choose one of the following forecasting methods discussed in

choose one of the following forecasting methods discussed in this chapter: last-value, averaging, moving-average, or exponential smoothing. Identify the conditions when the method is most appropriate to use and give an example of an application of this method.

Solution

Solution :

1) Last Value Averaging Method :

Value Averaging is a combination of its better-known cousin - \"dollar-cost averaging\" - and a process known as \"portfolio rebalancing.\"

The value averaging method, has been shown to produce better results over time than the old \"dollar-cost averaging\" method. Edeleson has tested VA using simulations to compare VA to DCA and purchases of a constant number of shares in each investment period. Without considering possible differences in risk, Edleson concludes:

“There is an inherent return advantage of value averaging (over dollar-cost averaging and purchase of a constant number of shares).”

“It’s about as close to ‘buy low, sell high’ as we’re going to get without a crystal ball.”

2) Averaging methods :

If a time series is generated by a constant process subject to random error, then mean is a useful statistic and can be used as a forecast for the next period.

Averaging methods are suitable for stationary time series data where the series is in equilibrium around a constant value ( the underlying mean) with a constant variance over time.

3) Exponential smoothing methods:

The simplest exponential smoothing method is the single smoothing (SES) method where only one parameter needs to be estimated

Holt’s method makes use of two different parameters and allows forecasting for series with trend.

Holt-Winters’ method involves three smoothing parameters to smooth the data, the trend, and the seasonal index.

The moving average for time period t is the mean of the “k” most recent observations.

The constant number k is specified at the outset.

The smaller the number k, the more weight is given to recent periods.

The greater the number k, the less weight is given to more recent periods.

4) Moving Averaging :

A large k is desirable when there are wide, infrequent fluctuations in the series.

A small k is most desirable when there are sudden shifts in the level of series.

For quarterly data, a four-quarter moving average, MA(4), eliminates or averages out seasonal effects.