Anadarko Petroleum will follow oil price

The evolution of Anadarko Petroleum’s (NYSE: APC) share price is of interest since it is different from that demonstrated by Apache (APA) which is presented in our previous article.  Both companies are likely driven by the same forces but their current prices are on opposite sides from their fundamental levels estimated with our pricing model.  According to the previously developed procedure, we also compare the APC pricing model to that for ConocoPhillips (COP), which is a recognized benchmark for energy related companies.

Imagine that you have to predict (describe) the evolution a share price for a company from Energy sector. It would not be a big mistake to assume that this share price is likely to be driven by the change in the overall energy price or some of its components (we use price indices for modeling). Even if the company does not change its production the overall increase in the price of its product should be manifested in the overall profit and thus the share price. On the other hand, when the overall price level (as expressed by the headline CPI) rises faster than the energy price index (say, 10% vs. 1% per year, respectively) one should not expect the energy company to gain extra pricing power. The company would rather suffer a share price decline.  Thus, considering the secular increase in the overall price level, it is not the absolute change in energy prices what affects the stock price but its current deviation from some energy independent price.  We have proposed to use the simplest model as based on the difference between the headline CPI, C, and the core CPI, CC, without any time lag between these indices and the share price. The headline CPI includes all kinds of energy and thus provides the broadest proxy to the energy price index. The core CPI excludes energy (and food) and thus may represent the energy independent and dynamic reference.

First, we present here the model for COP. We use it as a benchmark showing the quality of the concept and its predictive power. Figure 1 depicts the observed and modeled COP prices. Taking into account that both defining CPIs might be not the best proxies to some true defining indices, the accuracy of prediction is very good. We consider the predicted price as a fundamental one, i.e. the price which is defined by two economy-wide or fundamental indices. Quantitatively, we have estimated the following relationships to minimize the model error between 1998 and 2012:

COP(t) = 72.3 – 5.35(CC(t) - C(t))  (1)

where COP(t) is the share price in U.S. dollars at time t.  

Figure 1. Historic (monthly closing) prices for COP (black line) and the scaled difference between the core CPI and the headline CPI (red line).

Now we may suggest that the COP model determines the benchmark behavior for an energy related company, i.e. its share price has to gravitate to the fundamental price defined by the difference between the core and headline CPI.   In our previous article, we estimated an empirical model for APA:  

APA(t) = 110 – 8.1(CC(t) - C(t))

For APC, the best fit relationship is as follows:

APC(t) = 67 – 4.7(CC(t) - C(t))  (2)

Figure 2 depicts both models, i.e. the observed and predicted prices for APA and APC. For APC, the overall agreement is relatively good but the deviation from the predicted price has been much higher since 2009.   From Figure 2, the actual price is currently overvalued by $15. The underlying model is too crude, however, and we have developed several advanced APC models. The best from these advanced models shows that the current APC price is only slightly overvalued. 

Figure 2. Historic (monthly closing) prices for APA (upper panel) and APC and the scaled difference between the core CPI and the headline CPI - relationship (2). 

We have extended the original model and described the evolution of the APC share price as a weighted sum of two individual consumer price indices (or PPIs) selected from a large set of CPIs borrowed from the Bureau of Labor Statistics. We allow both defining CPIs (PPIs) lead the modeled share price. Additionally, we introduced a linear time trend on top of the intercept. As for many already presented companies, we have tested two principal pairs of CPIs: C and CC; CC and the index of energy, E, as well as the pair the PPI and the producer price index of crude oil, OIL. The best fit (as defined by standard error) model is obtained with the pair PPI and OIL:

APC(t)= 3.28C(t) – 5.56CC(t-1) + 12.25(t-2000) + 323.56; sterr=$6.12 (3)

APC(t)= -2.79CC(t-2) + 0.32E(t) + 13.73(t-2000)  + 326.49; sterr=$5.80 (4)

APC(t)= -0.21PPI(t-10) + 0.14OIL(t-9) + 4.31(t-2000) – 19.95; sterr=$5.48 (5)

where APC(t) is the (monthly closing) share price in U.S. dollars. We allowed both time leads in (3) through (5) to vary between 0 and 12 months. Figures 3 through 5 depict the observed and predicted monthly prices from (3) through (5).  For an oil company, it is not excluded that oil controls its price.

Figure 5 shows that the advanced model based on the producer price index of crude petroleum (domestic production) accurately predicts the current APC price.  There were two major excursions in the first half of 2010 and in the fourth quarter of 2011. Both ended on the fundamental price curve. This behavior is very instructive – all deviations should return to the predicted curve.

Figure 3. The observed and predicted monthly closing prices for APC between July 2003 and March 2012. The model is based on C and CC.

Figure 4. The observed and predicted monthly closing prices for APC between July 2003 and |March 2012. The model is based on CC and E.

Figure 5. The observed and predicted monthly closing prices for APC between July 2003 and March 2012. The model is based on PPI and OIL.

What is a better investment Apache or ConocoPhillips? An academic model

Here we model the evolution of Apache (NYSE: APA) share price and evaluate its current level relative to that predicted by our pricing model. We also compare the APA model and the ConocoPhillips (COP) model in order to evaluate their relative performance. In other words, we estimate quantitatively which of these two companies provides a better return. Two main findings can be formulated as follows: 1) the current price is slightly lower than that predicted by the model; 2) both companies show the same level of return.


The first finding is not surprising. We have already reported that for many energy related companies (e.g. Newfield Exploration (NFX) and Peabody Energy (BTU)) our empirical models show that their current prices are highly undervalued. In this group, APA is not the worst. However, it is still slightly undervalued despite our model has accurately predicted the rally between 2003 and 2007, the sharp fall in 2008 and the following recovery up to the third quarter of 2011. The second finding is might be an expected one since the stock market seeks for the best return. Therefore, investments are redistributed in a way to provide some constant return. At least this approach should work for all successful companies in the same industry.

We assume that a share price of an energy company is likely to be driven by the change in the overall energy price or some of its components. Considering the secular increase (change) in the overall price level it is not the absolute change in energy prices what affects the stock price but the difference between the energy price and some energy independent price. The simplest model can be based on the difference between the headline CPI, C, and the core CPI, CC, without any time lag between these indices and the share price. The headline CPI includes all kinds of energy and thus provides the broadest proxy to the energy price index. The core CPI excludes energy (and food) and thus represents the energy independent and dynamic reference. Four years ago, these two indices were used in our original models for ConocoPhillips and Exxon Mobil (XOM) and are retained as a benchmark since then.

During these four years, the best model was that for COP. We use it as a benchmark showing the quality of the concept and its predictive power. Figure 1 depicts the observed and modeled COP prices. Taking into account the character of the defining CPIs (they include many irrelevant components which are measurement noise for the model) the agreement between curves is outstanding. One can consider the predicted price as a fundamental one. These are two broadest consumer price indices which define the fundamental price. Quantitatively, we have estimated the following relationships to minimize the model error between 1998 and 2012:

COP(t) = 72.3 – 5.35(CC(t) - C(t)) (1)

where COP(t) is the share price in U.S. dollars at time t.

Figure 1. Historic (monthly closing) prices for COP (black line) and the scaled difference between the core CPI and the headline CPI (red line).

Accordingly, we depict in Figure 2 a similar model for APA. The best fit relationship is as follows:

APA(t) = 110 – 8.1(CC(t) - C(t)) (2)

The overall agreement is also good with the predicted and observed prices very close near the 2008 peak and the 2009 bottom. The recovery since 2009 has been described with a slight underestimation of the measured price, i.e. the actually observed growth was slightly stronger than the predicted one. This undervaluation was quickly compensated by the 2011 fall. Currently, the actual price is undervalued one by a few dollars.

Figure 2. Historic (monthly closing) prices for APA (black line) and the scaled difference between the core CPI and the headline CPI (red line).

Comparing the slopes in (1) and (2) one can estimate relative performance of APA and COP. These slopes define the price reaction to a given change in the CPI difference. For a one unit change in CC-C, the COP price changes by $5.35 and the APA price by $8.1. Since the price levels are approximately $67 and $100, respectively, the ratio of the slopes (0.66) completely corresponds to the ratio of price levels (0.67). This means that the returns provided by COP and APA are equal if their prices are driven by the difference between CPIs.

The original model is very crude. Both CPIs depend on many other goods and services, what introduces high measurement noise in the model. Also, both CPIs have the same weight (1.0) and cannot lead or lag behind the modeled price or each other. Apparently, it can be some non-zero lag between the change in energy price and in prices of energy companies. Therefore, we extended the model and described the evolution of a share price as a weighted sum of two individual consumer price indices (or PPIs) selected from a large set of CPIs borrowed from the Bureau of Labor Statistics. We allow both defining CPIs (PPIs) lead the modeled share price. Additionally, we introduced a linear time trend on top of the intercept. As for many already presented companies, we have tested two principal pairs of CPIs: C and CC; CC and the index of energy, E, as well as the pair the PPI and the producer price index of crude oil, OIL. The best fit (as defined by standard error) model is obtained with the pair PPI and OIL:

APA(t)= 5.83C(t) – 5.81CC(t-0) + 3.81(t-2000) + 28.53; sterr=$9.13 (3)
APA(t)= 2.69CC(t-9) + 0.57E(t) – 8.25(t-2000) - 447.41; sterr=$8.81 (4)
APA(t)= 1.66PPI(t-0) - 0.059OIL(t-9) – 1.25(t-2000) – 175.90; sterr=$7.68 (5)

where APA(t) is the (monthly closing) share price in U.S. dollars. We allowed both time leads in (3) through (5) to vary between 0 and 12 months. Figures 3 through 5 depict the observed and predicted monthly prices from (3) through (5). For an oil company, it is not excluded that oil controls its price. Interestingly, however, that the index of oil drives the price down, i.e. increasing oil price suppresses the return. From Figure 5, we expect the price to rise to $115 in the near future. Oil price may fall in this case.

Figure 3. The observed and predicted monthly closing prices for APA between July 2003 and March 2012. The model is based on C and CC.

Figure 4. The observed and predicted monthly closing prices for APA between July 2003 and
March 2012. The model is based on CC and E.

Figure 5. The observed and predicted monthly closing prices for APA between July 2003 and March 2012. The model is based on PPI and OIL.

Why Peabody Energy is highly undervalued?

In this article we model the evolution of Peabody Energy (NYSE: BTU) share price since 2003 and evaluate its current level relative to that predicted by the model. The main finding can be formulated as follows: the current price is much lower than that predicted by the model. At the same time, the same model has accurately predicted the rally between 2003 and 2007, the sharp fall in 2008 and the following recovery up to the third quarter of 2011.  The same effect is observed for many energy companies, as we have already reported on Seeking Alpha. For example, Newfield Exploration (NFX) and GeoResources (GEOI) demonstrate high-amplitude deviations from their relevant predicted prices but with opposite signs.  Thus, we have been measuring strikingly abnormal deviations in share prices of many energy related companies since the middle of 2011. This observation needs careful analysis and investors’ attention.

Our pricing concept is very simple and we explain it here step by step. First, we put forward a working hypothesis that a share price of an energy company can be driven by the change in the overall energy price or its components. Obviously, energy prices do not exist in vacuum: they affect and are affected by other goods and services. Therefore, we actually assume that the share price is driven by the difference between the energy price and some energy independent price, both can be presented as indices. 

In its simplest form, the model is based on the difference between the headline CPI, C, and the core CPI, CC, without any time lag between these indices and the share price. The headline CPI includes all kinds of energy and thus provides the broadest proxy to the energy price index. The core CPI excludes energy (and food) and thus represents the energy independent and dynamic reference. Historically, these two indices were used in our original models for ConocoPhillips (COP) and Exxon Mobil (XOM) and are retained as a benchmark since then.

The best ever model was obtained for COP. Figure 1 depicts the observed and modeled COP prices in order to demonstrate the predictive power of the pricing concept. The agreement between curves is excellent. One can see that all deviations of the actual price from the predicted one are only short-term and the predicted curve might be considered as a “fundamental” one. In other words, the actual price gravitates to the predicted one. Quantitatively, we have estimated the following relationships to minimize the model error between 1998 and 2012:

COP(t) = 72.3 – 5.35(CC(t) - C(t))  (1)

where COP(t) is the share price in U.S. dollars at time t.  In any case, both curves are close to each other throughout the whole period and there is deviation growing since 2011. 

Figure 1. Historic (monthly closing) prices for COP (black line) and the scaled difference between the core CPI and the headline CPI (red line).

In Figure 2 we present a similar model for BTU. The best fit relationship is as follows:

BTU(t) = 59.5 – 5.55(CC(t) - C(t))  (2)

The overall agreement is also excellent with the predicted and observed prices very close near the 2008 peak and the 2009 bottom.   The recovery since 2009 has been also described accurately to the peak value in April 2011. And then the prices started to deviate, which the predicted price falling at a much slower pace.  In this situation one may consider the currently observed price as an highly undervalued one (between $20 and $25). 

Figure 2. Historic (monthly closing) prices for BTU (black line) and the scaled difference between the core CPI and the headline CPI (red line).   

The original model is very crude. Both CPIs depend on many other goods and services, what introduces high measurement noise in the model. Also, both CPIs have the same weight (1.0) and cannot lead or lag behind the modeled price or each other.   Apparently, it can be some non-zero lag between the change in energy price and in prices of energy companies. Therefore, we extended the model and described the evolution of a share price as a weighted sum of two individual consumer price indices (or PPIs) selected from a large set of CPIs borrowed from the Bureau of Labor Statistics. We allow both defining CPIs (PPIs) lead the modeled share price. Additionally, we introduced a linear time trend on top of the intercept.

So, we continue presenting Peabody Energy Corporation which is engaged in the mining of coal. This is not an oil&gas company and we do not expect it directly depend on oil price. As for many already presented companies, we have tested two principal pairs of CPIs: C and CC; CC and the index of energy, E, as well as the pair the PPI and the producer price index of crude oil, OIL. The best fit (as defined by standard error) model is obtained with the pair CC and E:

BTU(t)= 5.21C(t) – 5.09CC(t-0) – 0.45(t-2000) + 40.84; sterr=$7.85 (3)

BTU(t)= 1.34CC(t-9) + 0.52E(t) – 6.57(t-2000)  - 228.58; sterr=$7.02 (4)

BTU(t)= 1.46PPI(t-0) - 0.0346OIL(t-6) – 4.77(t-2000) – 123.01; sterr=$7.86 (5)

where BTU(t) is the share price in U.S. dollars; the core CPI leads the price by 9 months. We allowed both time leads in (3) through (5) to vary between 0 and 12 months. As one can expect, the index of energy drives the price up. Surprisingly, the core CPI also affects the price positively and the long term time trend in both defining CPIs is compensated by the negative time trend. Figures 3 through 5 depict the observed and predicted monthly prices.  

The best model shows even a better overall agreement than the original model with the standard error of $7.02 between July 2003 and February 2012. As we discussed above, the model residual has been growing since the second half of 2011. Currently, the error is -$23. This is an extremely high residual relative to the “fundamental” price. We believe that the current excursion is just a short-term deviation. Therefore, the BTU price is highly undervalued.

The reader may also suggest that the model has failed on BTU. We cannot exclude this explanation but then why the concept works for the biggest energy companies and has also been working relatively well before August 2011?  

Figure 3. The observed and predicted monthly closing prices for BTU between July 2003 and |March 2012. The model is based on C and CC.

Figure 4. The observed and predicted monthly closing prices for BTU between July 2003 and |March 2012. The model is based on CC and E.

Figure 5. The observed and predicted monthly closing prices for BTU between July 2003 and March 2012. The model is based on PPI and OIL.

Safeway is slightly overvalued

Here we model another company from the S&P 500 list.  This is a company from Services category – Safeway Inc. (NYSE: SWY), which operates as a food and drug retailer in North America. SWY share price is approximated by a linear combination of two consumer price indices; with the CPI of food, F, directly related to SWY and the CPI of owner’s equivalent rent of residence, ORPR, representing an independent but dynamic price reference. We suggest that one CPI moves the SWY price but only relative to the overall prices, which evolve freely of SWY.  Thus, our stock price model tries to find one defining CPI and the best reference.  

We have borrowed the time series of monthly closing prices of SWY from Yahoo.com (March 2012 is included) and the CPI estimates through February 2012 are published by the BLS.  (The CPI estimates for March 2012 will be reported by the BLS in the middle of April and we will revise all models accordingly).  The best-fit model for SWY(t) is as follows:  

SWY(t) =  -1.59F(t-4) + 1.19ORPR(t-0)  + 3.34(t-2000) + 28.85,  March 2012 

where SWY(t) is the SWY share price in U.S. dollars,  t is calendar time. Figure 1 displays the evolution of both defining indices since 2002.  The ORPR index has a positive influece on the price. Apparently, the negative slope of F implies that higher food prices reduce the SWY return. This is not against common sense.  

Figure 2 depicts the high and low monthly prices for a SWY share together with the predicted and measured monthly closing prices (adjusted for dividends and splits). The predicted prices are well within the limits of the share price uncertainty as defined by the monthly high/low prices.  

The model residual error is shown in Figure 3 with the standard error between July 2003 and January 2012 of $1.86. The model predicts that all deviations are only short-term ones, i.e. the measured curve returns to the predicted one, which may be considered as a “fundamental” price level. Basically, the observed share price gravitates to the predicted curve.   

Considering Figures 2 and 3, one can conclude that they current SWY price is slightly overvalued and a negative correction is not excluded in the beginning of the second quarter of 2012. Such corrections have been observed many times in the past.    

Figure 1. The evolution of defining indices. 

Figure 2. Observed and predicted monthly closing prices for a SWY share. 

Figure 3. The model residual error: sterr=$1.86.

Northeast Utilities will stay at $37 per share in Q2 2012

We have presented quite a few pricing models for energy companies (ConocoPhillips, Exxon Mobil, Devon Energy, Chevron, and Chesapeake among many others) because of their clear relation to consumer and producer indices of energy. For different S&P 500 sectors, our pricing models often include indices not related at first glance. Here we present a new pricing model for a company from Utilities category – Northeast Utilities (NYSE: NU), a public utility holding company, which provides electric and natural gas energy delivery services to residential, commercial, and industrial customers in Connecticut, New Hampshire, and western Massachusetts. In part, NU is also linked to energy. But any company, small or big, has a link and is affected by energy prices. The opposite statement is likely wrong – not every company can affect the overall price of energy.  

Before presenting the best fit deterministic pricing model for NU we would like to brief on the underlying pricing concept.  Our approach consists in decomposition of a share price into a weighted sum of two consumer (or producer) price indices. One may test quantitatively our simple assumption that the growth in some specific CPI related to NU (e.g. housekeeping supplies, the BLS name is CUUR0000SEHN and we abbreviate it to HOS) relative to some independent but dynamic reference (e.g. food away from home, CUUS0000SEF or SEFV) should be seen in a higher pricing power for the studied company. In essence, our stock price model tries to find one defining CPI and the best reference. 

In order to find the best pair of CPIs we calculate the RMS residual errors using all permutations from a set of 92 different (not seasonally adjusted) CPIs. In addition we allow for time lag (fixed between 0 and 12 months) between the studied price and both indices, and introduce a linear trend and intercept terms. The best model has the smallest RMS error between July 2003 and February 2012. The complete set includes the headline and core CPI, all major categories from food to other goods and services, and many minor subcategories with long enough history (i.e. continuous estimates should be available since 2001). 

We have borrowed the time series of monthly closing prices of NU from Yahoo.com (March 2012 is included) and the CPI estimates through February 2012 are published by the BLS.  (The CPI estimates for March 2012 will be reported by the BLS in the middle of April and we will revise all models accordingly).  The best-fit model for NU(t) is as follows: 

NU(t) =  -2.57SEVF(t-6) + 0.97HOS(t-7)  + 15.18(t-2000) + 267.79,  March 2012 

where NU(t) is the NU share price in U.S. dollars,  t is calendar time. Figure 1 displays the evolution of both defining indices since 2002.  The housekeeping supplies index has a positive influece on the price, i.e. the growth in HOS results in a higher NU price. Apparently, the negative slope of SEFV implies that higher food prices reduce the NU return. This is not against common sense.   

Figure 2 depicts the high and low monthly prices for a NU share together with the predicted and measured monthly closing prices (adjusted for dividends and splits). 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 error between July 2003 and January 2012 of $1.23. All in all, the model is very accurate and all deviations are promptly annihilated, i.e. the measured curve returns to the predicted one, which leads by six months. One can foresee the share price behaviour six months ahead.  

Considering Figures 2 and 3, one can conclude that the price should rise in the second quarter of 2012 to the level of $37 per share. Since the current level is also $37, one may expect no big change in Q2.   

Figure 1. The evolution of defining indices. 

Figure 2. Observed and predicted monthly closing prices for a NU share. 

Figure 3. The model residual error: sterr=$1.23.

GeoResources is highly overvalued

We have just discussed in our previous article the price evolution of Newfield Exploration Company and found that its current share price is highly undervalued relative to the fundamental price level.  The latter is defined by our pricing model based on share price decomposition into a weighted sum of individual consumer price indices (alternatively PPIs). Here we present an opposite case – GeoResources (NYSE: GEOI), an independent oil and gas company, which engages in the acquisition, re-engineering, development, and exploration of oil and gas reserves in the Southwest, Gulf Coast, and the Williston Basin areas of the United States. This company with capitalization of ~$850M is highly overvalued relative to the predicted (fundamental) price.

Our pricing concept and approach have been discussed many times. For example, articles [1, 2, 3] were devoted to ConocoPhillips (NYSE: COP), which provides an excellent benchmark  and general explanation of the quantitative definition of fundamental price.  The measured COP price is accurately described by a linear combination of two consumer price indices, the core and headline ones. The headline CPI plays the role of that part of the overall energy price which is directly related to the share price.  The agreement is so good that it is difficult to deny that energy (in one form or another) drives the evolution of ConocoPhillips.

The best fit (LSQ) model for GEOI share is the following:

GEOI(t) = -2.59CC(t-0) + 0.14E(t-0) + 11.63(t-2000) + 321.12 ; sterr=$2.87

where GEOI(t) is the share price in U.S. dollars at time t, CC is the core CPI, and E is the consumer price index of energy.   The model standard error is only $2.87 for the period between July 2003 and March 2012. The model also allows both CPIs to lead the share price by 0 to 12 months. However, the best lags are both zero. The index of energy drives the price up, and the core CPI affects the price negatively. Figures 1 and 2 depict the observed and predicted monthly prices and the residual model error, respectively.  

The overall agreement is good but the model residual has several spikes; the most recent has been observed since the second half of 2011. Currently, the error is +$6.5, which is an extremely high residual for GEOI. The same positive residual was observed a year ago. It had returned quickly to the predicted curve and then was followed by a negative residual of approximately the same amplitude. Therefore, the evolution of GEOI price is characterized by very high volatility likely associated with its size; smaller companies are subject to higher risks.  At the same time, the COP model proves that the predicted price might play the role of “fundamental” price. Since GEOI is much smaller than COP, one can consider the current positive excursion as a short-term deviation. Then the GEOI price is highly overvalued.

Thus, we expect the GEOI price to fall to the level of $25 per share in the near future, i.e. we expect GEOI to return to its fundamental price.

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

Figure 2. The model residual error. 

Newfield Exploration Company is highly undervalued

In our previous articles on the Seeking Alpha [1, 2, 3] we demonstrated that the evolution of ConocoPhillips (NYSE: COP) share price can be accurately described by a model based on the share price decomposition into a weighted sum of individual consumer price indices (alternatively PPIs). Both defining CPIs (PPIs) may lag behind the price. This model has been working since 1982 with one structural break around 1998 [2]. This break was related to the change in the long term trend of the difference between the core and headline CPIs [4].

Figure 1 reproduces the observed and predicted COP prices in order to demonstrate the predictive power of the pricing concept. In Figure 2, we depict the model error between 1998 and 2012. One can see that the cumulative model error approached the zero line, i.e. the regression line coincides with the zero line. In other words, all deviations of the actual price from the predicted one are only short-term and the predicted curve might be considered as a “fundamental” one. For ConocoPhillips, we have estimated the following relationships to minimize the model error between 1998 and 2012:

COP(t) = 72.3 – 5.35(CC(t) - C(t))  (1)

where COP(t) is the share price in U.S. dollars at time t, CC(t) is the core CPI, and C(t) is the headline CPI. This relationship is slightly different from that in [2] where we did not minimized the model error.

Figure 1. Historic (monthly closing) prices for COP (black line) and the scaled difference between the core CPI and the headline CPI (red line). 

Figure 2. The model error for COP. The regression line shows that the cumulative error approaches the zero line.

There is a good reason why we discuss the price model for COP over and over. It provides a good benchmark when we estimate quantitative models for different companies.  With the predicted and measured COP prices in Figure 1, it is difficult to deny that energy (in one form or another) drives the evolution of ConocoPhillips. To many readers and/or investors this thought might be a trivial and obvious one. However, the thought that CPI components can drive share prices is not so trivial when we describe the prices of companies from different sectors and industries (e.g. BAC from Financial). The very same approach is treated as an inappropriate one.

In any case, there is no strong criticism of our models for energy related companies we continue presenting them with the case of Newfield Exploration Company (NYSE: NFX),  an independent energy company, engaged in the exploration, development, and production of crude oil, natural gas, and natural gas liquids. As for many other companies, we tested two principal pairs of CPIs: C and CC; CC and the index of energy, E, as well as the pair the PPI and the producer price index of crude oil, OIL. The best fit (we used the LSQ technique) model is obtained with the pair CC and E:

NFX(t) = -4.45CC(t-1) + 0.40E(t-0) + 17.48(t-2000) + 587.71 ; sterr=$7.18  (2)

where NFX(t) is the share price in U.S. dollars. We allowed both time lags (CPI leads the price) in (2) to vary between 0 and 12 months. However, the best lags are one and zero months, respectively. The index of energy drives the price up, and the core CPI affects the price negatively. Figures 3 and 4 depict the observed and predicted monthly prices and the residual model error, respectively.  

The overall agreement is not good when compared to that for ConocoPhillips. Moreover, the model residual has been growing since the second half of 2011. Currently, the error is -$24.4, which is an extremely high residual. Now it’s time to use the COP model as a reference. In line with the interpretation of the COP model error, one might consider the predicted NFX price as a “fundamental” price. Since NFX is much smaller than COP its price is subject to higher fluctuations. Accordingly, the current excursion is just a short-term deviation. Then the NFX price is highly undervalued.

As an alternative explanation, one may suggest that the model has failed on NFX. We cannot exclude this explanation but then why the concept works for the biggest energy companies and has also been working relatively well before August 2011?  Thus, we expect the NFX price to rise to the level of $60 per share in the near future, i.e. to return to its fundamental price. 

Figure 3. The observed and predicted monthly closing prices for NFX between July 2003 and |March 2012.

Figure 4. The model residual error. 

There is another interesting question why do the other two pairs of defining indices have larger residual errors? Obviously, all three oil related indices, i.e. the headline CPI, the index of energy and the producer price index of oil, are tightly linked but are also affected by different goods and services included in the CC and E. The evolution of these defining indices differs accordingly. On the other hand, there is some true set of goods and services which do define the evolution of the NFX price. Then the intercept of this true set and those comprising the CC, E, and OIL should mimic the behavior of the true defining set.   In other words, all three studied indices are just proxy to the true one. As a result, their predictive power may vary with time.