ConocoPhillips price revisited

Lately, we have developed and tested a concept of stock pricing as based on the dependence on consumer price index [1]. The model was originally introduced by Kitov and Kitov [1,2] and then applied to Exxon Mobile (XOM) and ConocoPhillips (COP) [4]. It is instructive to track the performance of the model and compare observed and predicted prices.

Since March 18, the readings of the headline CPI and its components for February 2010 are available (we retrieve all CPI data from http://www.bls.gov/data). Here we update our model [1] for ConocoPhillips (COP), as one of selected stocks from the S&P 500 list.

ConocoPhillips provides a good example of a company, which share price has been lagging behind defining components of the CPI. The model is seeking those two CPI components from 92 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 February 2010. The original model [1] included only nine top CPI subcategories and that obtained in [1] - 34 different CPI indices. Currently, the set of CPI components is extended to 92. This is not the final set, however.

The two-component (2-C) model also includes free term (constant) and linear time term [5-8], which compensates well know linear (time) trends between various CPI components. The best-fit 2-C model for COP(t) is as follows:

COP(t)= 2.792MCS(t-3) – 4.477PETS(t-2) - 10.964(t-2000) – 267.54

where MCS in the index of medical care services (CUUR0000SAM2) leading the stock price by 3 months, PETS is the index of pets and pet products (CUUR0000SERB) leading by 2 months, (t-2000) is the elapsed time. Therefore, the predicted curve leads the observed price by 2 (!) months, i.e. contemporary readings of relevant CPI subcategories allow the prediction at a 2-month horizon. Figure 1 depicts the observed and predicted prices, the latter shifted two months back for synchronization. Figure 2 presents the residual error, with standard deviation of $3.78 for the period between July 2003 and February 2010.

The model predicts the price to grow in March and April 2010 to the level of $55 and $60.7, respectively. We will revisit this prediction in May 2010.

Figure 1. Observed and predicted share prices.


Figure 2. Residual error of the model, σ=$3.78 for the period between July 2003 and February 2010.

References
[1] Kitov, I. (2010). Deterministic mechanics of pricing. Saarbrucken, Germany, LAP Academic Publishing.
[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., (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
[4] Kitov, I., (2009). Predicting ConocoPhillips and Exxon Mobil stock price, Journal of Applied Research in Finance, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. I(2(2)_ Wint), pp. 129-134.
[5] 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.
[6] 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
[7] 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
[8] 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 REAL ECONOMIC GROWTH IN FRANCE, GERMANY, NEW ZEALAND, AND THE UNITED KINGDOM

Journal of Applied Economic Sciences (JAES) has published the spring issue with my paper:

Ivan O. KITOV, 2010. "Predicting Real Economic Growth In France, Germany, New Zealand, And The United Kingdom," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(1(11)_Spr), pages 48-54.


Abstract
The growth rate of real GDP per capita is modeled and predicted at various time horizons for France, Germany, New Zealand, and the United Kingdom. The rate of growth is represented by a sum of two components – a gradually decreasing trend and fluctuations related to the change in country-specific age population. The trend is an inverse function of real GDP per capita with constant numerator. Previously, similar models were developed and validated for the USA and Japan.

Keywords: real GDP per capita, modeling, prediction, population

JEL classification: E1, E3, O4, O5

Deterministic mechanics of pricing

I've written a book on inflation and deterministic character of pricing of goods, services and stocks. It has been published by LAMBERT Academic Publishing. (I strongly recommend to publish monographs with LAP.)

The book is available via http://www.amazon.de/: "Determinisic mechanics of pricing" .

Abstract

The book presents a deterministic description of future prices of stocks, goods and services and commodities. Statistically, observed and predicted prices are cointegrated. The overall price inflation is a linear and lagged function of the growth rate of labor force, with projections foreseeing a deflationary period since 2012. There are long-term sustainable trends in the differences between various CPI and PPI subcategories. A deterministic link has been found between stock prices and CPI. To validate the link, empirical models for fifty four S&P 500 companies are presented, with statistically robust price predictions months ahead. One can compile a dynamic portfolio with a deterministic profit. In July 2008, the model would have accurately forecasted negative share prices of Lehman Brothers and AIG. The predictions are likely reliable until their influence on the stock market is negligible. Finally, we validate the link between the S&P 500 returns, real GDP per capita and the number of 9-year-olds in the United States.

Unemployment fell by 0.3%

As predicted several hours ago, the unemployment has decreased by 0.3% to 9.7%.
This was not expected by the market with the average forecast of 10% and the range from 9.9% to 10.2%. The decrease will accelerate in February. The reason behind the drop is the secular decline in the rate of participation in labor force, as decribed in this post.

Unemployment in the USA on a rapid decline

We would like to present one simple figure before the announcement of the employment situation in the USA. This is the link between unemployment and inflation. In the short run, they compensate each other, i.e. a large increase in the unemployment is compensated by a decline in the inflation (with CPI as a measure of inflation). Figure demonstrates that the inflation leads by six months and has been picking since August 2009. As a result, the unemployment in January or February will start to decline as well. By June 2010, the unemployment will drop to the level of 6% to 7%.

Dramatic decline in labor force participation

Michael Mandel at “Economics Unbound” is really interested in labor force participation rate (LFPR). He devoted couple previous posts to related problems in an attempt to explain the evolution of labor force participation rate and productivity by some modern and fancy reasons. It is not worth to repeat his posts here and I just refer to Figure 1 as a general argument against any short-term force driving LFPR. The overall trajectory has a very clear picture of secular oscillations. Between 1965 and 2000, the LFPR was growing with just minor plateaus near 1980m and 1990. After 2000, the LFPR has been declining. This is a robust downward trend which hardly to be compensated by innovations, as Michael suggests.

In 2009, the LFPR has decreased from 66% to 65.4% in Q3 with average over the three quarters of 65.6%. A 0.6% drop in LFPR is a dramatic one. It corresponds to ~2,000,000 people leaving labor force in the US almost at once! (It is worth noting that such a drop may severely affect the rate of unemployment because people without job are more likely to leave labor force). According to our model [1], this the decline in the LFPR was expected in 2010. However, the population estimates, which are used for the prediction, have never been accurate enough for sharp timing. In any case, the model developed in [1-3], which links LFPR and productivity in developed countries to real GDP per capita has proved its consistency. The next two to three years should serve for further validation, as Figure 2 assumes.

Figure 1. The evolution of LFPR between 1960 and 2009.

Figure 2. The observed LFPR and that predicted from real GDP per capita. We expect the LFPR to fall down to 64.5% by 2013.


The observed increase in productivity is directly related to the decrease in the LFPR. As a consequence, it was also well predicted by our model in [2]. We used the projection of the number of 9-year-olds from the number of 1-year-olds for the prediction of real GDP per capita in the 2010s. Since 2010, the productivity has to be growing, as Figure 5 in [2], demonstrates.

a)

b)

Figure 5. Prediction of the number of 9-year-olds by extrapolation of population estimates for younger ages (1- and 6-year-olds).
a) Total population estimates. The time series for younger ages are shifted ahead by 8 and 3 years, respectively.
b) Change rate of the population estimates, which is proportional to the growth rate of real GDP per capita. Notice the difference in the change rate provided by 1-year-olds and 6-year-olds for the period between 2003 and 2010. This discrepancy is related to the age-dependent difference in population revisions.
A downward trend in productivity, as has been observed since 2003, will turn to an upward one in the 2010s. This also means an elevated growth rate of real GDP per capita during the period between 2010 and 2017.


References

[1] Kitov, I., Kitov, O., (2008). The Driving Force of Labor Force Participation in Developed Countries, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. III(3(5)_Fall), pp. 203-222. http://www.jaes.reprograph.ro/articles/3_TheDrivingForceofLaborForceParticipationinDevelopedCountries.pdf

[2] Kitov, I., Kitov, O., (2008). The driving force of labor productivity, MPRA Paper 9069, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/9069.html
http://mpra.ub.uni-muenchen.de/9069/01/MPRA_paper_9069.pdf

[3] Kitov, I., Kitov, O., (2009). Modelling and predicting labor force productivity, MPRA Paper 15152, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15152/01/MPRA_paper_15152.pdf

Unemployment in Japan: now falling to the long-term level around 5%

In June and August 2009, we posted on the evolution of unemployment in Japan [P1] and [P2], which predicted the rate of 6.0% in August 2009. In the latter post we had to update the prediction and reduce the expected level of unemployment, as dictated by new readings of labor force. Moreover, we found that the level of unemployment in July was likely the peak value and the rate of unemployment in Japan would be decreasing since August 2009 according to the long term forecast (Kitov; 2006, 2007).

On the 2nd of October, the reading for August 2009 was reported by the Statistics Bureau of Japan. The (seasonally adjusted) rate for August was measured at 5.4%, i.e. 0.3% lower than in July. This observed decline is in line with that based on the model linking the rate of unemployment, UE(t), to the rate of change of labor force, dLF(t)/LF(t):

UE(t)= -1.5*dLF(t)/LF(t) +0.045 (1)

Figure 1 updates the observed and predicted curves for 2009. Because the accuracy of short-term estimates provided by labor surveys is not high, the monthly estimates of unemployment and labor force are prone to large measurement errors. The discrepancy between the observed and predicted curves likely manifests the problems with measurements. Figure 2 presents a mid-term view. In 2007 and 2008, the predicted unemployment was lower that the observed one, but in 2009 both variables are essentially the same (see Figure 1). In the long-run, the rate of unemployment in Japan will asymptotically approach 5.2% (Kitov, 2007), as displayed in Figure 3.

From relationship (1), we can conclude that the rate of unemployment in 2009 will likely undergo additional decline. The peak rate of unemployment is left behind, but its further decrease will accompany the decline in the level of labor force.

References

Kitov, I., (2006). The Japanese economy, MPRA Paper 2737, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/2737.html
Kitov, I., (2007). Exact prediction of inflation and unemployment in Japan, MPRA Paper 5464, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/5464.html

Figure 1. Observed and predicted rate of unemployment in Japan in 2009.

Figure 2. Observed and predicted rate of unemployment in Japan between 1998 and 2008.

Figure 3. Prediction of the evolution of unemployment rate in Japan between 1990 and 2050 (Kitov, 2007).
Notice excellent prediction between 1998 and 2007 with the peak value in 2001.