Modeling stock prices: Analog Devices has potential for further growth

 In this blog, we present and track successful quantitative models from the S&P 500 list. They are numerous. We revisit (recalculate) all models using new data and report on successful models. In some cases, a model should hold for a year before we publish it. Our stock pricing concept is very simple as based on deterministic links between share prices and prices of goods and services included in the consumer price index, CPI. Literally, we decompose a share price (monthly closing price adjusted for splits and dividends) into a weighted sum of two individual CPI components, linear time trend component and constant free term. We allow positive and negative time lags between all variables in the relationship and seek to minimize the RMS model error by varying the involved coefficients. The set of CPI components consists of 92 independent price indices of different level: from major (overall and core CPI) to very small (e.g. photo and related materials). When the modeled share lags behind both defining CPI components we have a deterministic model predicting at a horizon of the smallest time lag. This concept gives excellent results in terms of the model error and very stable pricing models which are valid during several years. In 2008, the model successfully predicted bankruptcy of some major banks, including Lehman Brothers. Fannie May and Freddie Mac. We were able to forecast negative share prices several months before the crash [1].  One can also find a formal model description in our monograph.  
 In this post, we present a share pricing model for Analog Devices Inc. (NYCE: ADI). It belongs to Information Technology sector and is specialized in analog and digital signal processing integrated circuits.  Here we present a model obtained in March 2014 and covering the period since July 2003.  This model includes the index of motor vehicle maintenance and repair (MVR) and the index of communication (CO).  The latter index makes some sense as directly related to communications. The MVR index does not lead the ADI share price and the CO index leads by 10 months.  Figure 1 depicts the evolution of the indices which provide the best fit model, i.e. the lowermost RMS residual error, between October 2013 and March 2014.  The model is as follows: 

ADI(t) = -1.74MVR(t-0) + 1.76CO(t-10) +14.41(t-2000) + 160.74      

where ADI(t) is a share price in US dollars, t is calendar time.  

The predicted and observed monthly closing prices are depicted in Figure 2 together with the high and low monthly price representing the price uncertainty. The residual error is of $2.55 for the period between July 2003 and March 2014 (see Figure 3 for details).  The dependence on time (linear time term) has been strong enough ($14.4 per year) to overcome negative influence of both indices since 2009: increasing MVR with a negative coefficient and decreasing CO with a positive coefficient both lower ADI price. Figures 1 and 2 suggest that ADI share has a potential for further growth if CO and MVR will retain their long term trends.

Figure 1. Evolution of MVR and CO. 

Figure 2. Observed and predicted ADI share prices.  

Figure 3. Model error for ADI

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