The Wall Street Journal's Moneybeat column last week mulled the relative value of European versus US equities. We took a special interest in that idea - in fact we intend to publish the results of our research in issue #2 of our premium newsletter later this month (see below for details of launch issue #1).
Let us say for the moment that the article's observation that:
"international stocks have underperformed the U.S. for what seems like forever"
has particular context in the case of Europe. "Forever" dates, in fact, to June 2014 when the European Central Bank took the bucket to the negative interest rate well.
Does that impact the judgment that they are relatively speaking good value? Stay tuned.
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Our inaugural edition is released Monday 6 June and considers a pairs idea likely to benefit from whoeverwins the White House in November. And by "whoever" we mean between Mr. Trump and Mrs. Clinton.
There's a detailed analysis of the pair (trade history and fundamentals) as well as downloadable ArbMaker files with the tailored strategy and set-up parameters used. This permits examination of not only the trade thesis but also allows the reverse engineering/deconstruction of the trade idea itself. Most important, ArbMaker users can follow the evolution of the pair over time.
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Academics and traders use statistical principles or concepts to study relationships between variables to make profitable trading decisions. Studies of bivariate relationships in finance and economics are dominated by the statistical concepts of correlation and cointegration.
The statistical tools can get very technical.
However, keep the following in mind: cointegration is regarded as superior to correlation in the modelling and in the trading of bivariate time series. This is because:
Finance or market data are usually nonstationary and:
Data and information are lost when they are differenced to achieve stationarity
Cointegrated time series are potentially mean-reverting, i.e., they may return to a mean or an equilibrium; this is unlike even highly correlated but not cointegrated series that move together but may not revert to a mean or an equilibrium
Cointegration, relative to correlation, more clearly identifies a causal direction in a linear relationship between potentially tradable pairs
Note, too, that the stat alone is generally insufficient over the long-term. It's a great tool for pair discovery. Validating the viability of those pairs for trading purposes is a further process.