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ConocoPhillips’ share price model revisited

Following my recent post on energy related companies and specifically on ConocoPhillips I have compiled a paper revisiting all COP models. Please follow the link to download the full text.

ConocoPhillips’ share price model revisited

Abstract

Three years ago we found a statistically reliable link between ConocoPhillips’ (NYSE: COP) stock price and the difference between the core and headline CPI in the United States. In this article, the original relationship is revisited with new data available since 2009. The agreement between the observed monthly closing price (adjusted for dividends and splits) and that predicted from the CPI difference is confirmed. The original quantitative link is validated. In order to improve the accuracy of the COP price prediction a series of advanced models is developed. The original set of two major CPIs is extended by smaller components of the headline CPIs (e.g. the CPIs of motor fuel and housing energy) and several PPIs (e.g. the PPIs of crude oil and coal) which may be inherently related to ConocoPhillips and other energy companies. These advanced models have demonstrated much lower modeling errors with better statistical properties. The earlier reported quasi-linear trend in the CPI difference is also revisited. This trend allows for an accurate prediction of the COP prices at a five to ten year horizon.

Key words: stock price, ConocoPhillips, predicti

Baker Hughes is likely undervalued

The evolution of Baker Hughes (NYSE: BHI) share price can be predicted quantitatively. Energy category of the S&P 500 list contains many companies linked to various types of energy production and services. BHI supplies oilfield services, products, and technology services and systems to the oil and natural gas industry worldwide.  As for other energy related companies, we assume that BHI share price is driven by the change in some energy-related prices. There are two opportunities: consumer prices and producer prices. In our previous article, we found that Noble Energy (NBL) is rather driven by the PPI of natural gas. On the other hand, many companies are deeply involved in consumer markets and might likely depend on consumer prices.  

Our concept assumes that a BHI share price can be represented as a weighted sum of two individual producer or consumer price indices selected from a predefined set. We split the overall set into two pieces. There are five producer price indices (all borrowed from the Bureau of Labor Statistics): the overall PPI; the PPI of electric power, EL; of natural gas, GAS; of coal, COAL; and the PPI of oil, OIL.  There are nine  CPIs: the headline CPI, C; the core CPI, CC; the CPI less energy, CE; the CPI of energy, CC; the CPI of motor fuel, MF; the CPI of household energy, HHE; the CPI of fuels and utilities, FU;  the CPI of food and beverages, F; and the CPI of housing, H.

All PPIs, CPIs and the monthly closing price are available now through March 2012. Our model seeks for the best pair of PPIs (CPIs) which minimizes the RMS error since 2003. We also allow both defining indices to lead or lag behind the modeled share price. Additionally, we introduced a linear time trend and an intercept term. The best fit model is obtained with the pair C and F:

BHI(t)= 4.409C(t-0) – 4.174F(t-6) 4.797(t-2000) – 50.84; sterr=$6.73

where BHI(t) is the (monthly closing) share price in U.S. dollars. We allowed both time leads to vary between 0 and 12 months. In the best model, the CPI of food leads the share price by 6 months and the headline CPI evolves in sync with the share price.  The model standard error for the period from July 2003 to March 2012 is $4.75. We report only on reliable models which do not change over eight to twelve months in a row. Thus, the above model is valid and reliable since the middle of 2011.  

Figure 1 shows how the model predicts the current BHI price.  As for many energy companies, there were two major fluctuations in the first half of 2010 and in the fourth quarter of 2011 (see Figure 2 for the model residual error). Both ended on the fundamental price curve.  In November 2011, we would estimate the BHI price as an undervalued one. The closing price of March 2012 is undervalued by $15.  We expect the price to return to the fundamental level as defined by the model, i.e. by the CPIs.

Figure 1. The observed and predicted monthly closing prices for BHI between July 2003 and March 2012.

Figure 2. The model residual error.

Noble Energy is likely driven by natural gas

In this article, we quantitatively predict the evolution of Noble Energy (NYSE: NBL) share price. As for other companies from Energy category of the S&P 500 list, one may assume that its share price is driven by the change in some energy-related prices. It might be a commonplace that oil companies depend on oil price as they have to depend on the price and amount of goods and services they produce/sell. When oil has a higher pricing power these companies are expected to show rising profits also reflected in their share prices.

NBL engages in the acquisition, exploration, development, production, and marketing of crude oil, natural gas, and natural gas liquids.  We model the evolution of the NBL share price as a weighted sum of two individual producer price indices selected from a set of five producer price indices borrowed from the Bureau of Labor Statistics: the overall PPI, the PPI of electric power, EL, of natural gas, GAS, of coal, COAL, and the PPI of oil, OIL. All PPIs and the monthly closing price are available now through March 2012. Our model seeks for the best pair of PPIs which minimizes the error since 2003. We also allow both defining PPIs lead or lag behind the modeled share price. Additionally, we introduced a linear time trend and an intercept term. The best fit model is obtained with the pair GAS and PPI:

NBL(t)= -0.049GAS(t-3) + 1.367PPI(t-0) - 1.22(t-2000) – 158.53; sterr=$4.75

where NBL(t) is the (monthly closing) share price in U.S. dollars. We allowed both time leads to vary between 0 and 12 months. In the best model, the PPI of natural gas leads the share price by 3 months and the PPI lead is zero months. In other words, gas drives the NBL share with a three month delay.

Surprisingly, the slope of GAS is negative, i.e. falling gas prices drive the NBL price in opposite direction.  The PPI slope is positive and the share price rises with the overall producer price. The model standard error for the period from July 2003 to March 2012 is $4.75. We report only on reliable models which do not change over eight to twelve months in a row. Thus, the above model is valid and reliable since the middle of 2011.

Figure 1 shows that the model based on the producer price index of natural gas and the PPI (domestic production) accurately predicts the current NBL price.  As for many energy companies, there were two major fluctuations in the first half of 2010 and in the fourth quarter of 2011 (see Figure 2 for the model residual error). Both ended on the fundamental price curve. This behavior is illustrative – all deviations finally return to the predicted curve. In November 2011, we would estimate the NBL price as a highly undervalued one. Since the actual price gravitates to the predicted one we consider our prediction as the fundamental price level, which is fully defined by the PPI of natural gas and the PPI.    

Figure 1. The observed and predicted monthly closing prices for NBL between July 2003 and March 2012.

Figure 2. The model residual error

EOG Resources is driven by natural gas and oil

Here we model the evolution of EOG Resources (NYSE: EOG) share price. Imagine that you have to predict (describe) the evolution of a share price for a company from Energy sector. A natural first guess is that this share price is driven by the change in some energy-related prices. Apparently, financial health of this company depends on the price and amount goods and services it sells. When these goods and services have a higher pricing power the company is likely healthy and generates some extra profit reflected in its share price.

EOG engages in the exploration, development, production, and marketing of crude oil and natural gas.  We extend our original model and describe the evolution of the EOG share price as a weighted sum of two individual producer price indices selected from a set of five producer price indices borrowed from the Bureau of Labor Statistics: the overall PPI, the PPI of electric power, EL, of natural gas, GAS, of coal, COAL, and the PPI of oil, OIL. (We do not use consumer price indices in for EOG.) Thus, the model seeks for the best pair of PPIs which minimizes the error since 2003. We also allow both defining PPIs lead or lag behind the modeled share price. Additionally, we introduced a linear time trend and an intercept term. Not surprisingly, the best fit model is obtained with the pair GAS and OIL:

EOG(t)= 0.129GAS(t-0) + 0.115OIL(t-0) + 7.85(t-2000) – 45.12; sterr=$7.77  

where EOG(t) is the (monthly closing) share price in U.S. dollars. We allowed both time leads to vary between 0 and 12 months. In the best model, both time leads are zero, i.e. the share price evolves in sync with the PPI of oil and gas. Instructively, both slopes in the model are positive and the share prices rises with that of oil and gas. The time trend coefficient is also positive and provides an annual increase of $7.85.  The model standard error for the period from July 2003 to March 2012 is $7.77. It is important that the above model is valid and reliable since the middle of 2011, i.e. the defining PPIs, lags, and coefficients are the same for all contemporary models since July 2011.   

Figure 1 shows that the model based on the producer price index of natural gas and crude petroleum (domestic production) accurately predicts the current EOG price.  There were two major fluctuations in the first half of 2010 and in the fourth quarter of 2011. Both ended on the fundamental price curve. This behavior is very indicative – all deviations should return to the predicted curve. In November 2011, we would estimate the EOG price as a highly undervalued one.  In that sense, the predicted price might be considered as the fundamental price level, which is fully defined by the producer prices of natural gas and oil.   

Figure 1. The observed and predicted monthly closing prices for EOG between July 2003 and March 2012.

Predicting the Oracle of Omaha

It is great pleasure to quantitatively assess the future of Berkshire Hathaway (NYSE: BRK-B). The Oracle of Omaha is considered as one of the greatest investors. Here we would like to introduce a deterministic model for a BRK-B share price. Berkshire Hathaway, Inc. is a publicly owned investment manager. The BRKB structure is highly diversified with interests from GEICO (car insurance) to Dairy Queen.

The model is deterministic since it has been obtained by decomposition of a share price into a weighted sum of two consumer price indices. One may follow up our simple assumption that the growth in the CPIs related to BRK-B, e.g. transportation service, TS, and dairy products, DAIRY, might be seen in the change of the pricing power for the studied company. These two CPIs are revealed as the drivers of the BRK-B price. However, they were actually selected using the LSQ method from a set of 92 different (not seasonally adjusted) CPIs. The best model predicting the monthly closing prices adjusted for dividends and splits has the smallest RMS error from July 2003 to March 2012.

We have borrowed the time series of monthly closing prices of BRK-B from Yahoo.com (the closing price for March 2012 is included) and the CPI estimates through February 2012 are published by the BLS. The best-fit model for BRKB(t) is as follows:

BRKB(t) = -0.802DAIRY(t-11) – 2.24TS(t-7) + 21.13(t-2000) + 584.66, March2012

where BRKB(t) is the BRK-B share price in U.S. dollars, t is calendar time. Figure 1 displays the evolution of the defining CPIs since 2002. Instructively, both indices are relevant to the BRK-B structure and have negative slopes. The DAIRY index likely has a larger impact on the growth of the price becasue of higher amplitude oscillations. The TS index is rather a linear line since 2002 with only a small positive fluctuation (negative impact on the price) in the end of 2008.

Figure 2 depicts the high and low monthly prices for an BRK-B share together with the predicted and measured monthly closing prices. The predicted prices are well within the limits of the share price uncertainty. The model residual error is shown in Figure 3 with the standard deviation between July 2003 and March 2012 of $4.73.

The accelerated growth in the DAIRY index since April 2011 induced a mid-term decline in the share price. This rise in dairy price did not come to end yet and we cannot exclude that the actual BRK-B price will return to the predicted level of $73 per share by the third quarter of 2012. The TS price index does not show any sign of deceleration and food price together with dairy products will likely be rising till 2014. If the model is right, we may observe in 2013 some further decline in BRK-B price.

Figure 1. The evolution of defining indices.

Figure 2. Observed and predicted monthly closing prices for a BRK-B share.

Figure 3. The model residual error: sterr=$4.73.

How deep will the S&P 500 fall?

Several days ago we predicted the current fall in the S&P 500 index. For this reason, we did not enter the stock market and instead invested in a defensive portfolio. We are waiting the level of 1350.  The reason is explained below.

Figure 1 shows the evolution of the S&P 500 index since 1980. After 1995, the index behavior reveals some saw teeth with peaks in 2000 and 2007. The current growth resembles those between 1997 and 2000 and from 2003 and 2007.  There are two deep troughs in 2002 and 2009 which are marked by red and green lines, respectively.  For the current analysis we assume that the repeated shape of the teeth is likely induced by a degree of similarity in the evolution of macroeconomic variables. The intuition behind such an assumption is obvious – in the long run the market depends on the overall economic growth.

Having two peaks and troughs between 1995 and 2009, what can we say about the current growth in the S&P 500? Before making any statistical estimates, in Figure 2 we have shifted forward the original curve in Figure 1 in order to match the 2009 trough (blue line).  When the 2002 and 2009 troughs are matched, one can see that the current growth path closely repeats that after 2002. The first big deviation from the blues curve in Figure 2 started in 2011 and had amplitude of 150 units (from 1210 to 1360).  The black curve returned to the blue one in August/September 2011. A month ago, we observed a middle-size deviation of about 100 units and predicted that the index will have a negative correction down to the level of 1300 any time soon.  If the index will repeat the path of the previous rally one-to-one, one may expect the peak level of 1500 in the end of 2013.  In two to four weeks it might be a good time to invest for a 15% return cumulated to October 2013 (but not more than two months), when the negative correction is over. 

With the S&P 500 falling down to 1350, the prediction does not seem inappropriate. The next several weeks should decide on the new level. In Figure 2, we have drawn the fall we expect by the end of May 2012. We would wait by the end of April to decide on the following move in the S&P 500. If the current fall will reach 1300, it’s likely a good time to buy. Otherwise, the end of May is the horizon to wait the bottom.

Figure 1. The evolution of the S&P 500 market index between 1980 and 2012.

Figure 2. The curve in Figure 1 peak is shifted forward to match the 2009 trough (blue line). Red line – expected fall in the S&P 500: from 1400 in Mach to 1300 in May.