ConocoPhillips share price
Our original pricing model states that a share price, for example, that of ConocoPhillips, COP(t), can be approximated by a linear function of the difference between the core CPI, coreCPI, and headline CPI:
COP(t) = A + B (coreCPI - CPI(t)) (1)
where A and B are empirical constants; t is the elapsed time. Here we extend the set of defining indices by the consumer price index of energy, eCPI, and the producer price index of crude petroleum, pPPI, together with the overall PPI. Thus, we test the following models for the period between 2001 and 2011:
COP(t) = A1 + B1(coreCPI - eCPI(t)) (2)
COP(t) = A2 + B2(pPPI - PPI(t)) (3)
Figures 1 through 3 compare the original and new predictions for COP. Coefficients in (1) through (3) are given in Figure captions. The best model for the period between 2001 and July 2011 is based on the index of energy and core CPI. Practically the same accuracy is associated with the original model as based on the core and headline CPI. At the same time, model (3) based on the producer price indices is the worst and has failed to predict the amplitude of the largest oscillation in 2008.
We have predicted oil price to fall through 2016. In 2011, we expect oil price to fall down to $70 per barrel. Considering these short- and mid-term predictions one can conclude that ConocoPhillips share price will be falling as well.
Figure 1. The observed COP price and that predicted from the core and headline CPI. A=75, B=-5.5.
Figure 2. The observed COP price and that predicted from the core CPI and the consumer price index of energy. A1=58, B1=-0.54.
Figure 3. The observed COP price and that predicted from the overall PPI and the producer price index of crude petroleum (domestic production). A2=45, B2=-0.3
Avon Products share price
Here we present a brand new share price model for Avon Products (NYSE: AVP). In April 2011, this model also showed a higher level of reliability and described the price through March 2011. We intentionally skipped it to publish in this blog because the predicted price showed just a small fall. The model was based on our concept linking share pieces and consumer price indices. The share price model for Avon Products was defined by the index of other household equipment and furnishing (OHEF) and that of public transportations (TPU). Figure 1 illustrates the evolution of these indices. In the April model, the former CPI component led the share price by 8 months and the latter one led by 5 months.
Here we revisit the model using the monthly closing prices (adjusted for splits and dividends) and CPIs for the period through September 2011. (The CPIs are available only for August 2011.) The principal result is that the underlying model is practically the same as six months ago with the same time lags but slightly different coefficients. This model predicted the fall in the price observed since June 2011 five months in advance. The March and September models for AVP(t) are as follows:
AVP(t) = -2.43OHEF(t-8) – 0.33TPU(t-5) - 5.22(t-1990) + 392.49, March 2011
AVP(t) = -2.25OHEF(t-8) – 0.33TPU(t-5) - 4.58(t-1990) + 366.41, September 2011
where AVP(t) is a share price in US dolalrs, t is calendar time. Relevant coefficients are both negative. The slope of time trend is also negative. There is some fluctuation in all coefficients caused by the uncertainty in measurements of both the stock prices and CPIs. Nevertheless, both models provide an accurate prediction at a five-month horizon as Figure 2 depicts.
Currently, the model shows that the price has reached a stable level of $20 per share and will not be changing in the fourth quarter of 2011. The predicted curve in Figure 2 (both versions are depicted) leads the observed price by 5 months with the residual error of $2.90 ($3.08 in April) for the period between July 2003 and September 2011. The model residual for the same period is shown in Figure 3.
Figure 1. Evolution of the price of OHEF and TPU.
Figure 2. Observed and predicted AVP share prices. Upper panel – March 2011; lower panel – September 2011.
Figure 3. Residual error of the model.