Buy and Hold (Part 2.5)

This post will eventually get merged into a Part 3. I've had some programming difficulties with the final part of the project and I've spent too much time watching Lost to resolve them before the Christmas break.

Continuing on with the Buy and Hold series (part 1, part 2) I've been writing. I was first curious to look at a long-term history of what a Markowitz Mean-Variance portfolio would look like over the years. Originally I planned on using about 90 years worth of data, but it seems really unstable for that period, so I only used the past twenty years (to get the weights, I used more data than that). I wanted to use this as a benchmark to compare strategies using similar Markowitz-type weights.

About three months ago, I did some research into interest rate environments similar to what has been done at MarketSci). After seeing their posts, I wanted to see if a long-term investor who solely identifies what interest rate environment they are in to determine their portfolio weights would outperform the typical Markowitz portfolio. I love what they do at MarketSci, but there is also value at creating rules that are simple enough for your Grandma to follow (like Mebane Faber's 200 day MA rule that I love so much).

Back to brass tacks, I have to concede that I couldn't operate the Matlab Mean-Variance optimizer. I could generate the portfolios, but then when I used those portfolios that I created in the optimizer it never worked. I'm still not sure why I was getting errors, but I decided that a simple approximation was to choose weights that maximize the Sharpe ratio, since that could replicate the optimizer's results. Unfortunately, this didn't let me use risk aversion to be able to change anything, but all I want to do is to compare one strategy vs. a benchmark stock/bond mean-variance-like strategy. I don't need things to get too crazy.

To identify periods of interest rates rising/falling/neutral, I looked at how much interest rates had changed over the past 6,12,18 months and if the difference was greater than some standard deviation multiples.

Then, I identified the returns in each period and separated them into different portfolios. As though they were investing in three separate strategies (ie expected returns and covariances for the positive interest rates were separate from the , I calculated weights using my Sharpe ratio optimizer. Where I'm stuck now is in error checking my lines of codes to combine them together (I might have the solution (pretty easy, just haven't gotten around to it), and will update after the holidays. Sometimes writing facilitates thinking.)

Again my hypothesis is that long-term investors could benefit just by investing differently depending on what interest rate environment it is.