In partnership with Utrust, the only crypto payments gateway your business needs 👇
Everyone knows that the key to stock market success is buying low and selling high. The hard part is figuring out what's low (and good value) vs what's expensive (and primed for a fall).
It sounds so simple. In practice, it's really not. Just ask Tesla bears who've been shorting for years because of the "INSANE" valuation.
The best thing? As soon as the Fed started to turn in November 2021, the Tesla game was up. These fateful words...
In light of the substantial further progress the economy has made toward the Committee's goals since last December, the Committee decided to begin reducing the monthly pace of its net asset purchases by $10 billion for Treasury securities and $5 billion for agency mortgage-backed securities.
They hadn't announced any rate hikes and they were still doing $100 billion or so QE at the time. But that day marked the top for Tesla's share price. Nothing to do with the company itself.
It's now trading ~53% lower and we see an entirely different Fed policy, just 16 short months later.
Nevertheless... Morgan Stanley analysts are enthusiastic about the company's future 👇
“Tesla's audacious efforts on vertical integration are about to pay off. EVs are far too expensive today. Tesla gave a number of drivers for a 50% cost reduction for its next-gen platform. We seriously question how the competition can keep up”
Even now, Tesla's market cap sits at $610bn. Compared to second place Toyota at $185bn, that's STILL a hefty premium. But stocks are worth whatever people are willing to pay.
This article isn't really about Tesla though. It's about this chart. 👇
John Authers shared this in a recent newsletter and explained 👇
For decades, the Philadelphia-based fund management group AJO Vista (formerly Aronson+Johnson+Ortiz) has kept a chart of the returns you would make by consistently shorting the most expensive 10% of US stocks, and putting the money into the 10% cheapest.
(It’s done using trailing price/earnings multiples to avoid any contamination from over-optimistic expectations, and is equal-weighted and sector-adjusted to control for extreme excitement such as the dot-com bubble, or the more recent era of the FANG stocks. Their universe is the 2,000 largest stocks across all sectors.)
Assuming that you were able to execute this ultimate crude value trade, and had the discipline to keep rebalancing, such a strategy would have been stunningly consistent, and successful, over time. They calculated the numbers back to 1962; during those 38 years, it made an average of 5.5% annually, a remarkable achievement when it was always betting against the most richly valued companies in the market.
But what’s most interesting is what tends to happen after the few periods when it loses money. There had been four periods of negative performance before 2000, the worst being the “Great Garbage Market” of 1968. In all cases — and in spades after 2000 — a huge snap back in favor of cheap stocks followed
Now, this is FAR from an optimal strategy for all kinds of reasons. AJO themselves say "These paper-portfolio returns are the result of an academic exercise and are not the results of any strategy or investment recommendations made by AJO. They do not reflect gargantuan transaction costs."
It set me thinking though. At roughly the same time as I was reading this, NVIDIA was reporting very unspectacular, normal earnings. Yet the stock rallied in after hours because "AI" is The Next Big Thing™ which had sent the share price even further into "Are you having a laugh?" territory.
So, NVIDIA's expensive. Why not short an expensive stock against a cheap one?
Tesla is a cautionary tale, but we're gonna do it anyway. Far easier to keep a ridiculous valuation when rates are at, or near, zero than when they're at 4 or 5%.
Here's how NVIDIA's stacking up.
None of this is a negative reflection of NVIDIA as a company. It's more a perspective on there being very little risk premium in the price if things don't not pan out as perfectly as planned.
One chart that caught my eye is this assumption of sequential data centre revenue growth from Wells Fargo 👇
Lovely extrapolation, might turn out to be right. Just looks a little too... perfect.
So, that's our expensive short leg of the trade... What can we find that's too cheap... Unloved and undervalued.
The Veteran screened for some candidates and came back with IBM. I was underwhelmed. Plus it's (kind of) tech as well.
Three more names came out of the hat...
- Valero (Petroleum Refiner)
- Pulte (US Housebuilder)
- Mosaic (Fertiliser/Chemicals)
Mosaic jumped off the page. A fertilizer & chemicals business. The company saw revenues grow drastically on the back of the Ukraine invasion. 👇
Financials breakdown 👇
In the current context of increasing sanctions and 'friendshoring', could Mosaic pinch market share permanently from Russia & Belarus?
Barclays aren't really fans....
JP Morgan highlight the risks 👇
Additional DAP production is likely to enter the market from Saudi Arabia and Morocco over the next few years. This capacity could lead the phosphate market into conditions of oversupply, reducing product prices and volume for Mosaic and leading to a lower share price.
Moreover, meaningful potash capacity could come on stream over the next few years, placing pressure on Mosaic’s potash volumes and prices. Higher phosphate production and exports from China post a COVID-19 world could add pressure to phosphate product prices and volumes for Mosaic and lead to a lower share price.
We're gonna do it anyway.
NVIDIA vs Mosaic
For transparency, we'll take it from the 24th February closing price of each stock (when the idea was born).
Obviously this isn't a trade recommendation or financial advice, DYOR etc.
Here it is as a ratio 👇
Let's see where it is in 12/18 months time!
One note to finish. Generally the above approach is horrific. From a trading perspective, buy high, sell higher is a better template 👇
The “52-week high effect” states that stocks with prices close to the 52-week highs have better subsequent returns than stocks with prices far from the 52-week highs. Investors use the 52-week high as an “anchor” against which they value stocks.
When stock prices are near the 52-week high, investors are unwilling to bid the price all the way to the fundamental value. As a result, investors under-react when stock prices approach the 52-week high, and this creates a 52-week high effect.
Academic research backing that up 👇
We find that the 52-week high effect (George and Hwang, 2004) cannot be explained by risk factors. Instead, it is more consistent with investor underreaction caused by anchoring bias: the presumably more sophisticated institutional investors suffer less from this bias and buy (sell) stocks close to (far from) their 52-week highs.