Moving average forecasting models are powerful tools that help managers in makin

Question

Moving average forecasting models are powerful tools that help managers in making educated forecasting decisions. A moving average is mainly used to forecast short historical range data. This tool along with other forecasting tools is now computerized such as in Excel, which makes it easy to use. With regard to moving average forecasting, read the following task.

Obtain the daily price data over the past five years for three different stocks. Data can be obtained from the Internet by using the following keywords: stock price data, return data, company data, and stock returns.

Create trend-moving averages with the following values form: 10, 100, and 200. Graph the data with Excel.

Create centered-moving averages with the following values form: 10, 100, and 200. Graph the data with Excel.

How do the moving averages for the same values of m compare between a trend-moving average and a centered-moving average?

Explain how these moving averages can assist a stock analyst in determining the stocks’ price direction. Provide a detailed explanation with justifications.

Submit your answers in an eight- to ten-page Word document and in an Excel sheet.

Explanation / Answer

The graph can be selected by simpling selecting the charts from the insert menu on excel

In financial applications a simple moving average (SMA) is the unweighted mean of the previous n datum points. However, in science and engineering the mean is normally taken from an equal number of data on either side of a central value. This ensures that variations in the mean are aligned with the variations in the data rather than being shifted in time. An example of a simple unweighted running mean for a n-day sample of closing price is the mean of the previous n days' closing prices. If those prices are p_M, p_{M-1},dots,p_{M-(n-1)} then the formula is

{SMA} = { p_M + p_{M-1} + cdots + p_{M-(n-1)} over n }

When calculating successive values, a new value comes into the sum and an old value drops out, meaning a full summation each time is unnecessary for this simple case,

{SMA}_mathrm{today} = extit{SMA}_mathrm{yesterday} - {p_{M-n} over n} + {p_{M} over n}

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