The last Structure we did on Jushi ($JUSH) included a spreadsheet I was playing around with.

Google Finance is an amazing plug-in for Excel – and pulls near real-time metrics into any spreadsheet. It’s limited of course: no historical data (natch); it doesn’t offer a wide array of metrics; and it doesn’t warrant the information either. Professional data services have that latter characteristic as well, except they have a far higher bar to meet. And being commercialized, data quality is typically far higher. At any rate, Google Finance is still a very useful (and free) tool for the DIY analyst.

Since we have many current and aspiring traders here (you know who you are), I thought I’d update that spreadsheet to include the latest quarter, and do some analysis around the delta. Absolute values aren’t particularly useful to a trader. It’s in the rates of change (delta) where money is made, and trade discovers the exposure they want to take on. Volatility can be seen as a measure of the velocity of delta. <Options traders will know that the ‘delta of delta’ is also known as gamma, and way out of scope here. I’ll include my usual “please don’t touch options w/o a complete understanding of the underlying exposures” caveat. Seriously. Please don’t.>

Any trade that’s put on needs to have a specific purpose, a timeframe, and a costing. Similarly, investments need to be reviewed for changing circumstances. One might view a trade like a dating partner, where an investment is more like marriage. A higher level of commitment is the difference (but neither are necessarily permanent).

Back to delta, I put together a quick spreadsheet that compared market cap divided by sales by MSO ‘tier’. One could view it as as an applied DCF calculation using share price as a present value. That table said to me that both Curaleaf ($CURA) and Terrascend ($TER) were priced higher per dollar of expected sales than peers. (The Tier 3’s are showing what would be expected of higher risk: more volatility = greater variance from mean. <Please ignore Vireo, I made an error last time around, it’s been corrected>.

My purpose for doing this table was to answer a specific question: I am interested in MSO exposure, but assume I can’t figure out who to buy. If I do buy a basket of Tier 1’s, how might I know how to weight them in my portfolio?

Our outliers last time indeed changed QoQ. We also see a strong bullishness priced into $PLTH:

From an exposure standpoint, in the Tier 1’s, a high positive variance to average suggests a premium is priced into a particular company relative to sales. In this case, $CURA’s had a higher per average dollar of market cap per dollar of sales. Now? Back to earth, and essentially on average.

If I had been buying a basket of Tier 1 back in January, my weightings would have been relatively highest in $TRUL, and lowest in $CURA. If the market backed off, I would have expected to lose the least value per revenue dollar relative to Tier 1 valuation. It doesn’t mean I wouldn’t have lost money.

What that specific position does is address was a very specific purpose: I expected all of the stocks I bought to advance (that’s why I went long), and, I am speculating that $CURA will advance more slowly relative to its’ peers.

By doing this, I expected to make superior returns…..as opposed to equal weighting.

I didn’t do the trade. But, I wanted to provide an example of the need for a trade/position to have specific purpose. And that a trade book needs to have coherence in the positions it takes on. Having unseen cross-hedges laying around – or stacking risk – muddies exposure and clips returns.

I’d encourage the interested reader to do a look back between January 15th and April 12th, and do a paper trade with weightings as I’ve suggested on the equities. Then compare the returns on both of the positions. You’ll see that the portfolio with equal weighting will have lost less than the equally weighted portfolio. I’d kind expect that too, because the position was built around maximizing upside exposure. Should relative before/after ratios have held, I’d expect it to have been an equal loss to the equal weight. Either or, the core premise (prices increasing) didn’t hold.

And hey, there’s a hundred ways to look at the data in the table. Along with a hundred ways it could be made better (or worse). Some folks will think it’s dumb, some not. And that’s fine. Because forward views are subjective, and so are the positions one takes to exploit them.

What is inarguable, is that trades require specific purpose, that exposures are clean, and that they require specific entry/exit points.

I’m putting this up to give you a springboard into how a trader looks at position. And because I really didn’t want to do a Structure on 4Front ($FFNT) this morning.

These latest numbers say to me that $HBOR is seen to be a mutt, and that $PLTH is a darling. It also says that expected revenues in the Tier 1’s are now being priced relatively equal by the market. You’ll probably have your own views on it all. Good.

The preceding is the opinion of the author, and is in no way intended to be a recommendation to buy or sell any security or derivative.