All of the data we produce originates from a single algorithm which for reasons of simplicity, we call “the algo”. Before we go into the specifics of what makes our approach so unique, it’s worth clarifying our hypothesis on why the algo works.
A financial market is a complex system containing a myriad of variables and inter-relationships. To make sense of this system, we have adopted a first principles approach; relentlessly questioning the foundations of financial markets. If we boil down the complexity to its fundamental core, markets are simply price discovery mechanisms reflecting underlying economic conditions. Understand those economic conditions and you can uncover the path of least resistance to alpha generation. Think of Macrowonk Indicators like a force multiplier for your trading and investment decisions.
Instead of focusing on price or company fundamentals, we look to the economy for signal. Fundamentally, a stock market is just an index consisting of companies whose balance sheets depend upon economic health. Of course there are other variables that are reflected in stock market prices (geopolitical events for instance), however, we argue that over the long term the most critical driver of returns is economic health. The Macrowonk algo is designed to filter out the noise inherent in macro-economic data. The resultant Indicators deliver a signal with strong predictive power across multiple countries and asset classes.
How are the Indicators calculated?
The algo calculates a numerical score for a large sample of published economic data (everything from vehicles sales to high impact employment releases). These scores then get aggregated into the indicators that we publish. The aggregation of scores is intended to give a more representative picture than any one score would in isolation. Obviously the exact formula we use to derive the scores and indicators is proprietary. Essentially though, the algo calculates approximately 30 variables for each economic indicator using nonparametric statistical methods in addition to proprietary formulas. These variables are then combined into a single score based on weighting factors. Using these scores allows us to quantify the movements in economic indicators, something that is lacking in the majority of economic commentary and analysis.
In any given day, there are approximately 50 economic data releases across the markets that we track. The algo runs multiple times per day, performing over 3000 calculations to compute the scores that underpin our indicators. All of this number crunching is distilled into a simple series of Indicators for each country we cover; United States, Europe, United Kingdom, Japan, Canada, China and Australia.
Our aim is to separate the noise and deliver at-a-glance Indicators that give you the perspective you need to trade.
Macrowonk Indicators are superior to publicly available economic data
The scatter plot above compares the Macrowonk US Economic Index and the US Nonfarm Payrolls from January 2004 – December 2016. Both are plotted against the S&P500 stock index (x axis) to derive a coefficient of determination (r²). The r² for Nonfarm Payrolls data is 0.76, however our US Employment Indicator does better at 0.87. As a client of Macrowonk, this is your edge over your fellow market participants. Our US Employment Indicator has a tighter distribution and less outliers than the highly anticipated Nonfarm Payroll figure. In summary; Less Noise, More Signal.