Now that inflation is raging at highs not seen in the last 40 years, it’s no wonder that investments which guard against inflation have been experiencing a massive influx of money. With billions of dollars of new inflows every month, Treasury Inflation-Protected Securities (commonly referred to as TIPS) have quickly become some of the hottest portfolio options for nervous investors. And since questions about TIPS on message boards and in my inbox are apparently directly proportional to those cash flows, this feels like a good time to dig into the topic and separate the measurable truth from what passes as common knowledge.

How do TIPS work? How often have they succeeded in generating a real return above inflation? And are they really better than normal bonds without the inflation protection? Stick with me, and I wager you’ll learn a few things that may surprise you.

One of the core assumptions baked into all of the Portfolio Charts calculations is the idea that the portfolios are rebalanced once a year back to their target percentages. While that simple process seems rather mundane on the surface, there’s actually a bit of mathematical magic going on that often gets lost in broader portfolio discussions. Yes, maintaining your target asset allocation is an important part of risk management, but it goes so much deeper than that. What if I told you that, like a lonely plant in a barren desert, in the right conditions rebalancing can cause profits to seemingly appear out of nowhere?

That peculiar phenomenon can clearly have a major impact on the way people think about diversification in their investments. So let’s unpack the mystery and talk about the elusive rebalancing bonus.

Sometimes the simplest investing concepts we take for granted are actually a lot more complicated than we think. For example, reasonable people might rationally assume that two small cap value index funds should have identical returns since they theoretically follow the same asset. But it doesn’t really work that way because definitions matter. What does “small cap” mean? Who defines “value”? Read the fine print and two similar funds may be a lot more unique than you realize.

I’ve been thinking about that subtle complexity recently, as in the process of finalizing the most recent annual Portfolio Charts data update I refined the definition of “small cap” to better match common index fund methodologies. The tweak was simple enough, but succinctly explaining what it means and why it matters got more and more difficult the longer I thought about it. The entire process made me realize it might be a good time to have a longer discussion on how stock index fund definitions work.

So if you’ve ever looked at an assortment of large, mid, small, blend, value, and growth stocks and wondered what all of that actually means, this article is for you. It’s going to get a little technical, but if you stick with it you’ll learn something not only about how indices are constructed but also how to use that info to interpret the data you find both here and elsewhere.

An interesting recent trend I’ve noticed in portfolio discussions is a renewed debate about the resilience of factor premiums versus the good old cap-weighted stock market. It’s entirely predictable that a tough stretch for any investment has a way of bringing out both the nervous supporters on one side and the proud haters on the other. But I really can’t fault the pros for keeping an eye on performance of some of the trendier factors or the investing laypeople for wondering what everyone is even talking about.

What do I think? It’s complicated. So let’s talk about factor investing.

When discussing historical investing data one of the more interesting points that inevitably arises is the question of just how applicable past results are to current events and future investing decisions. Some people reject all historical data as completely irrelevant because the future will never look exactly like the past, while others hold up mathematical evidence-based investing as some sort of scientific principle that one would be foolish to question. I imagine one might expect me to fall into the latter camp, but frankly I think it’s more complicated than that. Good data speaks for itself but unless you’re speaking the same language you can easily get the message completely wrong.