Small Town

I wish I had written this before Saturday's events in Butler, PA. That said, aside from saying it's tragic that someone that went to support their choice for President died because of some lunatic, I will keep my comments on those events to a minimum. I've been to Butler dozens of times in my time visiting family out in Western, PA. It's a small town north of Pittsburgh where the majority of people I've ever dealt with there are wonderful. I'm glad former President Trump is ok, and I loved his response to this.

Now I'm going to get back to business and markets. It was a great week for most equities, but especially value and small cap. Most commodities traded lower, and most bond indices were higher. Most of this was essentially off the lighter than expected CPI print. This week I'll highlight the performance of small caps and take a look at how traders are positioning themselves for the near term. If you don't recognize the title this week, it's from 80s rocker, John Cougar Mellencamp. Mellencamp says he's seen it all and had himself a ball in a small town. He also mentions he was born and will probably die in a small town. Last month we saw a few small town stock get to the big town, but I feel many small caps will stay small. Ben Carlson, CFA points out that may just be a recent phenomenon, as there are many periods which small caps dominated their large cap brethren.

The US CPI print last week was down month on month and below 3% on the year on year for the first time in quite a while and this set off traders to assume FED cuts are coming.

This saw a shift lower in the in the rate expectations. Now they're expecting a Fed cut by September. Call me crazy, but I also think after recent events that Trump will take a commanding lead by September. This takes pressure off the Fed to be lean neutral and allows them to do what they think they should do, which most assume is cut.

Small caps, with their bigger impact from rates, saw the most benefit on Thursday and Friday. It wasn't just the US either. Look at how the global and regional indices also responded.

Russell 2000 Value was the best performing of the sub-indices for the week, and I wanted to break down the industry groups to see where the most success was in that group. Health Care and Banks were up rather strongly. Food, Beverage, and Tobacco was the one group that underperformed by a large amount.

This week's strong performance barely puts a dent in the longer term underperformance. Whether it's since the COVID lows or the end of '22 bear market, small caps are still getting crushed.

Something of interest that I saw was the large increase in the 1-month at the month implied volatility. Notice here that relative to the trailing month realized vol, which you'd expect to spike with last week's moves.

I wanted to look out a few months and track the 6M/1M spread. It's essentially come down to flat (0.30) from the 2-2.5 range, but over the last year the current reading is about average.

The skew on the Russell 2000 options has also been on the rise. I think this essentially means that traders are taking on more upside risk.

Let's take a final look at this using the current volatility surface for the 1M and 6M tenors. You can see the 6M in white is more inline with the typical volatility smirk. A slightly negative skew with people positioning for downside risk. Whereas, the 1M has a positive skew with the belly of the curve is in the 35 puts.

So with all that I'm reading the traders are positioning for more upside in smallcaps, but what I don't get is there were no flows last week. In fact, net flows across about 3,000 funds in the Lipper database were negative. I'm intrigued to see if/when those flows begin to appear.

If these little guys are gonna go, I'm a bit concerned just buying the index as a whole though. There's too many mines that field. Here's a quick look at the largest names across the small cap universe. The Starmine Combined Credit median is a weak 38th percentile. Right off the top, the largest name is a terrible score of 5. The bottom graph looks at the range of Combined Credit scores along the x-axis versus the 1 week performance on the Y-axis. Seems sort of random on how they performed last week, but you can see a small bias to the left of the chart, which is lower credit scores.

Before we get into the content. I'll be off next week, as I celebrate my anniversary with my lovely wife. If I want to stay married, I definitely need to be off the computer next weekend.

Best of the Week

This week Barry Ritholtz talks with Matt Eagan, CFA , who is a PM and head of the Full Discretion team from Loomis, Sayles, & Company. Matt and team run about $75B in assets, but Matt got his start in electrical engineering in university. Just goes to show many of us don't end up where with think we will when we're 17 years old. The portfolios that the team at Loomis Sayles manages today are multi-sector versus the typical core plus fund. Most core plus manage against a benchmark and are constrained, but Matt's multi-sector has no benchmark and is an absolute total return. The one thing they note here is that many consider total return to be Price + Dividends for equities and Income + Principal Return for Bonds. The team is credit cycle investors, and when a bad event comes they tilt into risk. They try to get alpha from their bottom up research by taking advantage of spread premium but avoiding permanent losses and like to go big opportunistically. They also utilize the ability to get into convertible bonds, which they can make more on because they're less efficient. Matt touches on their opportunistic investing. He says he enjoys work during downturns. Barry turns that around and asks how he dealt with the long bond bull market, which Matt said was essentially following the FED. Matt's in the higher for longer now camp because of the huge amount of fiscal stimulus. This was a great conversation and I learned a lot about active bond fund management. Listen time: 67 minutes

Fixed Income Credit Analyst to Bond Manager With Loomis Sayles

Best of the Rest

Multi-strat hedge funds have been all the rage of late following their great performance of 2022. That said their market share of AUM has not increased much at all, and in my view barely broken out of, what appears to be, a down trend since 2000. In this article, the author notes the differing performance of Eurekahedge and Credit Suisse as sources. He also says that there tends to be some backfill and survivorship bias. Another point covered is the correlations to the S&P 500, which on average are between 0.55 & 0.7, but at times they've been as much as 0.8, which isn't good. The article also replicates the performance of said funds with a partial allocation to equities of 40% and then some cash to round it out. Finominal also replicates the performance of a couple of the hedge fund ETFs, QAI & HFND, by doing a simple allocation. Essentially, the post concludes that on average multi-strat funds are expensive and easily replicated.

Multi-Strategy Hedge Funds & Replication ETFs

This post is working on a couple of new additions for the second edition of Jason Zweig 's The Devil's Financial Dictionary. First is social investing, as in research on social media as a lens and it's effects on markets. The second is more common as the "S" in ESG, and it focuses on the nuances of human capital and product safety scores and how the future returns differ for the two. The article also highlights the chart below from GMO's latest quarterly letter. Essentially, it's not just multiplier expansion, but from the bottom line EPS growth.

Two Kinds of Social Investing

One for the Road

The author John Pavlus help us with a nice explanation of the fundamentals of machine learning. First, that it's a subfield of AI. John explains that ML uses algos to improve other algos through both supervised and unsupervised learning, as well as with reinforcement. The article points to the take over of neural networks since the 2010s because of the billions of parameters. "Deep down, these machines all learn the same way: back and forth, back and forth." Machine learning is one of the tools that many in the financial world are using to analyze and learn from big data. My employer LSEG Data & Analytics recently worked with WatersTechnology.com to survey the industry and find out more about the use of Large Language models. Essentially, it's early in the process of implementing these, but there's interest.

What Is Machine Learning?

Thanks for reading. Have a big week.