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The Purpose Of This Project Is To Construct A Model To Attempt To Forecast The Interim Earnings Of Harley Davidson.

2386 words - 10 pages

1. SYNOPSISThe purpose of this project is to construct a model to attempt to forecast the interim earnings of Harley Davidson. Harley Davidson, Inc. manufactures premium motorcycles, recreational vehicles, specialized commercial vehicles and parts and accessories. The Company's Motorcycle Division manufactures and markets heavyweight touring and custom motorcycles and a broad range of related products including riding apparel, motorcycle accessories and replacement parts. My purpose is to construct a model using six years worth of historical data and try to predict the earnings for the following year. I will use the interim earnings for the years 1989-1994, and divide them into 24 equal quarters. The model uses a quantative forecasting method called the Multiplicative Time Series Model. It is based on the assumption that there are four factors that have influenced the time series in the past. They are:1. Trend2. Cycle3. Season4. IrregularThese factors will also be influences in the forecast year as well. If I isolate them I will be able to forecast the earnings for 1995. Finally, I will measure the accuracy of my forecast in order to decide if the model is capable of giving accurate forecasts. The actual time series are expressed in this model as the product of the four components: Yt = T*C*S*I.3. RESULTSDuring the years of 1989 - 1994, Harley Davidson, Inc. experienced an overall increase in interim earnings. The peak of their earnings occurred in the second quarter of 1994 with a .63; while the lowest earnings occurred during the fourth quarter of 1991 with only a .09.3.1 CMAChart 2 shows the Center Moving Average and Harley Davidson's actual interim earnings. The CMAV is the product of two factors, Cycle and Trend. It represents the values of interim earnings if Trend and Cycle were considered the only factors influencing them. As a deductive result, the discrepancy between the actual sales and the CMAV must be due to other components. It is most likely the Seasonal because the Irregular is rarely relevant. The CMAV is obtained to reduce the volatility in the data over time, and to calculate the seasonality. The CMAV is considered a smoothing technique for its capacity to not follow the individual peaks and valleys that Seasonal and Irregular components will cause on a time series.Several limitations arise from the use of CMAV. As you can see from Chart 2 the values do not cover the entire 24 quarters. CMAV removes seasonality by taking the average over four quarters instead of just one, losing four observations in all, two at the beginning and two at the end. This is particularly important because the last two observations are used to forecast the Cycle value. Other limitations are that CMAV gives all observations the same weight no matter which is more recent, when the more recent data should really be given greater consideration than the older ones. And finally, since the CMAV will always drag behind the Trend, it is not suited for...

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