Tag Archives: paul-novell

Comparing Portfolio Performance (1973 To 2015)

I’ve finally managed to gather enough portfolio performance data to put together this year’s portfolio comparison edition. I was able to add 2014 and 2015 data. Last year’s post is here . You can use last year’s post and the Portfolios page for portfolio definitions. I’ll present the comparison of the portfolios in a few ways. I also added a few new fields this year. I added the last 3-yr, 5-yr, and 10-yr performance for each portfolio and performance in the last bull market and last bull/bear market cycle. Now, on to the data. First, let’s present the data in its most traditional way, by sorting the portfolios in terms of performance over the full time period, 1973 through 2015. There is a lot of data in these images so click on the image to make it easier to see. Click to enlarge A few notes on this presentation. Performance, CAGR, is highlighted in light orange. The most common, ‘traditional’ benchmarks, are highlighted in light blue. The portfolios in yellow are some of the popular buy and hold allocations where I had to create or simulate the last 2 years of performance data based on their allocations. This method is pretty accurate since they’re passive portfolios but still the data is not exact and does not come from a standard source. So, what’s the main message here? Quant and TAA portfolios provide the highest performance over the entire time period. The Bernstein portfolio is the highest ranked buy and hold portfolio on the list at #12. Now on to risk-adjusted performance. Risk-adjusted performance is a much better metric in choosing portfolios. Here I present the portfolios sorted by the Sortino ratio, which doesn’t penalize portfolios for upside volatility, instead of the more traditional Sharpe ratio. Click to enlarge In risk-adjusted terms, quant and TAA portfolios are also at the top of the list. The highest ranked buy and hold portfolio is the Risk Parity portfolio at #9. Risk-adjusted metrics like the Sortino ratio are particularly important for investors in the withdrawal stage of their life. Higher risk-adjusted returns is highly correlated with higher SWRs in retirement. Great, but many investors ask, ‘what have you done for me lately’? Maybe markets have changed, maybe what worked in the past doesn’t work anymore, etc… Many investors use recent performance, especially since the start of the most recent bull market in 2009 to make portfolio comparisons. This is a mistake. You should at least look at the most recent bull/bear market cycle in addition to the full history. Below are the portfolios sorted by performance since the start of the last, in this case, the bear/bull cycle, which started in 2007. Click to enlarge Nothing surprising or new here. Quant and TAA portfolios have led the pack in the last full market cycle. The highest performing buy and hold portfolio, the 70/30 US stock bind portfolio, comes in at #9 with the vast outperformance of US markets over pretty much anything else in that time. There you go. Tons of data to make all kinds of comparisons. Go crazy. These are my favorite 3 ways to look at portfolio performance.

Tactical Asset Allocation – February 2016 Update

Here is the tactical asset allocation update for February 2016. As I mentioned last month, I am now using a new data source for the portfolio updates. I am also maintaining the old portfolio formats, in Yahoo Finance, for a while. Here is the link to the Yahoo data. Let’s dive right in. Below are the updates for the AGG3, AGG6, and GTAA13 portfolios. The source data can be found here . The big change here is the use of FINVIZ data and more importantly that these signals are valid after every trading day. So, while I’ll maintain these month end updates, this means that you can implement your portfolio changes on any day of the month, not just month end. FINVIZ will at times generate signals that are slightly different than Yahoo Finance. Click to enlarge AGG3 is now 100% bonds and no cash. This is a significant change from last month where AGG3 was 66% invested. AGG6 is 33.3% cash and 66.6% bonds. AGG6 is more invested than last month’s positions. Below is the YTD performance along with some popular benchmarks. Once change in the performance figures this year is that I am know including the performance of cash when the portfolio sin cash (using SHY as the cash proxy). For the Antonacci dual momentum GEM and GBM portfolios, GEM is now in bonds, BND, and the bond portion of GBM is in cash. I’ve also made my Antonacci tracking sheet shareable so you can see the portfolio details for yourself. Here is the data. Click to enlarge Finally, I am receiving quite a bit of interest in the simple bond quant model I published previously . So, I created a spreadsheet to track one version of the model I presented. The spreadsheet ranks the bond ETFs by 6 month return and uses the absolute 6 month return as a cash filter to be invested or not. Several versions of this model work quite well as discussed in the blog post. Personally, I am now using a 3 month return, 3 month filter, top 3 model but the differences are not that big. That’s it for this month. These portfolios signals are valid for the whole month of February. As always, post any questions you have in the comments. **Note: an observation for this week. Ever notice the percentage of self-called ‘long term investors’ who know what the stock market did on a daily basis? Let me tell you that is long term detrimental to your portfolio performance. It is hard to ignore market data in today’s world. I try very hard to ignore it and have to take active action to avoid finding out about daily gyrations in the market. It’s one of the reasons I do not blog more often. My goal is to only check one per month, that’s it. And even that is too often. If I could auto trade my quant systems I would… I once heard it said that most investors would achieve higher returns if they lost their password to their investment accounts for years. There is a lot of truth in that statement….

Quant Investing: Improving The Value Of Shareholder Yield

Part of quant investing is always being on the look out for better metrics and systems that enhance performance. Today I want to look at a simple improvement to the value metric shareholder yield. I’ll look at this in the context of the quant index replication strategy I posted on here . First, lets look at shareholder yield in more detail. Recently there has been some interesting discussion on the level of buybacks, as a percentage of market cap, and how strong a conviction by management that represents. The idea being that the higher percentage of shares a company is buying back, the more conviction management has on the value of the company, and thus leading to better stock performance. The best analysis of the topic is here . The analysis going back to 1987 shows two key things; the largest buybacks (greater than 5% of market cap) are done at cheaper valuations and this leads to better performance over the following year. The large stock shareholder yield quant value strategy is a big improvement on the traditional indices. But maybe we can do better armed with this new information on the level of buybacks. I’ll take the original large stock SHY value strategy and compare it to a new version which only buys the large cap stocks sorted by SHY if the buyback yield is greater than 5%. We’ll go back to Jan 1999 and run the backtest through yesterday’s market close. First, the performance for the original large stock SHY strategy. Pretty darn good, 16.44% per year since 1999 with a Sharpe of 0.75 and Sortino of 1.06. Now lets add the filter that only buys stocks with a buyback yield greater than 5%. Even better as the research suggested. 17.59% per year since 1999 with a Sharpe of 0.80 and a Sortino of 1.17. That a 1.1% per year return enhancement with an improvement in risk adjusted returns as well for a very simple addition to an already powerful strategy. In short, screening for high conviction buybacks is a powerful addition to a large cap shareholder yield value strategy.