Chapter 4 - Forecasting ... Case Study #2 Case Study 2: Forecasting Box Office Returns For years, people in the motion picture industry-critics, film historians, and others - have eagerly awaited the second issue in January of Variety. Long considered the show business bible, Variety is a weekly trade newspaper that reports on all aspects of the entertainment industry; movies, television, recordings, concert tours, and so on. The second issue in January, called the Anniversary Edition, summarizes how the entertainment industry fared in the previous year, both artistically and commercially. In this issue, Variety publishes its list of All Time Film Rental Champs. This list indicates, in descending order, motion pictures and the amount of money they returned to the studio. Because a movie theater rents a film from a studio for a limited time, the money paid for admission by ticket buyers is split between the studio and theater owner. For example, if a ticket buyer pays $12 to see a particular movie, the theater owner keeps about $6 and the studio receives the other $6. The longer a movie plays in a theater, the greater the percentage of the admission price returned to the studio. A film playing for an entire summer could eventually return as much as 90% of the $12 to the studio. The theater owner also benefits from such a success because although the owner's percentage of the admission price is small, the sales of concessions (candy, soda and so on) provide greater profits. Thus, both the studio and the theater owner win when a film continues to draw audiences for a long time. Variety lists the rental figures (the actual dollar amounts returned to the studios) that the films have accrued in their domestic releases (United States and Canada). In addition, Variety provides a monthly Box-Office Barometer of the film industry, which is a profile of the month's domestic box-office returns. This profile is not measured in dollars but scaled according to some standard. By the late 1980's, for example, the scale was based on numbers around 100, with 100 representing the average box-office return of 1990. The figures from 1997 to 2006 are given in the table below and in the file BoxOffice.xlsx in blackboard. All the figures are scaled around the 1990's box-office returns, but instead of dollars, artificial numbers are used. Film executives can get a relative indication of the box-office figures compared to the arbitrary 1990 scale. For example, in January 1997 the box-office returns to the film industry were 95% of the average that year, whereas in January 1998 the returns were 104% of the average of 1990 (or they were 4% above the average of 1990's figure). 1997 1998 2004 2005 MONTH JAN 1999 88 2002 111 104 2001 125 118 2006 145 101 2000 132 109 101 2003 127 129 147 119 147 100 96 110 123 146 149 99 82 129 121 121 132 133 148 FEB MAR APR MAY 88 84 113 111 139 108 164 135 124 148 140 141 148 89 85 115 141 148 108 124 201 149 119 156 154 168 191 140 179 145 140 114 169 131 139 120 201 184 109 134 159 178 101 109 137 JUN JUL AUG SEP OCT NOV DEC 156 106 155 129 117 166 121 152 138 137 138 144 136 105 132 120 129 149 119 166 151 166 170 111 115 138 102 78 111 159 175 118 175 101 112 116 128 123 164 152 173 139 148 195 188 194 From the time series given in the above table, you will make a forecast for the 12 months of the next year, 2007 Managerial Report is due on ... Wednesday, 17 Feb 21 (40 pts) . 1. Produce a time series plot of the data, From this graph, do you see a pattern? Can you see any seasonality in the data? You may have to perform time series plots by years. 2. Use exponential smoothing to fit the data. Select an appropriate constant a based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for each month in 2007. Remember, for smoothing methods, you can only provide the forecast for the next period i. If you use ALL 10 years of data, that means you are providing a forecast for only Jan 2007. . ii. I still need the forecast for the other 11 months. And you cannot do a forecast using these forecasts. You will have to figure out how to use all 10 years of data to determine 12 individual, monthly forecasts. 3. Use regression to build a linear trend model. Comment on the goodness-of-fit of this model to the data (or how well does R2 explain the variance in the data?). Plot the predictions from this model on the graph with the original data. 4. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for the next 12 months of 2007 using this technique. 5. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 2007? Use MAPE (mean absolute percent error) as your criterion to justify your decision. Enrichment (5 pts): Use Optimization (and Solver in Excel) to find the optimal smoothing constant in problem 2 above (by minimizing the Mean Squared Error or MSE)