Business Studies Assignment Paper on Forecasting



            Finance department of a company may be interested in knowing the future market price of their company’s shares to forecast the possible future value of the company and evaluate the company’s future financial performance. Shareholders also are normally interested in future share prices to make decisions on whether to buy, hold or sell their shares. Moving averages is a forecasting method that can be used to forecast future share prices. Using this method, this paper forecasts the share prices of Apple Inc. and discusses the strengths and shortcomings of using this forecasting method.

            Moving Averages forecasting method is simple; hence, can be used by investors with little or moderate statistical knowledge. Thus its application in the stock market price forecasting is more practical. By calculating averages, the method smoothens out short term data fluctuations or what is statistically known as ‘noise’ such that the long term trends or patterns in the data which can be used to predict future outcomes are made clearly visible. Moving average forecasts the next period simply by finding the average of previous periods (Wisner, 2011).  To forecast the outcome on period four, for example, using moving averages the actual results for periods one, two and three and summed and divided by three and the result will be the forecast for period four. To forecast period five, the actual data for periods two, three and four are added and averaged to find the fifth period forecast. This would be a three period moving average since forecasting has been done by calculating the average of the three previous periods. The following computations show how March 2014 market price of Apple Inc. shares can be forecasted by a four month moving average using historical share prices from yahoo finance.

Date Closing Price 4-Month MA
Feb-13 430.52
Mar-13 431.74
Apr-13 431.86
May-    13 441.54
Jun-13 389.31 433.92
Jul-13 444.29 423.61
Aug-13 481.51 426.75
Sep-13 471.16 439.16
Oct-13 516.57 446.57
Nov-13 552.76 478.38
Dec-13 557.68 505.5
Jan-14 497.62 524.54
Feb-14 522.06 531.16
Mar-14 Forecasted 532.53

            The moving average forecasts do not give a precise prediction of what the future share price would be. For example it predicts that the price for January 2014 would be $ 524.24 while the actual price was $ 497.62 giving an error of $ 26.62.  The forecasts cannot be fully trusted however they can still give a rough idea of what the future prices would be. I chose to forecast Apple stock prices because its management would ordinarily be interested on their performance in the market in comparison to its competitors. I also plan to invest in this company in the near future. To make a profit out of such an investment, one must have a general idea on the pattern of stock price movements and sell them when prices are likely to drop in the future or buy stock when prices are currently low but are likely to rise in the future. The accuracy of moving averages can be increased by reducing the number of periods used to calculate the averages. Jain and Malehorn (2005) write that more recent data is more accurate when used to predict the future than when older data is used. Therefore using a two month moving average to predict share prices would be more accurate than the four month moving average used above. Price trends also change over time. At times prices generally increase while other times they drop. Using data from the period they drop for prediction in a period they increase would be erroneous. To eliminate this error the latest data would be used in calculating the averages.


            Moving averages forecasting method is widely used due to its simplicity and thus applicability in the business environment. However, its accuracy can be enhanced by using shorter periods which uses most recent data and thus recognizes latest changes in data patterns.


Apple Inc. Historical Prices, Retrieved on February 26, 2014 from;

Jain C. L., Malehorn J. (2005)  Practical Guide to Business Forecasting  (2nd. Ed). New

York, NY: Graceway Publishing Company. Retrieved on February 26, 2014, 2014 from;

Wisner J. D. (2011) Principles of Supply Chain Management: A Balanced Approach (3rd ed.),        Mason OH: South-Western Cengage Learning. Retrieved on February 26, 2014 from;