ConocoPhillips share price revisited

Since September 16, the readings of the headline CPI and its components for August 2009 are available (we retrieve all CPI data from http://www.bls.gov/data). We recalculate our model for selected stock prices from the S&P 500 list. First is ConocoPhillips since it provides a good example of a company, which share price has been leading defining components of the CPI. In the previous posts and articles [1,2] we introduced several models with varying number of CPI components. Here we compare two models. The first one, we call it 3-C model, is seeking those two CPI components from 34 pre-selected ones, which minimize the difference between observed (monthly closing price adjusted for dividends and splits) and predicted prices for the period between July 2003 and August 2009. This model also includes free term (constant) and linear time term [3-6], which compensates well know linear (time) trends between various CPI components. The second model (5-C) uses four CPI components and free term. The number of potential defining components is reduced to ten: food (F), housing (H), apparel (A), transportations (T), medical care (M), recreation (R), education and communication (EC), other goods and services (O), the headline CPI (C), and the core CPI (CC). These components compile a full set of the CPI expenditure categories, and also comprise a subset of the 34-component set.
The 3-C model for COP is as follows:

COP(t)= 3.41CF(t+1) - 7.17EC(t-6) + 7.22*(t-2000) + 145.76 (1)

where CF is the headline CPI less food. It does not differ from the model presented in the previous post except the time shifts are +1 (was +2) month and -6 (was -5) months now. This is due to the inclusion on the CPI readings for August. Figure 1 shows the predicted and observed curves. Standard deviation between the curves is $3.96 for the period between July 2003 and August 2009.

Figure 1. Observed and 3-C predicted COP’s share prices.

The 5-C model does not include the linear time term, but two components extra to the 3-C model play this role. The best 5-C model also shows that the stock price leads all defining CPI components:

COP(t)= 1.53H(t+3) – 8.29R(t+1) – 4.90EC(t-6) + 3.53C(t+1) + 518.4 (2)

There components from the four defining lag behind COP share price and only the EC leads by six months. Figure 2 presents the model. Standard deviation between the curves is only $3.03 for the period between July 2003 and August 2009, which is better than the accuracy of the 3-C model.
Figure 2. Observed and 5-C predicted COP’s share prices.

At first glance, the 5-C prediction is better that the 3-C one. However, this conclusion is not fully correct. As mentioned above, the extra two components chiefly represent the linear trend. Figure 2 illustrates the phenomenon. The difference between R and EC actually provides a linear trend with small fluctuations, which slightly suppress the residual of the 3-C model. Among all pairs of CPI components -8.29R(t+1) – 4.90EC(t-6) suppresses the model residual (noise) the most efficient. It is a positive side of the 5-C model. It has a negative side as well. The difference may diverge from the linear trend in the next several months and will increase the residual and thus the accuracy of prediction instead of suppression. So, we do prefer using the 3-C model for the prediction of share prices. Figure 4 depicts both residuals.

Figure 3. Two differences between defining components in (2).

Figure 4. Residuals of the 3-C and 5-C models.

We will continue updating both empirical models. The next obvious date is October 1, 2009 with the next closing share price for September.

References
[1] Kitov, I., Kitov, O., (2009). Predicting share price of energy companies: June-September 2009, MPRA Paper 15863, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15863/01/MPRA_paper_15863.pdf
[2] Kitov, I., Kitov, O., (2009). Modelling selected S&P 500 share prices, MPRA Paper 15862, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15862/01/MPRA_paper_15862.pdf
[3] Kitov, I., Kitov, O., (2008). Long-Term Linear Trends In Consumer Price Indices, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ), pp. 101-112.
[4] Kitov, I., (2009). Apples and oranges: relative growth rate of consumer price indices, MPRA Paper 13587, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/13587/01/MPRA_paper_13587.pdf
[5] Kitov, I., Kitov, O., (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15039/01/MPRA_paper_15039.pdf
[6] Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, MPRA Paper 15194, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15194/01/MPRA_paper_15194.pdf

Predicting Microsoft stock price

We continue modelling various stock prices. This post is devoted to Microsoft (MSFT). In my article on the prediction of stock prices for COP and XOM - “Predicting ConocoPhillips and Exxon Mobil stock price”, Journal of Applied Research in Finance, v.2, 2009 (a draft version is available here.), only a very crude model of the stock price evolution was presented. Now a more reliable model is available in the following form:

sp(t)= A1S1(t+t1) + A2S2(t+t2) + A3t +A4

where sp(t) is the stock price at time t, A1 through A4 are empirical coefficients, S1 and S2 are components of headline CPI, t1 and t2 are time difference (negative or positive) between the change in the stock price and relevant changes in the CPI components. Term A3t is introduced to compensate linear trends observed in the difference between the components.

For Microsoft, an accurate empirical model is as follows:

MSFT(t)= -3.61*R(t-1) – 1.22*MSC(t-11) + 23.5*(t-2000) + 680.5.

where t is the calendar time, R(t-1) is the index for recreation leading MSFT(t) by one month, MCS(t-11) is the index for medical service leading the MSFT(t) by eleven months. The linear trend term 23.5*(t-2000) compensates the trend term in the difference between R and MCS. Standard deviation between the curves is $1.75 for the period between July 2003 and August 2009.

Figure 1. Comparison of measured (open circles) and predicted (solid diamonds) stock prices of Microsoft

Unemployment in Germany at 11% in 2011

Original paper:

Kitov, I., (2007). Exact prediction of inflation and unemployment in Germany, MPRA Paper 5088, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/5088.html

On July 1, we presented a model of the evolution of unemployment in Germany developed in [1]. There were new readings of unemployment (from 2005 to 2007) and labor force (from 2004 to 2006) available at the OECD , and we were able to verify the model, to extend it in time, and to predict beyond 2009.
The model stats, that unemployment in developed countries is a function of labor force change [2-5]. There is a general expectation of a good fit between these two variables for Germany. Figure 1 repeats the results of a simple manual trial-and-error process for the period between 1965 and 2007. Since such procedure is based on visual fit only, no statistical estimates of the residual were made.
The resulting relationship between unemployment and labor force in Germany is as follows:

UE(t) = 2.5*dLF(t-5)/LF(t-5) + 0.04, before 1995
UE(t) = 2.5*dLF(t-5)/LF(t-5) + 0.08, after 1995 (1)

where UE(t) is the rate of unemployment in Germany at time t, LF(t-5)is the level of labor force five years earlier. The observed unemployment needs a structural break to be introduced in 1995, which is easily explained by the reunification, i.e. 1990 plus the five-year lag. Free term in (1) underwent a significant increase, but the slope of 2.5 and the time lag of 5 years did not change, however. (It should be noted that the slope depends of definition of unemployment and labor force, i.e. on reporting agency.) One can read (1) in the following way – a 1% increase in labor force in Germany results in 2.5 % increase in unemployment five years later. When the labor force does not change, the level of unemployment is constant at 8%.

An important finding here is that unemployment in Germany increases with increasing rate of labor force growth. So, the remedy against high unemployment in Germany consists in the (likely artificial) reduction of labor force growth. The estimates of unemployment are provided by the OECD and cover the period till 2007. In 2008, the unemployment had to reach the bottom around 8% (notice that this prediction was made in 2004!). In 2009, the rate of unemployment in Germany should be increasing due to the increase in labor force five years ago, not due to the crisis. In 2010, it will reach its peak value at 11% and then will start to decline into the 2010s.

Figure 1. Observed and predicted rate of unemployment in Germany.

In order to test our model, we have borrowed unemployment estimates from the DEStatics (Federal Statistics Office). They are slightly different from those provided by the OECD, and are issued at monthly frequency. Figure 2 presents the measured and predicted unemployment rate for the period between 2000 and 2012. The predicted curve has slightly higher coefficient than that obtained from the OECD data (+3.2):

UE(t) = 3.2*dLF(t-5)/LF(t-5) + 0.08 (2),

but the time shift is the same – five years. All in all, both curves are very close. Therefore, our prediction of 11% rate in 2011 is a reliable one. Germany will suffer another surge in unemployment rate during the next 18 months. In 2010 the rate will reach 9%.

Figure 2. Observed and predicted rate of unemployment in Germany - likely to reach 11% in 2011.


References

[1] Kitov, I., (2007). Exact prediction of inflation and unemployment in Germany, MPRA Paper 5088, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/5088.html

[2] Kitov, I., Kitov, O., Dolinskaya, S., (2007). Inflation as a function of labor force change rate: cointegration test for the USA, MPRA Paper 2734, University Library of Munich, Germany, ideas.repec.org/p/pra/mprapa/2734.html

[3] Kitov, I., (2007). Inflation, Unemployment, Labor Force Change in European countries, in T. Nagakawa (Ed.), Business Fluctuations and Cycles, pp. 67-112, Hauppauge NY: Nova Science Publishers

[4] Kitov, I., (2007). Exact prediction of inflation and unemployment in Canada, MPRA Paper 5015, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/5015.html

[5] Kitov, I., Kitov, O., (2009). Unemployment and inflation in Western Europe: solution by the boundary element method, MPRA Paper 14341, University Library of Munich, Germany

Krugman, Cochrane, Altig ...

There is an active discussion of the future of economics profession. Where should we ( e.g. the broader scientific community) seek the ideas and tools, which could save economics as an emergent science?

Some mainstream economists like Krugman, Cochrane, Altig and others again discuss these problems in light of which school of economic thought should prevail. I guess that this discussion will stretch straight into the next failure to predict significant economic phenomenon; like deflation in the USA starting in 2012. So, despite its activity the discussion seems to be a hopeless one.

We deserve a better treat. Therefore, the scientific community must formulate clear questions and set some threshold requirements for economics as a science to be allowed to shape and control economic and social life. In a sense, we are all customers of economic theory because it ifluences in one way or another the decisions made by economic and financial authorities. As the customers we should ask the economic profession to formulate a new research plan (not to argue which school is wrong). This plan has to define clear (for general public and experts in various fields) ideas and tools which are necessary to answer the question why the theory has failed to describe 2007-2010, and when it expects a new unpredictable change likely to happen. Meanwhile, it would be helpful for economists to regain public trust. This current discussion on the difference between various (failed) approaches does not look like helpful. If they follow the route of the negation of the presence of educated audience waiting for reasonable answers, they will completely detach themselves from the scientific community and general public as well.

PPI of copper ores and grains: update. July and August 2009

Lately, we have demonstrated that the evolution of various components of CPI and PPI in the United States is not a random process but rather a predetermined one with long-term sustainable trends [1-4]. Using these trends, one can predict consumer and producer price indices for various goods, services and commodities [5-7]. Moreover, share prices for selected S&P 500 companies are also well described in the past by the differences in the PPI and CPI [8,9]. The near future will test the predictive power of our model.
In [4-7], we presented the evolution many commodities with varying weight in the PPI. But there are many more commodities of interest for producers, consumers and investors. In this article, we present the producer price indices of copper and grain. The evolution of the indices of these two commodities is independent, but both give a good example of the absence of clear sustainable trends. In other words, not every commodity price is predictable as an extension of a liner trend, as mentioned in [4]. However, in the short-term, there is observed some inertia in the evolution of both prices. Therefore, one can predict the indices at a several month horizon. On July 27, we published selected results of our study for June 2009. This is a bi-monthly update, which includes two new readings for July and August 2009. Both commodities demonstrate continuation of short-term trends. Supposedly, these trends survive till the end of 2009.
Figure 1 displays the overall (commodity) PPI and the index of copper ores since 1988. The difference of these two indices has a remarkable history; no big change between 1988 and 2005, and then a sudden giant jump in the copper index. Fluctuations between 350 and 500 points lasted three years, and then the index dropped by ~300 units back to the PPI level. Despite the early start in 2005 the growth seems to be oil independent, the most recent fall looks to be driven by oil price and the overall economic slowdown. In July and August 2009, one can observe a sustainable increase in the cooper index likely associated with the rise in oil price. In the long-run, oil price should decline to the level of ~$25 in 2016. Therefore, copper price will likely not be growing to its peak in April 2008 (491.7), but will likely return to heights around 350. (Our model based on the presence of sustainable trends in the difference between the PPI and individual PPI is not applicable to copper ores.)

Figure 1. Evolution of the price index of copper ores and the PPI.

The producer price index for grains presents another difficult case. Figure 2 depicts the PPI and the index, and their difference between 1960 and 2009. There is no sustainable trend in the difference. Between 1974 and 2005, the difference demonstrated an overall growth with several spikes, the strongest one in 1996. The presence of a long-term positive trend in the difference is completely due to the growth in the PPI because the index of grains fluctuates around a constant level just slightly above 100 points. Lately, the grains index suffered the biggest rise and fall in absolute terms. The main increase started earlier in 2007 and stretched into 2008, with the peak in June 2008. Volatility of the difference during the past two years was so high that it is difficult to predict the next move of the price index of grains at a several year horizon. Seemingly, it has been repeating the trajectory of the index for crude oil in 2008 and 2009. If it is the case, one can expect that the index for grains will continue to oscillate around the constant level of ~100.

It is instructive to compare two major spikes in the grains index in 1996 and 2008 relative to the PPI. In order to avoid comparing absolute values, which undergo secular growth, the evolution of the difference between the PPI and the price index of grains normalized to the PPI. Figure 3 presents the normalized curves. The left panel shows that the spike in the grains PPI in July 1996 is similar in relative terms to that observed in 2008. The right panel tests this hypothesis: the spikes are synchronized - for the black line is shifted forward by 142 months. From this comparison, it is likely that decline in the grains index relative to the PPI will extend into the 2010s.

Figure 2. Evolution of the price index of grains and the PPI.

Figure 3. Evolution of the difference between the PPI and the price index of grains normalized to the PPI. Left panel shows that the spike in the grains PPI in July 1996 is similar to that observed in 2009. Right panel tests this hypothesis – the spikes are synchronized (time shift by 142 months). One might expect a decline in the grains index relative to the PPI will be extended into the 2010s.

Conclusion

In the short-run, the index for copper will be growing at least till the end of 2009. The index for grains will continue its decline relative to the PPI. As a consequence, one can expect that the index for food will be also decreasing and this decline will stretch into the 2010s.

In the long run, the producer price index for copper and that of grains both demonstrate practically unpredictable behavior with unclear future. This observation only emphasizes the importance of sustainable trends observed for other commodities. In the US economy, as in many natural systems, there exist trend components, oscillating components, and random components.

References
1. Kitov, I., Kitov, O., (2008). Long-Term Linear Trends In Consumer Price Indices, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ), pp. 101-112.
2. Kitov, I., (2009). Apples and oranges: relative growth rate of consumer price indices, MPRA Paper 13587, University Library of Munich, Germany.
3. Kitov, I., Kitov, O., (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany,
4. Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, Journal of Applied Research in Finance, v. 1, (in press)
5. Kitov, I., Kitov, O., (2009). PPI of durable and nondurable goods: 1985-2016, MPRA Paper 15874, University Library of Munich, Germany
6. Kitov, I., (2009). Predicting gold ores price, MPRA Paper 15873, University Library of Munich, Germany
7. Kitov, I., (2009). Predicting the price index for jewelry and jewelry products: 2009-2016, MPRA Paper 15875, University Library of Munich, Germany
8. Kitov, I., Kitov, O., (2009). Predicting share price of energy companies: June-September 2009, MPRA Paper 15863, University Library of Munich, Germany
9. Kitov, I., Kitov, O., (2009). Modelling selected S&P 500 share prices, MPRA Paper 15862, University Library of Munich, Germany

Predicting ConocoPhillips and ExxonMobil stock price

Lately, I have published an article on the prediction of stock prices for COP and XOM - “Predicting ConocoPhillips and Exxon Mobil stock price”, Journal of Applied Research in Finance, v.2, 2009. (A draft version is available here.)

Abstract
Exxon Mobil and ConocoPhillips stock price has been predicted using the difference between core and headline CPI in the United States. Linear trends in the CPI difference allow accurate prediction of the prices at a five to ten-year horizon.
Key words: stock price, Exxon Mobil, ConocoPhillips, prediction, CPI
JEL classification: G1, E3

We continue modelling various stock prices and recently revisited XOM and COP. The general model can be rewritten in the following form:

sp(t)= A1S1(t+t1) + A2S2(t+t2) + A3t +A4

where sp(t) is the stock price at time t, A1 through A4 are empirical coefficients, S1 and S2 are components of headline CPI, t1 and t2 are time difference (negative or positive) between the change in the stock price and relevant changes in the CPI components. Term A3t is introduced to compensate linear trends observed in the difference between the components.

For ConocoPhillips and Exxon Mobil, more accurate empirical models are as follows:

Figure 1. Comparison of measured (open circles) and predicted (solid diamonds) stock prices of ConocoPhillips. The prediction is obtained from the following empirical relationship:

COP(t)= 3.41CF(t+2) - 7.17EC(t-5) + 7.22*(t-2000) + 145.76,

where CF(t+2) is the headline CPI less food lagged by one months behind COP(t), and EC(t-5) is the index for education and communication leading the COP(t) by five months. The explicit trend term 7.22*(t-2000) compensates the trend term in the difference between CF and EC. Standard deviation between the curves is $4.0 for the period between July 2003 and August 2009.

Figure 2. Comparison of measured (open circles) and predicted (solid diamonds) stock prices of ExxonMobil. The prediction is obtained from the following empirical relationship:

removed by author

Standard deviation between the curves is $3.27 for the period between July 2003 and August 2009.

These two models provide a good approximation of the price evolution and even predict the future price for XOM.

Crude oil at $100 in December: August update

In March 2009, we presented a prediction of crude petroleum price for 2009. Briefly, our analysis has shown that oil will overcome $100 per barrel before the end of 2009. As promised, we evaluate this prediction every month, when new readings of producer price index (PPI) with all its components become available. In this article, we report the results for August 2009.

The period between January 2008 and likely the end of 2010 is characterized by an elevated volatility in oil price, but the evolution of the price is not random. Moreover, even after the start of crisis in 2008, the price has been following a predetermined trajectory, as it has been demonstrating since 1980.

In August, the PPI reached the level of 175.1 from 172.7 in July, and the producer price index of crude petroleum (domestic production) increased to 190 after 158.9 in July. Our assumption on the average monthly increment for the crude petroleum index was +20, and for the PPI +1. So, the increase in August was larger than predicted. However, this increase just compensated the fall in the difference observed in July relative to June.

Figure 1 presents the new readings. Overall, the evolution of the difference between the PPI and the index for crude petroleum follows the predetermined path (compare the updated curve to that drawn in June) – from its peak in February 2009 to the bottom of a through, which will likely be reached by December 2009. This is a natural path for a pendulum, as discussed in the previous articles [1], [2]. Hence, there is not sign that the oil price significantly deviates from the predicted trajectory. We expect the price to hit the new (red) trend line in September 2009. This is the level of $73-77 per barrel.

Figure 2 depicts the evolution of crude oil price. Values for the period between September and December 2009 are shown by solid red circles. According to the prediction, the price should break the $100 level before the end of 2009. The next update is expected in October or November 2009.

Figure 1. Evolution of the difference between the PPI and the index for crude petroleum (domestic production). Solid circles – the readings between March and August 2009, which were anticipated in February 2009. Open circles – the predicted difference between September and December 2009. Upper panel – figure from June. Lower panel – August update.

Figure 2. The evolution of crude oil price. Red circles – oil price predicted for the period between July and December 2009. According to the prediction, the price should break the $100 level before the end of 2009.