MOS
The Mosaic Company
stock NYSE

Potash prices are [stable](https://ycharts.com/indicators/potassium_chloride_muriate_of_potash_spot_price), some co's like Nutrien (NTR) are in clear bulls, others like Mosaic (MOS), K+S Aktiengesellschaft (SDF.DE), and ICL Group (ICL) appear to have bottomed in November/December and are trailing NTR. Ag inputs are a defensive sector, and hence have mostly benefited from the rotation since October. I think that rotation will continue for a while.
I have some NTR, but my main interest is in Brazil Potash (GRO), a pre-production co that could be valued in the 40s by 2030, and is currently trading in the 2s. But I hesitate mentioning junior miners when threads are active, as they're really only for speculators who diversify away risk and have strong stomachs.

It's very possible. Many of these commodity names run together. I am adding $BTU to my $AA, $MOS, $HAL commodity bucket. I don't need more metals so I am buying coal. I am building a very diversified port that is very tech light. I want cap ex heavy asset stocks. Atoms are more valuable than bytes in this market cycle.

Some perspectives, having been in ML algorithm development and implementation on mini's, uP's and DSP's for ~ 50 years in R&D. My first major application area was speech recognition -- it's almost universally employed now, but at the time we struggled to get decent performance for speaker dependent isolated word recognition that could be fielded in limited applications. Think about the total market size of a new technology. I can pretty much guarantee that anyone trying to forecast the ultimate size of the AC power generation, distribution and utilization (via AC generators, transformers and AC motors) after Westinghouse and Tesla first finished the Niagra Falls power generating station, were laughably wrong. Or the transistor, we've gone from the little pocket 9 transistor radios to IC's which have tens of billions of transistors on them. I still have 7400 TTL and 4000 CMOS IC's in my parts drawers that have less than 100 transistors each. Then the uP, first designed for calculator use (Intel's 4004), now embedded in virtually every device that has any thought of using electrons in an optimized, or intelligent way (now lightbulbs, for gosh sake). So look at the size of the semiconductor market over time, from those first semi companies who first started selling transistors commercially in the early 50's, to the overall size of the semi market now. Pretty hard to estimate the ultimate size of a market for new technology. (Could easily apply to the steam engine or the wheel for goodness sake.) What make that problem of prediction even more difficult is the near impossibility of seeing the range of applications that can be enabled by a new technology, which only expand the market further. If transistors were limited to those pocket transistor radios, we wouldn't even be here on this platform now. I've been attending conferences in this area (and related areas) since the late 70's, and still am. Take a look at the incredible range of application areas at NeurIPS, ICML, or others of that calibre, and you realize that we're not only talking about robotics and "agentic AI" and the now inescapable LLM's. Having worked in ML for healthcare myself (among other areas), I think ML/AI in healthcare will be another huge win for this technology -- and not just for recording patient interviews, either. You can fill in the blanks for yourself. Virtually anything that can be optimized will be running an ML based program to squeeze out the last bit of efficiency along with intelligent action, and even impending component failure prediction.
One other perspective comes from a career of writing both low level (assembly) up to high level (Matlab) for mini's, uP's and DSP's, and working for my always frustrated engineering managers. I've spend way too much time debugging code and hardware, including from cutting edge companies like TI who inevitably have both hardware bugs coupled with bugs in development software, even for a relatively mature series of processors (the TMS67xx in this case). Maybe I'm lazy, but I hate switching development platforms, and I don't think I'm the only one. It's not just the learning curve, but the inevitable issues with less mature software that is usually the case when switching from an industry standard platform. And engineering managers hate risk, especially when being tasked with bringing out a new product in a hypercompetitive market. If they bet on substituting an alternative to Nvidia's hardware / software ecosystem, with it's huge developer and software tools / packages / applications base, and something goes wrong, they are pretty much history in that company. For developers, having a rock solid, mature development environment is just the start for development of a new algorithm and product built around that algorithm. Anything that gets in the way of new product development R&D, which is really the primary goal for a company wanting to be a "first mover" in a new area, is nothing but a drag on the timeline, as well as carrying the potential with it to have fielded a buggy system, for which fixes are notoriously expensive.
One more thing about ASIC's. Sure we'll always have them, for mature applications that require the last bit of efficiency. But if they're tied to a class of algorithm that is suddenly usurped by something significantly new, they risk becoming ash tray components (does anyone make those ashtrays for your parents in shop class anymore?). Thinking back on the types of algorithms that were used in speech, for example, you've got (mainstream only) template matching, dynamic programming, on to learning based algorithms starting with (again, mainstream only) HMM's, HMM's coupled with GMM's or neural networks, TDNN's, and starting with deep learning, CNN's, RNN's, LSTM's, up to transformers and beyond. That's not to mention underlying techniques for dataset enhancement, efficient training and architecture optimization, and sometimes application dependent pre-processing (not all applications allow for "black box with ginormous datasets" training). Again, look at NeurIPS or ICML (and others) to get a sense of the huge range of algorithms that could become mainstream, and wonder if an ASIC specifically designed for a particular type of algorithm may or may not be useful in that new paradigm.
One other thought about alternatives. I happily watched all the semiconductor companies bring out their 8 and even 16 bit uP's in an attempt to compete with Intel -- GI, Intersil, Fairchild, TI, National, Motorola, MOS Technology, Zilog, RCA, AMD's bit slice components. IMHO, only Motorola wound up with a second tier (in terms of volume, not quality of design!) position to Intel (while Intel's architecture was kinda crufty compared to the clean architecture of the 6800 / 68000 and TMS9900, for two examples of DEC PDP-11 inspired designs). But Intel was the dominant uP for decades, thanks to IBM's decision to use it in their first PC (it wasn't really their first, which was a 68000 based PC oriented more towards scientific use). So many developers and companies converged on Intel's architecture that it took decades to pry their dominant position away from them. I kinda think Nvidia is in that same position for some time...

Expecting the Instutions to start Loading Hard - should see a Double in the Share Price is the next days, the Positive P3 Result is worth 70x the current manipulated market value.

you state the historical BAT MOS is 10 months ... prove it.
All Actual data confirm BAT MOS is 6-8 months.

As usual, disclaimer: Everything I write is an opinion, none of my words may, at any point in time, be considered financial advice. Let´s go through this:
"I also check stock sentiment and the business fundamentals as well."
The latter is value investing. The aforementioned is not - that´s gambling. So to your questions: I refuse to recommend certain stocks and even if I wanted to, I´d have to ask people to pay for my research - cause that´s close to 10 years of work put into my selection. As far as the "good idea" goes: You lost money to value traps. Now ask yourself: What made them value traps?
The weird thing about questions like yours: The answer is so damn obvious every single time, but the vast majority of people still let their greed (or at least their emotions) get the better of them. They do the irrational thing - and that´s where they get burned. I quote again:
"I reduce last year’s free cash flow by 50% and if the enterprise value is less than 20 times that adjusted number, I consider it a buy"
Cool. But it still drops by 60%. It goes on to 70%. Then to 80%. Again you end up in a value trap.
Do you notice what just happened? Nobody has a crystal ball. All that juggling with numbers may be fun - but it tells you exactly nothing about a company. See, if a company actually had proper, solid fundamentals that make it a buy, why even worry about the hypothetical FCF -50% anyway? If it has a track record, that scenario is incredibly unlikely to begin with.
Having a MOS is important, yes. But by the end of the day, unless you find an opportunity that is a certain buy-and-forget kind of thing, you are not value-investing. I can only tell you this: All the companies I do own I want to own 20 years down the road, too. And note my phrasing: I don´t care about stocks and stock prices that much - cause I am not buying stocks. I am buying a part of a business.
That´s where people get it all wrong: They buy ideas and hypes and hypotheticals. They buy shares as if those themselves had any value - which they don´t, cause they all can go to 0 and that´s just that.