But there's another overarching factor to consider, and that is the effect of central bank liquidity presently gushing into the overall equities market and, specifically, into high-quality stocks such as SIRI. In fact, using the monthly closing stock price of SIRI against the
S&P 500 during a 50-month period, SIRI correlates to the S&P almost perfectly at 0.97. In other words, as the S&P rises, SIRI rises, and visa versa, as the S&P falls, SIRI falls, nearly 100% of the time.
Therefore, in the case of SIRI (few stocks have such a high correlation to the S&P), the outlook for the S&P in the coming four months may be more germane at this time. Given the dominance of daily trading volume from institutions and hedge funds, I'm suggesting that SIRI's financial stability appeals to institutional managers and hedge fund managers alike who gamble on the macro picture for the outlook for stocks as an asset class.
SIRI fits the bill quite well. Also, the stock would be considered relatively cheap on a valuation basis when compared with other high-visibility stocks like
(AMZN), for instance, which trades at a PEG of 14.06 and forward P/E of 138.7.
So, how do I get to a $4.60 price target for SIRI?
Taking the daily closing prices of SIRI, starting from Nov. 22, 2011, to today, a least-squares estimate of 474 closing stock prices was calculated (exhibit A, below). A Standard Error of Estimate was also calculated to delineate an upper and lower band against the least-squares estimate approximation.
Though a least-squares analysis doesn't predict future values, I believe it provides a better analysis of trend than the more popular moving averages offer. Of course, the underlying assumption, other than a surprise from Sirius or a radical change in investor sentiment toward equities, is a continuation of the defined trend in SIRI's stock price.
Least-Squares analysis provides a trend line through the data that equalizes variance between data falling below and rising above the tend line (as opposed to drawing lines through arbitrary data points at perceived lows or highs, or applying moving averages).
Therefore, calculating the Least-Squares estimate line of 474 data points, we get: Y=4.496211546 * 0.001 * X + 1.799407, where "X" is the input for the number of trading days from Nov. 11, 2011. Solving for "Y", the formula returns a projected price of SIRI for that day in the future (applied, below, as a means of clarifying).
The Standard Error of Estimate calculates to 0.191, which means approximately 68.2% of all data points fall between Y+0.191 and Y-0.191. For my analysis, a larger Standard Error of Estimate of 0.37436 (95% of all data falling between Y+0.37436 and Y-0.37436) is applied.
Therefore, 95% Confidence Level: 1.96 X 0.191 = 0.37436
$4.60 Potential by March 31, 2014
So, substituting the number, 539, March 31, 2014 (the 539th day of trading from Nov. 22, 2011) for "X" in the Least-Squares Estimate equation calculates to $4.22, with the Standard Error of Estimate of 0.37436 added to/subtracted from the result of $4.22. Those estimates calculate to an upper limit of a stock price of $4.60 ($4.22+$0.37436) and lower limit of a stock price of $3.86 ($4.22-$0.37436) for Mar. 31, 2014.
At the time of publication, the author held no positions in any of the stocks mentioned, although positions may change at any time.
This article is commentary by an independent contributor, separate from TheStreet's regular news coverage.