Here we revisit our pricing model for a company from the Utilities category - Northeast Utilities (NYSE: NU), a public utility holding company, which provides electric and natural gas energy delivery services. In April 2012, we concluded that the price should rise in the second quarter of 2012 to the level of $37 per share, with the relevant standard error of $1.23. The closing monthly price (adjusted for splits and dividends) in June was $38.46. It increased to $39.3 in October 2012. The updated model indicates that the price has to decrease by approximately $2 in the next few months. Otherwise, the price has to be relatively stable over the next seven months, i.e. during the natural forecasting period. One may consider NU as a reliable company for investments but wait before buying at $37.

Before presenting the best fit deterministic pricing model for NU we would like to brief on the underlying pricing concept. Our approach consists on 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, HOS) relative to some independent but dynamic reference (e.g. food away from home, 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 lags (fixed between 0 and 11 months) between the studied price and both indices, and introduce a linear time trend and intercept terms. The best model has the smallest RMS error between July 2003 and October 2012. The complete set includes the headline and core CPI, all major categories from "food and beverages" to "other goods and services," and many minor subcategories with a long enough history (i.e. continuous estimates should be available since 2001).

We have borrowed the time series of monthly closing prices from Yahoo.com (October 2012 is included) and the CPI estimates through October 2012 are published by the BLS. The best-fit models for *NU(t)* in March and October are as follows:

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

*NU(t) =* -2.60SEVF*(t-7) + 0.98HOS(t-8) +* 15.25*(t-*2000*) +* 268.06, October 2012

Where *NU(t)* is the NU share price in U.S. dollars, *t* is calendar time. The changes in the model from March to October are just minor with the same principal CPIs, very close time lags, and slightly changed coefficients. One can conclude that the data for the past seven months have validated the original model.

Figure 1 displays the evolution of both defining indices since 2002. The housekeeping supplies index has a positive influence 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. The high/low prices put natural limits on the variation of the monthly price. The predicted prices are well within the high/low share price curves. The model residual error is shown in Figure 3, with the standard error between July 2003 and October 2012 of $1.20. All in all, the model is very accurate and all deviations are promptly recovered, i.e. the measured curve returns to the predicted one. One can foresee the share price behavior seven months ahead, as shown by solid red line representing a contemporary prediction of the price.

Considering Figure 2, one can conclude that the price should fall in the fourth quarter of 2012 to the level of $37 per share. It will likely stay at that level in 2013Q1.

Figure 1. The evolution of defining indices.

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

Figure 3. The model standard error $1.20.

**Disclosure: **I am long SPY. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.