This teaching tool illustrates the nature of financial disclosure and accountability. Students will learn about corporate 10-Q and 10-K filings and the role of third-party auditing.
- Use proprietary WRDS SEC Analytics Suite
- Search over 3.5 million SEC filings
- Understand the role of the auditor
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
Developed in collaboration with award-winning Wharton Professor of Finance Robert Stambaugh, this application enables students to design and test their own investment strategies.
- Set up a backtest to test an investment strategy
- Run tests against a wealth of archived data
- Review and assess the outcome
How do we price a stock option when its present value depends upon the unknown future price of the underlying stock?
- Learn the basics of using geometric Brownian motion as a process for modeling stock price paths
- Run a Monte Carlo simulation to value a stock option
The Black-Scholes Model used for option pricing is considered one of the most important and elegant concepts in modern financial theory. After an introduction to the model, students will:
- Recognize underlying assumptions of the Black-Scholes method
- Practice pricing European call options using a Black-Scholes calculator
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
Introduces non-negative matrix factorization (NMF), a linear algebraic optimization method used for topic modeling.
- Review benefits and challenges of using topic modeling
- Visualize the process using simplified set of “documents”
- Compare two commonly used approaches for topic modeling
In this exercise, students will log into WRDS for the first time, and run a very simple data query. They will use the CRSP Monthly Stock file to retrieve one year of monthly prices for three securities. Assignment instructions for students are found in the accompanying PowerPoint deck.
Requires a CRSP subscription.
Students are re-introduced to WRDS, taught how information is organized, and then shown lists of available datasets in WRDS. Explore how financial data is categorized in WRDS and introduce students to the most commonly used databases.
The objective of this case is to be able to locate specific data on the WRDS platform. Students should be able to differentiate between data vendors and categories, and understand specific vendor products and update frequency. They should also be able to locate documentation and find specific data items.
Students will learn about the different types of identifiers, including header and historical identifiers, issue and company specific identifiers, as well as relevant date ranges for certain identifiers. Additionally, students will gain knowledge of and understand the distinction between universal and proprietary identifiers.
One challenge in finance and accounting research is that not all datasets allow the user to easily track company history. Changing identifiers can be especially problematic when linking data across databases. When tracking companies through time, students should recognize which company identifiers can change and which will not change in the dataset they are using.
When conducting financial research, it is often necessary to match security-level data in the CRSP database with company-level data in the Compustat database. The CRSP/Compustat Merged Database is a CRSP product that contains Compustat data items. The database is structured so that Compustat items can be accessed using CRSP's PERMNO/PERMCO identifiers and Compustat's GVKEY identifiers. After being introduced to CCM, students complete an assignment using CRSP PERMNOs to access Compustat data—specifically, earnings per share.
Requires a subscription to CRSP and Compustat.
Students will be introduced to SAS and SAS Studio and will learn the following upon completion of the exercise:
• Accessing WRDS data through SAS Studio
• Using the table view to filter data
• Write and read a basic data step in SAS Studio
• Export a table in SAS Studio
The objective of this exercise is to introduce students to the basics of connecting to WRDS using PC-SAS for Windows. Students will learn how to sign on to SAS remotely and build a query to download stock price data. After being taught the details of the SAS data step within the query, students will export the newly retrieved data to Excel.
A Jupyter Notebook is an open-source web-based computing environment that enables the combination of live code, equations, explanatory text, and visualizations.
Designed for first-time users, we have created a step-by-step guide within a Notebook to provide a hands-on Jupyter experience. The instructions take you through the process of creating cells, running cells, accessing your WRDS datasets, querying data, and graphing results.
Instructions in this Notebook contain sample Python code. However, Jupyter at WRDS is also available for use with R if that is your preferred language.
Your instructor may have additional guidance regarding the use of this Teaching Tool.
The slide deck begins by explaining how to access WRDS data via the WRDS API. Step-by-step instructions illustrate how to download CRSP Daily Stock – Securities data via the web using Excel’s Power Query Editor and the API. Once you have downloaded your data into an Excel sheet, you can further manipulate the data using standard Excel functionality.
Requires Windows, Excel, and a CRSP subscription.
This tool was designed as a basic introduction to BoardEx, including a description of how the data is structured, as well as an explanation of key identifiers. Two data visualizations are provided to demonstrate different approaches to using the data. The first is an interactive network diagram showing how entities are connected. The second visualization illustrates the difference between CEO salaries and CEO total remuneration.
In a guided exercise, users will perform a search for a company’s board of directors and then download the list of names to Excel. The search results will include BoardEx’s data on the gender breakdown for the company’s board.
The exercise requires a subscription to BoardEx.
This tool was designed as a basic introduction to the ExecuComp data product, including an overview of how the data was affected by new accounting standards in 2006.
In a guided exercise, users will perform a search for total annual CEO compensation. A tutorial on using the query form, including the WRDS conditional statement builder, is provided in the instructions in the slide deck.
Assignment requires a Compustat ExecuComp subscription.
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.
Companies whose stocks trade on U.S. exchanges are required to disclose their financial condition via 10-Q and 10-K filings. To help ensure accuracy and integrity, financial statements are audited by professional firms. This teaching tool introduces students to the concept of financial accountability and guides them through an exercise in detecting an issue related to proper disclosure.
Requires a subscription to the WRDS SEC Analytics Suite.
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.
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
Depending on a course’s pedagogical goals, students have the ability to control different aspects of their backtests, including start and end dates, number of securities per portfolio, investment style, weights and rebalancing frequency.
- Multiple graphs and tables for deciphering results
- Summary recaps the test’s parameters, displaying investment performance
Requires subscriptions to Compustat and CRSP.
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.
The slide deck begins with a basic introduction to derivatives, then focuses on familiarizing students with the terms of a stock option contract.
Designed so students can engage in a real-life trading scenario, the experiential learning assignment asks students to be bullish, and buy a call option. Students look up a current option chain and enter the data into our interactive option tool. The tool generates a payoff diagram and provides feedback.
The slide deck walks students through the mathematical steps of pricing a call option using a risk-neutral valuation approach. The first exercise enables students to visualize the growth of a binomial tree based on stock prices going up and down at each node. The second exercise asks student to use a binomial pricing calculator to determine the price of a long European call option.
The slide deck opens with an introduction to using geometric Brownian motion for simulating stock price paths. Students are asked to use our interactive tool to run a Monte Carlo simulation to value a European-style call option. The tool’s graphical results allow students to easily visualize the wide range of possible outcomes. Students can change the input parameters on the calculator portion of the tool, and rerun the simulation to consider how these changing variables affect the results.
Designed as an intuitive explanation of the Black-Scholes option pricing method, the slide deck breaks down the option pricing formula into its individual components.
In a guided exercise, students price European call options using our interactive Black-Scholes calculator. What happens when one of the input parameters is increased or decreased? Does the calculated option price change commensurately? Students complete the assignment by altering one of the input variables while holding all other variables constant.
Designed to increase financial literacy, the slide deck describes how loan amortization works. The assignment asks students to generate a loan amortization schedule using our interactive application.
By manipulating the input parameters, students learn how a loan repayment schedule is impacted by factors such as the interest rate and the loan’s term. A dynamically changing amortization table highlights these variable adjustments.
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.
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.
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.
WRDS' proprietary SEC research platform is used in such disciplines as corporate due diligence, forensic accounting, disclosure research, and investment management. After completing the slide deck, researchers will have an understanding of the different components of the Suite.
Requires a subscription to the WRDS SEC Analytics Suite.
Whether you are a programming expert or novice, this activity has been designed to teach you how to use regex to perform a text search in SEC Filings. Run the cells in this Notebook to perform an exact phrase search across the WRDS SEC database. Learn how to filter by company and form type. You can also expand your search to multiple companies.
We provide an option for downloading full text files of the search results to your WRDS Notebook directory. After becoming comfortable with this simple search, you may modify the sample code to build more complex searches.
Requires subscription to WRDS SEC Analytics Suite.
Follow the steps in the Jupyter Notebook to perform a sentiment analysis on Twitter’s 10-K Filings. This sample code uses the Loughran-McDonald dictionary as an example of a sentiment lexicon.
After running a simple, dictionary-based sentiment analysis on one company’s filings, you may modify the sample code to add additional companies, or use a customized master dictionary for your analysis.
Please note: Jupyter at WRDS is not yet available for Class Accounts.
Requires a subscription to the WRDS SEC Analytics Suite.
WRDS' proprietary SEC research platform is used in such disciplines as corporate due diligence, forensic accounting, disclosure research, and investment management. After completing the slide deck, students will understand how to search SEC filings by keyword.
Requires a subscription to the WRDS SEC Analytics Suite.
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.
Although sentiment analysis is considered one of the main uses of Natural Language Processing (NLP), it can be one of the hardest text analytics functions to get right. Students well-versed in the challenges of sentiment analysis will make better research decisions, and will be smarter at interpreting sentiment analysis results in general.
The slide deck, focusing on a lexicon-based approach, examines common challenges to sentiment analysis. As a practical application, we introduce students to the VADER model for performing their own sentiment analysis on five movie reviews.
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.
Topic modeling is a process that uses unsupervised machine learning to discover latent, or “hidden” topical patterns present across a collection of text.
This tool begins with a short review of topic modeling and moves on to an overview of a technique for topic modeling: non-negative matrix factorization (NMF). The slide deck provides an intuitive narrative of how NMF works. The goals are for students to have enough understanding of NMF to be able to use it in practice, interpret results, and appreciate some of the challenges that can occur with topic modeling.
In a hands-on activity, students use the NMF application to generate word distributions using a given dataset. They are then asked whether or not they can identify any of the word distributions as coherent topics. Note that this is the same exercise found in Introduction to Topic Modeling. However, that tool uses latent Dirichlet allocation (LDA) as the topic modeling technique instead of NMF. Although each tool stands on its own, they have been designed so that students can compare their topic modeling results using the two different methods.