Using this introductory aggregate demand and aggregate supply model, students learn how macroeconomic events impact economic output and price levels.
Developed in collaboration with award-winning Wharton Professor of Finance Robert Stambaugh, this application enables students to design and test their own investment strategies.
Students learn the balance of assets and liabilities/equity. On-screen output includes such items as:
WRDS' proprietary Beta Visualization application teaches students about beta, R
, and how the two metrics represent stock risk.
Determine the fair price of a bond using the following parameters:
Students will learn to calculate cost of equity and evaluate the relationship between risk and expected return.
Students will learn important concepts in diversification and portfolio optimization using a rich, interactive application.
Using MATLAB, students will learn about volatility clustering in stock prices and test the predictive power of the GARCH model.
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
Students become acquainted with a broad range of financial ratios using this sophisticated and intuitive visualization tool.
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.
Using this multiplier calculation tool, students learn how fiscal policy – both government spending and tax policies – can impact aggregate demand.
What are futures contracts and how do they work? Learn the mechanics of futures trading, including how profits and losses are calculated.
Understand the structure of a company's profit and loss accounting using the income statement. Students will become acquainted with the following income statement components:
Measure and compare the profitability of different investments using a capital budgeting analysis.
Review the complete financial statements of a company for multiple years.
Students learn some basics of sentiment analysis, including:
This teaching tool presents an overview of natural language processing (NLP), concentrating on techniques used to prepare text data for analysis, including:
Students will learn how the balance sheet, income statement, and cash flow statement are interrelated.
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:
Use the Beta Visualization application to learn about alpha and beta, and how they relate to firm-specific and market risk. Gain exposure to:
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:
Duplicate market returns or try to exceed them? Students evaluate the impact of management styles on mutual funds.
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:
Using a series of visualizations, students will see the progressive impact of diversification on portfolio return and volatility.
Looking at descriptive statistics of stock returns, students evaluate the impact of diversification.
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.
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.
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.
Students learn the components of a company's Statement of Cash Flows. Includes data such as:
Students learn the methods that financial managers use to increase ROE.
Students learn basic time value of money concepts and visualize growth of investments using an interactive compound interest graph.
Use the interactive computational tool to familiarize students with the variables found in basic time value of money problems. Students learn techniques of:
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.
Learn about clustering as an unsupervised machine learning task and become familiar with how the k-means algorithm works for text classification.
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