Forecasting Microsoft's Revenues

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A forecasting project for an economics course at the Schulich School of Business

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  • Time series begins in Q2 1996 = October 1, 1995 Q2 1996 release of Windows 95 and MSN Q3 2007 release of Windows Vista & Microsoft Office 2007 Dip in 2008-2009 due to recession but quickly rebounded in fall of 2009 Q2 2010 release of Windows 7 General upward trend with increasing variability
  • Peaks are in Q2 = Fall Troughs are in Q1 = Summer Lots of variation = demand for Microsoft products affected by economic conditions
  • Forecasting horizon: using data up to Q2 2010 to forecast 4 quarters in 2010 for which financial reports have been released for comparison + 2 quarters in 2011
  • Winter’s Method used because there is trend and seasonal component Smoothing constants derived from iterative process that yielded lowest forecast summary statistics Fits the data really well, captures both the trend and the seasonality
  • This is a fair forecast, captures the general movement of the data, and captures the seasonal spike at Q2
  • Parabolic trend minimizes the error as compared to linear and exponential trends. Captures the trend but does not reflect real, recent changes
  • Not very good, while it captures the general upward trend, it doesn’t respond well to recent variability
  • Original series exhibited nonstationarity – had an growth trend and increasing variability. This requires transformation: To get rid of increasing variability, get natural logarithm To deseasonalize, logged data differenced by seasonal lag of 4, this also removes trend
  • Autocorrelations: die out, partial autocorrelation: cuts off after 1 st significant autocorrelation; suggests regular AR(1) There is also significant partial autocorrelation at time lag 4, suggests seasonal AR(1) 4 The high P-values suggest Q-stats are not significantly different from 0 and model fits data well
  • Captures seasonal changes but quite far off in reflecting immediate changes, bringing back seasonality and delogging.
  • Winter’s Method has lowest MSD and MAD, minimizes errors, model fits data well, captures growth and seasonal movements, forecasts were most accurate in capturing immediate changes in the series
  • Univariate shortcomings: does not take into account changes in Microsoft’s strategy (i.e. New products launches), environmental factors that significantly shift the behaviour of the trend, other irregularities Other Variables used to supplement historical trend: US data - US accounts for 60% of Microsoft’s sales GDP – GDP a great indicator of overall health of the economy relevant for both B2B and B2C segments, tends to be a good indicator of profits Personal Income – important determinant of future consumer demand Retail Sales – good indicator of level of consumer expenditure, predictor of consumer demand
  • Forecasting Microsoft's Revenues

    1. 1. Sales Forecasting Jeric (Jose) Kison
    2. 2. BACKGROUND
    3. 3. Microsoft’s Quarterly Sales
    4. 4. Seasonality
    5. 5. FORECASTING
    6. 6. Winter’s Method
    7. 7. Winter’s Method Quarter Actual sales Forecasted Sales Q3 2010 $ 14,503,000,000 $15,599,000,000 Q4 2010 $ 16,039,000,000 $15,961,700,000 Q1 2011 $ 16,195,000,000 $15,702,300,000 Q2 2011 $ 19, 953,000,000 $19,043,100,000 Q3 2011 - $16,652,900,000 Q4 2011 - $17,022,300,000
    8. 8. Parabolic Trend
    9. 9. Parabolic Trend Quarter Actual sales Forecasted Sales Q3 2010 $ 14,503,000,000 $16,659,400,000 Q4 2010 $ 16,039,000,000 $16,999,300,000 Q1 2011 $ 16,195,000,000 $17,342,100,000 Q2 2011 $ 19, 953,000,000 $17,687,800,000 Q3 2011 - $16,659,400,000 Q4 2011 - $16,999,300,000
    10. 10. ARIMA
    11. 11. ARIMA <ul><li>Model: ARIMA(1,0,0)(1,1,0) 4 </li></ul>Final Estimates of Parameters Type Coef SE Coef T P AR 1 0.4066 0.1364 2.98 0.004 SAR 4 -0.7263 0.1218 -5.96 0.000 Constant -0.01581 0.01656 -0.95 0.344 Differencing: 0 regular, 1 seasonal of order 4 Number of observations: Original series 56, after differencing 52 Residuals: SS = 0.693955 (backforecasts excluded) MS = 0.014162 DF = 49 Modified Box-Pierce (Ljung-Box) Chi-Square statistic Lag 12 24 36 48 Chi-Square 14.3 18.1 40.8 51.4 DF 9 21 33 45 P-Value 0.112 0.642 0.164 0.237
    12. 12. ARIMA Quarter Actual sales Forecasted Sales Q3 2010 $ 14,503,000,000 $12,925,265,037 Q4 2010 $ 16,039,000,000 $13,610,182,455 Q1 2011 $ 16,195,000,000 $12,848,267,877 Q2 2011 $ 19, 953,000,000 $19,419,519,788 Q3 2011 - $13,339,540,703 Q4 2011 - $13,747,055,400
    13. 13. Which has the best forecast? Winter’s Method Forecast summary statistics Parabolic Trend Winter’s Method α = 0.5, β = 0.1, γ = 0.4 ARIMA (1,0,0)(1,1,0)4 MSD 9.71164E+17 8.03534E+17 4.97E+18 MAD 6.75902E+08 5.53333E+08 1.97E+09 MAPE 6.85693E+00 6.07142E+00 1.23E-01
    14. 14. Multivariable regression model <ul><li>Variables to be used </li></ul><ul><ul><li>US Economic Indicators </li></ul></ul><ul><ul><ul><li>GDP </li></ul></ul></ul><ul><ul><ul><li>Personal Income </li></ul></ul></ul><ul><ul><ul><li>Retail Sales </li></ul></ul></ul>
    15. 15. THANK YOU!

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