Courtesy of ZeroHedge. View original post here.
Submitted by Tyler Durden.
While anecdotally we see again and again that equities rally on bad news (the Fed will save us) and good news (see the Fed saved us), none of that matters until it gets the Goldman Sachs stamp of approval.
Sure enough, in a detailed study over the weekend, designed to defend their bullish equity view (specifically financials) and expectations for QE3 to continue to Q3 2014, the bank that does God's work offered up these pearls of statistically sound wisdom: "while equity prices respond more to dovish surprises than hawkish surprises, the results suggest that equity prices typically go up regardless of whether the Fed policy surprise is positive or negative (“good news is good for equities, and bad news is good for equities”). But it is not at all clear why the equity market should systematically buy into this pattern." So rest assured, buying wins; of course, that is, until it doesn't.
Via Goldman Sachs,
… Specifically, we find that a 25bp surprise [or QE implied equivalent] is usually associated with a 1% change in equity prices on the same day.
…
Taken literally, these results suggest that equity prices typically go up regardless of whether the Fed policy surprise is positive or negative (“good news is good for equities, and bad news is good for equities”).
Asymmetric Fed communication might help explain some of this pattern. When policy is tightened, Fed commentary tends to be bullish on the outlook and thus helps prevent equities from selling off. But when the funds rate is cut, Fed commentary is not correspondingly downbeat and so equities rally a lot. This asymmetry in Fed commentary, in itself, is not surprising as Fed officials are usually intent on emphasizing dovish policy changes (to help stabilize the economy) and downplaying hawkish ones (to avoid destabilizing markets).
But it is not at all clear why the equity market should systematically buy into this pattern.
An alternative explanation is that the asymmetry in the equity price effect is simply a statistical mirage due to a small sample that is driven by outliers.
