Motivation This string of posts is meant to help non-professional investors understand some of the complexities involved in choosing an investment strategy, suggest a logical framework on how to do just that, and offer different ways to analyze the data that motivate the strategies we’ll examine. Strategies for the long run Investment strategies and styles abound — from fundamental to quantitative to technical. For the professional investor how much credence one gives to each approach depends as much on education, training, and employer as on open-mindedness and opportunity cost.

A recession a year from now? As we saw in the last post, when we run a model with a 6-month look forward, it does a fairly reasonable job in predicting a recession, assuming we use a threshold closer to recession base rate. In this post, we look at 12-month look forward and then use the best of the two look forward models to test it on out-of-sample data.

Where did we go wrong? Not another model! As we saw in the last post, one iteration of the yield curve – the spread between 10-year and 3-month Treasuries – doesn’t generate a great model of recession probabilities. Part of this is that recessions are not that common, so we’re trying to find the veritable needle. Another problem is picking the right threshold to say the model is prediciting a greater likelihood of the economy being recession.

All models are wrong Build the model In the last post, we discussed the yield curve, why investors focus on it, and looked at one measure of the curve – the spread between 10-year and 3-month Treasury yields. In this post, we build a model that tries to quantify the probability that the economy is in recession based on the 10-year/3-month spread. All models are wrong A first question to ask is what kind of model should we build?

Introduction What is a yield curve? (Skip if you already know!) Show me the data! (Start here if don’t want the background) Investing pundits like to quote the yield curve as a nearly infallible indicator for the next recession. But what do the data say? And which yield curve should you use? In this multi-part series we try to answer these questions in as straightforward (though not necessarily simple) a manner as possible.

Python-bloggers aggregates blogs focused on using Python’s data analysis super-power for data science, machine learning, and statistics. Brought to you by the same folks that publish the hugely popular R-bloggers, it is well worth a read. Check it out here!

Quantocracy is a great resource for all things related to quantitative and empirical investing. We learn something every time we visit. Expand your knowledge here!

R-bloggers is a great resource. We visit the website almost every day. Shouldn’t you? Have a look https://www.r-bloggers.com/.