The market isn’t breaking down as much as it’s reflecting a structural squeeze. Demand still outpaces supply, particularly from industrial uses like solar and EVs, even as prices swing violently. Fundamentals and positioning both matter here.

And some new start up will come out of nowhere and likely surpass them all (just like Google did to all the original search engines.

You mentioned the stock looks cheap relative to peers — what valuation metric (EV/EBITDA, P/E, etc.) do you find most reliable for Canadian LPs like OGI given the volatility in the sector?

Department of Defense just awarded $ARSMF a contract for $168M for a supply of fluorspar from the only US fluorspar mine in the US. Fluorspar is used in every industrial a metal application. It's probably used in the metal to make the drones. $.45 is a great price before it really runs. It's about to go into production this quarter. It's also used to make uranium hexafluoride for nuclear production. Bullish

Given the volatility in markets, do you believe silver will continue to be a reliable hedge against inflation and economic instability, or are there other factors that could disrupt its traditional role in portfolios?

Highly recommended

As the CEO of #Verizon alluded to, I suspect there is a "Gotcha factor" at play here. Verizon is betting on 'upselling' customers to higher-tier plans, insurance, and/or other fees once they get in the door. In your analysis, what is the most common 'hidden cost' that turns this $25/line deal into a much more expensive monthly bill for the average family?

This sounds pretty revolutionary. Are any other companies doing somehing similar? What companies does it compete with?

I like #Netflix, but they only have 9 billion in cash. So they will probably do a secondary at some point in my opinion. Purchase price is 82 Billion. We will see.
Bullish on $NFLX.

The primary challenge in quantitative finance is not finding a signal, but ensuring its robustness and preventing alpha decay. What specific methods does your portfolio management strategy employ to combat common issues like data-mining bias (overfitting to historical data) or sudden market regime shifts (when signals stop working)? For instance, are you using regime filtering, dynamic re-weighting, or incorporating fundamental data overlays to validate the purely quantitative signals?