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The financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.

The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.

EuroPet S.A. was a multinational company operating gas stations in many European countries. There was a growing propensity for supermarkets to attach gas stations to their retail operations, which was developing into a major threat to EuroPet. As a result, in the mid-1990s, the company began to develop and brand its own convenience stores co-located with its gas stations. However, the company was spending much more on advertising the convenience stores than its competitors did. Management now had to decide if the increase in sales attributed to advertising efforts justified the advertising spend by analyzing the market data from one large metropolitan area: Marseille, France.

Students will learn: how to use cross-tabs and other marketing research tools to identify segmentation descriptors; how to analyze data and interpret results; and how these research results could guide new product development and positioning strategies in order to effectively target relevant customer segments.

thyssenkrupp Steel Europe, a major European steel company, operates a so-called push-pickling line (PPL) in Bochum, Germany. The PPL produces a particular type of steel strips that are sold to B2B customers, mainly in the automotive industry. In spring 2014, a senior vice president of thyssenkrupp Steel's production operations and one of his production managers notice that over the span of ten years the production facility regularly did not meet its planned production volumes. They set out to determine the drivers for the deviations from planned production figures with the ultimate goal to improve the production planning process at the Bochum PPL. Students will step into the shoes of Markus Schulze a production manager at thyssenkrupp Steel as he searches for performance drivers at the Bochum PPL and analyzes recent production data to build a forecasting model for production planning.

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.

Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

The decision maker is in charge of procurement auctions at the department of transportation of Orangia (a fictitious U.S. state). Students are asked to assist him in estimating the winning bids in various auctions concerning highway repair jobs using data on past auctions. The decision maker is faced with various professional, statistical, and ethical dilemmas.

To analyze highway procurement auctions from the buyer-auctioneer perspective, establish basic facts regarding the project price-to-estimated cost ratio, set up and estimate a structural regression model to predict the winning bid, and compute the probability the winning price will be below estimated cost. Difficulties include heteroskedasticity, logarithmic specification, and omitted variable bias. Also to estimate a Logit regression and predict bidder collusion probability.

Supplements the (A) case.

An asset management company must replace the manager of its two signature mutual funds, who is about to retire. Two candidates have been short-listed. The management team is divided and cannot decide which of the two candidates would make the better mutual fund manager. The retiring manager presents a linear regression model to examine success factors of mutual fund managers. This linear regression is the starting point for the subsequent analysis.

Application of linear regression analysis to analyze the performance of mutual fund managers.

Agriculture knowledge, market intelligence, emerging business model.

It is best suited to teach undergraduates and graduates in the areas of rural marketing, agri-business management, service management and information and communication technology for development.

India is changing with great pace by inclusive growth on espousal of technology into the mainstream. Indian farmers are wholly depending even now on traditional methods for decision making on entire agriculture supply chain. The constant decision making provides middle men with a chance to exploit and empower themselves on the returns produced by farmers. Technology is creating waves providing an opportunity for farmers to benefit by adopting information and technology to solve their basic livelihood problems. The Thomson Reuter group launched a SMS-based mobile information service to support India's 250-million-strong agricultural community. The service, named Reuters Market Light (RML), is trying to provide a missing link by providing required information in the quickest possible time to farmers; user need-based services are critical to this. How far RML services are delivering in this context is quizzed by some analysts. Thomson Reuter's service started with the global climb down in commodity prices, coupled with increased risk of natural disasters as per experts. The competitors providing similar services at price which differ with RML wondered about the success, scalability and sustainability of its venture.

This is a practical view of how these interventions can be better looked at and can get into policy for a framework for rural areas' socio-economic development.

Teaching notes.

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.