Question

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 $8 to see a particular movie, the theater owner keeps about $4 and the studio receives the other $4. 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 $8 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 measure 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 1980. The figures from 1987 and 1996 are given in the table below and in the file BoxOffice.xlsx in blackboard.

All the figures are scaled around the 1980’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 1980 scale. For example, in January 1987 the box-office returns to the film industry were 95% of the average that year, whereas in January 1988 the returns were 104% of the average of 1980 (or, they were 4% above the average of 1980’s figure).

Month	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996
Jan	95	104	101	88	132	125	111	127	119	147
Feb	94	100	96	110	109	118	123	129	147	146
Mar	98	99	82	129	101	121	121	132	164	133
Apr	96	88	84	113	111	140	139	108	135	148
May	95	89	85	114	140	141	119	115	124	141
Jun	115	108	124	169	179	201	156	149	168	191
Jul	107	109	134	131	145	152	154	155	159	178
Aug	104	101	109	139	140	138	136	129	137	156
Sep	96	106	121	120	120	137	105	117	149	119
Oct	112	102	111	115	129	138	132	166	159	138
Nov	98	78	101	116	118	144	123	152	175	175
Dec	102	111	112	128	139	148	164	173	195	188

From the time series given in the above table, you will make a forecast for the 12 months of the next year, 1997.

Managerial Report is due on … Thursday, 19 Sept (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?
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 1997.
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 1997 using this technique.
5. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 1997? 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).

Answer 1

solution:

(1)

The time series plot is shown below:-

From the plot,is is clear that the data has a)Seasonality and b) Trend

(2)

Exponential smoothing with =0.1612(obtained by minimizing MSE using solver)

CALCULATIONS:-

(3)

The R² valve is poor(only 52%).Therefore, only 52% of the data points are explained by the model.

(4)

Here using the deseasonalized data, the R²  valve slightly improve to 65%

(5)

Summary of forecasts from all the methods

As per this analysis, therefore, the last method i.e the multiplicative model is more appropriate.