n July 2010 we presented a share price model for IBM for the period between July 2003 and May 2010:

IBM(t) = 4.93MVR(t-12) – 3.51TS(t-4) - 10.39(t-2000) + 39.39

where MVR is the index of motor vehicle maintenance and repair (CUUR0000SETD) and TS is the index of transportation services ( CUUR0000SAS4). The former CPI component led the share price by 12 months and the latter one led by 4 months.

Here we extend the modeling period in both directions - between January 1995 and December 2010. As before, the model coefficients are obtained by minimizing the RMS residual error. Current IBM model is as follows:

IBM(t) = -4.32*H(t-1) – 1.48*MVI(t-1) + 40.69(t-2000) + 779.0

where H is the index of housing and MVI is the index of motor vehicle insurance. Figure 1 depicts the overall evolution of both involved indices. The index of housing was on rise before 2009. Since December 2008, this index has been slightly decreasing. Since it has negative influences on the share price, one can expect an increase in IBM price. The MVI index has been quickly growing over the entire period, except during some short segments. Thus, did not allow the share to increase to fast since linear trend also has positive influence on the price. All in all, these two defining components provide the best fit model between December 2009 and December 2010.

The predicted curve in Figure 2 leads the observed price by 1 month with the residual error of $9.49 for the period between January 1995 and December 2010. Currently, the price is slightly underestimated by the model, as Figure 3 shows, and one cannot exclude a downward correction in the first quarter of 2011.

In the long run, the index of housing will be decreasing during the next 10 years. This is a helpful background for IBM share. The MVI has a clear rise/plateau structure. The next segment is likely to be a shelf, starting in 2011 of 2012. Hence, the price share looks good at a two-year horizon.

Figure 1. Evolution of the price of H and MVI.

Figure 2. Observed and predicted IBM share prices.

Figure 3. Residual error of the model. Mean residual error is 0 with standard deviation of $9.49.