Do macro indicators add value? Whether your analysis is driven by technicals or fundamentals, the key question should be the same: Does this information provide me with an edge? How stable is it? And how can I make use of it in the real world? In order to answer these questions, we need a way to backtest macroeconomic indicators. This article will show a simple example of how to use US employment data and back test it on the S&P 500.
Initial Jobless Claims – a good Recession Predictor
For our example here, I picked a well-known macro data series – the US initial jobless claims. This indicator is released weekly and thus gives analysts a timely information on the health of the job market: In short, the lower the number, the better the job market situation. The higher the number, the higher the probability of a recession. The following figure shows the long-term chart of initial jobless claims. Recessions in the USA are represented by grey shaded areas. All recessions have been preceded by rising initial jobless claims.
…and a good Signal for the Stock Market?
But this indicator really help us to time the stock market, which generally anticipates recessions? Let us create a very simple code and run a back test on the S&P 500. Since there is lots of data going back to the 70s, we can find out how this indicator performed during different environments. Of course, this is not a trading strategy, but it could become one if we add other rules and/or data sets.
Let`s say we want to smooth the weekly data in order to reduce potential signals. The following chart shows the initial claims with two exponential moving averages (EMAs). The last two economic downturns have been preceded by significant increases in inital jobless claims numbers and our EMA crossover worked well. So let us code this idea and test it on several decades of stock market data.
Please take a look at the code below, it`s just 9 lines short. I make use of inline instruments here, so that any macroeconomic data series from your data provider (I am using Eikon from Refinitiv here) can be entered in the parameter section. In order to make it easy, I define a period for the slow EMA, while the fast one is just one third of the slow one (line 6).
Once the code is done, it can be applied to any price series. In the example below I use the S&P 500 cash index for simplification reasons. Long positions are marked in green, while grey means we are out of the stock market. To avoid any optimisation, I just picked 52 weeks for the EMA. Using this parameter, this macroeconomic indicator was a solid exit signal for equity investments. The volatility of the equity curve (lowest sub chart) is lower compared to the market itself. On the other hand, you would have missed some gains in 1979/1980 or 1995/1996. A simple trend following trading strategy (purely based on technical indicators) would have performed better than this macro indicator.
This example here is only the first step in what could become a more complex model: We could combine different signal sources, add technical rules or implement a sophisticated position sizing algorithm. With Equilla all of that can be done in a reasonable period of time.
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