynamic portfolio evaluation
- Adjustable parameters
- Stocks from the Dow 30
Students will learn how to identify an event and create the necessary input file. They will then configure the input parameters and execute the query. Finally, students will be tasked with analyzing and interpreting the event study output.
Requires a subscription to the WRDS Event Study Suite.
Students are introduced to important futures trading concepts, such as contract size, tick size, contract value, and margin requirements. In the assignment, students use the interactive tool to calculate their potential profit or loss on a futures trade after specifying the necessary parameters.
This introduction to bond valuation begins by describing the features of fixed rate coupon bonds, and provides step-by-step instructions for computing the fair price of a bond. Students complete an assignment using an interactive tool that enables them to change key input variables and examine their effect upon the bond’s price. The tool also introduces the concept of Macaulay duration.
When a significant portion of a bond investor’s revenue is dependent upon reinvesting coupon payments, it becomes essential to understand and assess reinvestment risk. Students explore the difference between yield to maturity and realized compound yield in this introduction to reinvestment risk.
Students complete an exercise designed to encourage them to consider the impact of changing interest rates on bond investment decisions. Given the option between buying a 1-year zero-coupon bond and rolling it over, or buying and holding a 2-year, zero-coupon bond, students must consider the factors that lead to one decision over the other.
In this introduction to Keynesian macroeconomic dynamics, students use the IS-LM model to recognize how macroeconomic events impact GDP and real interest rate levels. The interactive assignment allows students to visualize how different factors—including consumer spending, income tax rate, money supply―shift the position of the IS and LM curves.
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 are 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.
The k-means clustering algorithm is one of the most popular methods used today in unsupervised machine learning. Students are taken through a step-by-step explanation of how the k-means clustering algorithm works. Then, using an interactive tool, students investigate how well the k-means clustering algorithm performs in a text classification task using business articles.
Students will learn enough about topic modeling from this tool to be able to ask the right questions when considering this approach―or when encountering the results of this approach―in either research or practical applications. In the slide deck, a narrative outline of how LDA works is provided for students who are not versed in advanced mathematics. It is designed so that they can more intuitively understand this probabilistic technique, including what it assumes, and what its limitations are. Students finish the assignment by completing two activities using an interactive LDA topic modeling application.