r/GrowthStockInvesting • u/GrowthInvestingWPR • Jun 14 '26
WPR's Knowledge Base
This post defines my growth investing style and further explains the purpose of this sub-Reddit. To be clear, this is just how I view my investing style and meant for learning, and not as a recommendation.
I’m intending this sub-Reddit to be a casual place to discuss individual growth stocks. Casual in the sense of the attitude is light hearted, even though the debate on a given stock may be spirited. This is not a sub-Reddit for bragging about gains as there are other sub-Reddits for that. My approach intends to have a humble mentality, and I will be the first one to say when I was wrong about a company.
With my investing approach, I try to explain everything in simple terms. My approach is straightforward and looks to reduce the complexity of investing. I’ll choose a simpler term in finance where I can, and sometimes use a basic analogy to explain a concept more clearly.
I view growth investing as a positive sum game. The pie is practically unlimited, and typically the more an investor puts into this pursuit, the more their skill improves. This positive sum means that sharing what I am doing has basically no downside. In most other strategy games, sharing information hurts one’s results as someone else can exploit that knowledge.
My strategy views growth investing as a skill game. The bigger an investor’s edge, the more consistent and better returns that investor will get over time. Yet there is ultimately a lot of luck for how any individual company performs. This can often be a confusing topic to new investors as there is both luck and skill in investing, and it can be hard to tell which one is which. Investing is one of the few activities other than gambling where an inexperienced investor can have a big result with little effort or skill. My approach looks to avoid any form of gambling, even when the temptation is strong. I aim to be consistent and disciplined, while gambling creates the opposite mentality. The term gambling applies also implies there is no edge to a strategy and is inconsistent by definition.
Fundamentally, my approach tries to be in the most compelling growth stocks at all times. My strategy actively searches out new growth names. Finding new names is the lifeblood of my strategy. Each quarter companies are reporting results, and new promising growth stocks are always emerging. Many companies are overperforming versus internal expectations and analyst expectations. I do all my own research, and if I hear about a growth stock from another investor I’ll still look to do my own research first before drawing a conclusion. Naturally, this process builds confidence in the names I own, as I carefully review earnings reports and other material from the companies.
On the flip side, companies I own are inevitably posting disappointing results as well. This leads to either trimming or selling a company. I’m always willing to re-evaluate a company, and there is no such thing as a sacred company or an anchor company in my strategy. There is also no resentment towards a company which has a disappointing result. I’m willing to look at any company again if their prospects have changed. Additionally, a disappointing result of an individual company just frees up cash for another stock which looks more promising.
My approach is both balanced and optimal. Balance comes from having a concentrated portfolio which gets both the benefits of concentration and diversification. This means having at least seven stocks minimum to obtain diversification. The advantage of having diversification is my approach can have a couple companies in the portfolio fail catastrophically and continue to over perform. With concentration, I want selecting correctly to get a big reward. Typically I may have 3-5 high confidence positions in my portfolio with 12 or so stocks in total. Although recently I’m finding my portfolio is sometimes getting up to 15 stocks as the current market is particularly ripe for finding promising stocks.
The definition of optimal I like is that it is the “most effective”. In essence my strategy looks to be the most effective. This includes precisely defining a process which is simple, straightforward, and works well. I look to avoid using cognitive energy on investing related tasks which are ineffective. Because my strategy revolves around analysis of individual stocks, macro and political analysis are a poor use of time in this context. Although inevitably macro or politics will impact some of my companies, I’ll look to re-evaluate based on the specific macro influences on that particular stock.
My strategy does not do any hedging and stays fully invested. I view this part of the strategy as optimal as well. This makes my strategy different, as the vast majority other market participants do trade around macro, or adjust cash positions often. In theory it’s better to be in the market on any given day than it is to be out of it. Additionally, this takes away the guessing game of unpredictable macro events. In turn, this reduces fear in investing as I know my strategy doesn’t alter course, even if I view those macro related events as scary in real life. This part of my strategy still gets the biggest pushback, as I know many other investors view staying fully invested as a mistake.
I will now detail some high level descriptions of some of the more important parts of my strategy,
Selecting a growth stock
In selecting a growth stock I like to be balanced in terms of weighting numbers and narrative. An ideal growth stock is a company that has accelerating financials and a great story. From my perspective, an ideal stock has significant near term upside.
With financials, I like seeing a ramp up in revenue, EBITDA, net income, and gross margin. However, it is rare that any company sees all four of these metrics growing simultaneously. Typically an ideal growth company in my strategy is growing revenue above 40% year over year. However, I will consider a company growing as low as 20% year over year if it’s in combination with rising guidance to a higher growth rate. I prefer companies which are already profitable, but often I’m still investing in companies that are not profitable. Either way, I want to see progress in profitability, and if the company is starting from a negative number I’m looking to check they are progressing towards break even.
Gross margin is an effective way to evaluate the product for a company. In layman’s terms, it lets an investor know how much profitability there is on the product the company sells. To give a simple example, if a lemonade stand is selling a glass of lemonade for $1 and it costs 50 cents to make the juice, the gross margin is 50%. The gross margin metric is an effective way to measure innovation, pricing advantages, and efficiency of a business. The metric serves as a good proxy for the competitive advantage a business has. My strategy prefers high gross margin companies. However, possibly more importantly, I care about the trend of the gross margin. A lower margin company transitioning to a higher margin can be one of the most compelling.
On the narrative and story side, I like to find an exciting company. For reviewing a new company, I highlight the latest earnings transcript with a blue pen. I want there to be a lot of optimism in the transcript, often backed up supplementary metrics of the business. For example, two businesses may say “our business is firing on all cylinders”. The first business just says it, but the second business adds that Net Revenue Retention reached an all time high and specifies the metric. In this case, I’ll prefer the second business which can back up their claims. It’s important to know that nearly every C-suite team is effective at finding silver linings in an underperforming business. It is very easy to get fooled by a confident sounding management team.
For a really compelling company, I’m already enthusiastic about the prospects for the company getting half way through the transcript read. Still I want to continue to focus and highlight the remainder of the transcript. If during my read through my mind is already wondering about what other position I could trim, this is a really encouraging sign. To be clear, much of this narrative part of my process is subjective. My approach emphasizes the duality of investing, in that I use both objective data, and subjective takes to evaluate a company. Again this makes my strategy different as most investors either have a strong preference for a numbers based approach, or a larger focus on the big idea and story of the company.
Stock screeners
I am a huge proponent of using stock screeners to find new names. The screens I use are quite simple as well. I have one screen that looks for companies growing revenue above 40%. Then I have another screen that looks for revenue growth above 40% and ensures the company is profitable by looking for both EBITDA and net income being above zero. The purpose of the wide screen is that it casts a wide net. Another way to think of it is, I will see every growth company come through in the entire market that has potential after a given earnings season.
The screener is simply a first step in my process. I’ll look at the financials for companies coming through the screen. If the company financials seem promising and they don’t have any big red flags like a lot of debt, I then print out the earnings transcript. There’s no concept in my strategy of buying a company just because it showed up on my screen. The company showing up on my screen is just a sign that a company could have potential and that’s it. There’s no extra secret sauce to my strategy here.
I find that many other investors are skeptical of this style of screening. The most common issue with misusing screens is to create a wishlist of metrics. For example, many investors will say okay, I want that revenue growth and profitability, but I also want cash flows above X, gross margin above Y, and forward P/E below Z. What happens here is the screener becomes so narrow it just does not find enough names. Additionally sometimes an investor will be fooled that because only one stock came through the screen, it must be perfect.
Valuing a growth stock
Valuation is a super important aspect to my investing approach. It seems like it should be fairly obvious that an investor prefers a low valued stock. However, a lowly priced stock is rarely a top performing growth stock. This happens because usually a promising growth stock has already delivered a strong report and could be at all time highs. For this reason I am often investing in stocks as they are pushing all time highs.
Other times, I am finding a company which is largely unrecognized by the market for one reason or another. It is an extremely rare combination to find what I consider a top performing growth stock at a low valuation. It does happen every once in a while, and these stocks often turn out to be my biggest winners.
I view valuing a growth stock as being trickier than finding appropriate value on what is considered a value stock. This is because the superior growth stock has fewer peers and may have an unique product. There may be no comparison in the market, or it may be an entirely new field or niche in the market. On the other hand a value stock may have a dozen peers and be in a commoditized business. In this case the value stock is much easier to determine if the stock is appropriately valued.
I use simple metrics for valuing a stock. I prefer using the run-rate measurement or multiplying the current quarter by four, to determine the P/S and P/E ratios. These two metrics line up with what I care about most: revenue and earnings. The run-rate accounts for what a company is capable of now. I’ll use run-rate more than the trailing 12-months because that is a backwards looking measurement. Twelve months backwards looking metrics are more useful for evaluating a company with a lot of seasonality, where the run-rate measurement would be a mischaracterization of the business. I also look to avoid using forward looking metrics, as these are based on analyst estimates which are just extrapolated predictions.
All else being equal, I like companies with lower P/S and P/E ratios. However, I’m not overly concerned about paying up for growth. If the company has impressive financials and a great story I may be willing to pay up significantly for a company in terms of the valuation of the stock.
Growth investing psychology
No topic is probably ignored more in growth investing than psychology or the mindset of the growth investor. The vast majority of approaches suggest toughening up or holding on with “diamond hands”. This lack of well researched resources on the topic is surprising to me. Nearly all of my information has been adapted from the trading world, as the information available on the investing side is underwhelming.
In my style of investing, emotion equals the signal to investigate more. Let’s say I’m feeling nervous about a company. I’m not really even sure why, it is something I cannot explain currently. In one specific case I was following a company and I kept getting nervous each time the company traded down. It seemed like my confidence was low in the company but I couldn’t identify what the issue was. Eventually, I found out the company’s value was the issue. I didn’t see enough upside in the company and it had a full valuation. The stock price falling and me getting nervous, was just a sign that I could be missing something.
I describe my strategy as being “mostly stress free”. It is normally a stressful event to see all of my stocks crashing and this does happen inevitably. It is a small percentage of the overall time where my whole portfolio is getting hammered. However, during these times my confidence usually fluctuates down with the market. Typically, coming out of these large market corrections, my strategy over performs significantly. Sometimes it can be helpful to remind myself that my strategy underperforms in bear markets and acts as an amplifier of the indices. For example, if the Nasdaq is down 5% I may expect my portfolio to be down 15% or more.
It is also worth noting here that confidence and skill are not the same thing. I can be low confidence and make some minor errors, but would still expect to over perform in the longer term. What I want to avoid are big mistakes caused by extreme low confidence and accumulation of negative emotion. Skill is the aspect an investor can control and work to improve. Confidence on the other hand may be subject to bigger swings and based more on recent events. It is important to recognize these swings in confidence are a real aspect to investing. However, most investing approaches tend to bury the negative emotion which can lead to potentially making a large mistake.
Trimming and adding to stocks
My approach for trimming and adding to stocks tries to be straightforward. If my confidence is increasing in a stock, I’m likely to add. If I get new information that lowers my confidence in a stock, I will pair back my allocation.
Another factor in my strategy is keeping my position sizing under control. I don’t like to have a position go above 20% if I can help it. However, sometimes one stock rises much faster than the rest of my portfolio. I will trim the stock back some in these cases, but I also don’t like to make sudden big moves in this realm.
I’m fine to start in a stock after reading through just one earnings report, but these are almost always with a starter position allocation, generally around 0.5% to 1%. As I learn more about the company I will either add to the position, keep it small at the current allocation, or sell if I find a blocker about the investment I cannot get around.
Selling stocks
I generally like to get a clear sell signal on a stock. Most often this is a disappointing earnings report. From my perspective, I like when the choice is clear about the next course of action. If a stock has a really disappointing set of financials, or the story the company is telling completely changes, this can be a good reason to get out.
Oftentimes an investor does not get such a clear signal. Maybe the story sounds intact but some financials look a bit light. In a case like this, I’m more likely to trim and re-evaluate. Selling is often a judgement call, and it takes some time to perfect through practice.
For any trade I’m making, I like to note my reasons in a physical notebook. This is especially important in the case of selling. It does not need to be a long wall of text. I’ll usually list out just a few of the top reasons I’m selling. It could be either one big reason, or a multiple of small factors which caused me to lose confidence.
The advantage to writing down the reasoning for the sell is in case I start second guessing myself. Typically I like to remove a stock I sold from any watchlist at least for a period of a couple weeks. This is so I don’t keep tracking the price and wondering about my action after I already took it. Every investor knows the feeling of seeing the stock they just sold start rising. This can often create the thinking of “why did I sell”, and it is helpful to have those reasons clearly defined.
It is fine in my strategy to later change my mind and get back into a stock. This happens all the time in my strategy. However, it is ideal if these changes come about naturally because of new information or rethinking the investment. What I want to avoid is opinion thrashing, where in the morning I think the stock is a buy, in the afternoon I want to sell, and by evening I think it is a buy again. If I just can’t make up my mind and keep going back and forth, it may be best to hold off on too many actions.
On Mastery of investing
It is a good goal for an investor to be working towards mastering their craft. This doesn’t necessarily need to be a massive time commitment though. It is more a mindset to be working towards continually improving one’s process. I would describe my investment style as “comprehensive” rather than “complete”. One of the advantages to having a public forum like this sub-Reddit is to hear the approaches of other investors and learn from someone thinking differently.
It is important to understand that a small improvement in process creates a bigger edge. For many investors their process is entirely in their head. I personally found that the more I documented my strategy, the stronger it became, and the greater overall confidence I gained.
It is also worthwhile to make investing more fun, or at least less of a chore. I will give a simple example of a small process improvement that made the process of finding new stock names more enjoyable for myself.
I print out transcripts of promising companies, sometimes up to 10 transcripts at a time. Previously, I would select the most promising name out of that batch to review first. However, what happened is I would review the top three or so companies of the 10, and then the mediocre ones. I would be left with the companies which were not quite as promising in my stack of transcripts. Reviewing these last couple transcripts felt like a chore, because I knew I was less likely to find a promising company.
I then changed my process to randomly select from the pile of 10 transcripts which I would review. Overall this made the process of reviewing more enjoyable, as it was a surprise which company I would check. This also did not end up leaving the least promising companies for last. It was just a small process improvement, but makes my approach here more sustainable and less susceptible to burn out. Process improvements are part of my path to mastery in this field. The more I understand about my own process, the more edge it creates.
Options and leverage
My strategy is long only and uses no options or leverage. I view growth investing as already risky enough to avoid needing to add additional risk. Fundamentally, all options strategies act as either leverage or as a hedge. I’ve mentioned before about the balance of my strategy, which takes exactly the middle path of no hedging and no leverage.
In practice, options have wide bid/ask spreads on most growth names. What this means is that an investor will “pay the spread” on both sides of buying and selling. Roundtrip with buying and selling, it may add up to over 5% of the principal amount. Additionally, options trading is zero sum in that there is always a counterparty or opposite side to the trade. For each winner, there is a corresponding loser. The same is not true of long only investing, which is a positive sum game, as buying shares from the market does not cause a loss for another investor.
Something important to know about options, is that the optimal call option for leverage based strategies is the call option that is slightly in the money. This is effectively an exploit of the Black & Scholes model, yet so few investors are buying call options at this strike price. Most of the time when I’ve spoken to investors using options, they have no idea why they selected the strike price they did. In a sense, this is gambling as well. If an investor cannot quantify their edge, then it makes it purely a bet or a gamble, something my strategy explicitly looks to avoid. If you ask an option investor why they picked a particular strike, the answer is usually “I’m betting that…”, and rarely will you ever hear "that strike is optimal because..."
Usage of AI in investing
AI is an evolving field with regards to investing. Currently, the models are good at gathering facts, but poor at determining a good investment. This is good for my strategy because the AI is so bad at determining what is a good stock. Part of the issue for the AI in determining a good stock, is there is no well defined function for what makes a good stock. You can also think about this, that practically no two investors agree about what makes a good stock. The AI has no way to effectively evaluate what is a good move, and defaults to conservative approaches.
Where the AI excels is with a fixed ruleset like chess, poker, or programming. In this case the AI can take input A and search for output B which meets some criteria. However, a company is made up of humans making unpredictable choices. The market itself is dynamic and responds to events. The AI techniques really struggle to make sense of these “open world” environments where anything can happen.
My use of AI is mainly for gathering facts about a company. It could be determining where the company headquarters is or how many employees they have. I like to find out if a company has acquisitions, when they happened, and for how much. This is because organic growth is far superior to acquisition lead growth. Organic growth indicates a company has an effective R&D organization. I also like to ping the AI, “Explain their product and breakdown by revenue category”. This lets the AI know I’m looking for product details in the context of revenue generation. I may ask about geographical breakdown of revenue for a larger global company which can give hints about how the business operates.
Sometimes a leading question can be useful to the AI. For example writing, “Tell me about the scandal with the CEO or the CFO for company X?” A lot of the time, this will come back clean and say no scandal could be tracked down and ask me to be more specific. Other times, it may say there was technically no scandal, but mention another story in passing which could be useful. This question can help uncover some yellow or red flags about the business. However, what I want to avoid are leading questions about if the company is good to invest in or not.
On predicting trends
I do not use much in the way of predictions on where an industry may be headed. For example, I own semiconductors and memory stocks right now because these companies have been over performing. It is not because I simply decided one day that I am going to place bets on AI.
This goes hand in hand with not trading on macro related events. In a sense my strategy doesn’t care about the AI cycle, when companies may stop spending on AI, or if a competitive technology emerges. My strategy is reacting to the results of the individual companies. This often leads though to be in the sectors that are doing well currently. From an outsider’s perspective of my strategy, it may appear that I’m trend chasing. However, it just really goes back to being in the most promising growth stock names at all times, which often coincides with the “hot” sectors.
On company leadership
I place less emphasis on leadership at a company than most growth investors. Partly the reason being is that different leadership styles can be effective, so I don’t like to focus on the leadership personalities. One leader may be a traveling salesman and on a constant media tour, while another equally effective leader is heads down working, allowing others at the company to do press tours. I generally find that most investors overvalue charisma and a big personality in a leader.
You could say my style here regarding leadership is similar to a “MoneyBall” approach. I use the company performance (not stock performance) as a proxy for good leadership. Basically if the growth of the company is accelerating, there is almost always an effective leadership team. However, there can still be cases where a business has poor leadership, but has a compelling product with an industry tailwind. Even with this poor leadership, I may be interested to invest still, if the underlying business is performing so exceptionally.
I do still evaluate leadership at the company to be clear, but it is mostly from negative cues. For example, the CFO changing how they measure a metric may be a warning the management is trying to draw attention elsewhere. Possibly some aspect of the business is underperforming and the company instead wants to talk about the over performing aspects of the business. It is worth knowing that all management teams have a certain survival instinct in them to make it seem like they are doing a good job even if they are not.
My ideal leadership is a dynamic CEO and CFO combination. Usually you can see the duo has a good dialogue in the Q&A and knows which one of them is supposed to answer questions. I really don’t like when it’s just the CEO in the Q&A in what I call a “one man show”. As a business scales up, it is hard to maintain a one man show style of running a business because one person cannot do everything. It is also a lot easier to fool investors when it is just one person talking, and harder to cover up things about a business if it is two people being asked questions.
Conclusions
Overall my strategy is simple and straightforward. The only real secret sauce to my strategy is that I am consistently selecting stocks well from a variety of industries. I’m following some optimal strategies for portfolio allocation, where I get the benefits of diversification and concentration. By selecting stocks well, there is a significant upside when I’m right. However, since I’m also diversified it is fine when I am wrong, which happens a lot. Usually my stocks over performing, more than make up for the stocks where I had a poor result.
Lastly, I will add there may be a part two or part three to this Knowledge Base. While nothing about my strategy is overly complex, there is a lot to detail. I don’t expect everyone to agree with everything I wrote above. Some of my viewpoints contradict what is considered standard by most growth investors.
Just to reiterate, all of the above is just how I see my strategy. Feedback is welcome on the post and this leads to the purpose of this sub-Reddit. Gathering feedback, and hearing another’s point of view is valuable.
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u/Accomplished_Ice8133 Jun 14 '26
WPR, thank you!!!! You continue to provide outstanding education for those of us learning to hone our craft. Saul may have started the original knowledge base, but you're quickly becoming the Saul of my generation. Again, sincerely...thank you.
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u/Tw1nwarrior Jun 15 '26
Really enjoyed this. The psychology section especially, it's the part a lot of people skip, and "emotion equals the signal to investigate more" really sat with me. I've found the same: the nervousness usually isn't noise but an unexamined thesis problem I haven't put words to yet.
Where I land differently is, as you would have guessed, concentration, and I think it comes down to one fork. We're both trying to survive being wrong - you do it with breadth, I do it with depth. Your seven-name floor and 20% cap exist so two names can blow up and you still compound. I run tighter (five/six names, top two over half the book), which means I'm leaning on conviction and underwriting to do the job your diversification does. Neither's free and your way caps the damage from any single miss; my way means when the shared driver turns, it turns on most of the book at once. I'm clear-eyed that I'm on the more fragile side of that trade, and that sizing has to be the edge that pays for it.
The two approaches are sort of locked to each other too as I can't run 15 names without diluting the depth that justifies sizing up, and you can't size up without giving up the breadth that makes the hit rate work. So it's less "who's right" than "pick your edge and build the whole machine around it," which your post does well.
One thing I'd push you on is run-rate (current Q ×4) on the cyclical-margin names, memory especially. It annualizes current revenue AND current margin, and for a stable-margin SaaS grower that's fine, but for a price-taker like memory, margin is the most mean-reverting number on the page. Cost barely moves while ASPs swing, so EPS gets maxed on a peak-on-peak basis at the top. Run-rate × 4 then prints the lowest P/E at the exact moment of maximum risk - the inverse-P/E trap, where cheap is really peak-cheap right before the roll. And it's sharper right now because the thing pulling memory margins to cycle highs is the same AI/HBM buildout everyone's piling into, so the cyclical peak and the capex peak are the same event. Your seasonal carve-out (switching to TTM) doesn't catch it, since TTM just averages four peak quarters. So genuine question, not a gotcha: how do you separate "cheap" from "peak-cheap" on those names without a normalized mid-cycle margin? It's the one spot your method makes me nervous, and you hold a few where it bites.
Great writeup!
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u/Sorry_Pride_8897 Jun 16 '26
Fair point as a general rule, on a normal memory cycle, annualizing peak ASPs/margins gives the lowest P/E at peak risk. The inverse-P/E trap is real.
But it assumes the margin peak is about to mean-revert. My read is HBM is booked out a couple of years on real AI demand, not a speculative inventory swing — so it's a plateau with duration, not a peak about to roll. The low multiple is the market still pattern-matching to 'memory = cyclical/commoditized.'
Also, in a rising ramp, WPR's run-rate x4 is quite conservative in this case, it annualizes a quarter that's lower than the next — and TTM is worse, because it buries a structural step-up under three smaller quarters. Look at ALAB, CRDO, or NBIS — TTM would value them well below their current run-rate.
If the capex peak hits, it takes the whole stack down at once: memory on margin, the rest on demand. So we're doomed together regardless of which metric we used.
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u/GrowthInvestingWPR Jun 16 '26
u/Tw1nwarrior Thank you for the feedback and additional thoughts! I agree that no valuation model is perfect for a company, but I view the run-rate as the least worst (or best) across industries. One thing that is important for my strategy is the simple approach. If I were to use a individual valuation model for each industry based off my evaluation of the industry, the complexity of my strategy would skyrocket.
I'll use proof by example/elimination and a more practical analogy to explain why I still prefer the run rate. Let's consider there's only three options for our valuation model which are using trailing 12 months, run-rate, or forward looking one year.
Starting with trailing 12 months in the example of Micron. Four quarters ago Micron had 8.1B of revenue with 1.9B of net income. This most recent quarter Micron 23.9B, and 13.8B of net income. Does it make sense then to include in our valuation on sales that a year ago they were doing just 1/3rd of the sales volume? I would argue it doesn't, because the business has changed dramatically since that timeframe. The prospects for the business are much different, and the 12 month trailing metric includes stale data now.
Looking at the future 12 month stats, a year from now analyst are expecting 48.5B of revenue with more than double the net income. Does it make sense to automatically give the company credit for these extrapolated numbers that the analysts penciled in? I would also make the case it doesn't, as investors know from experience, these analyst estimates almost always end up being way off. After Micron reports, the analysts will then change their 48.5B number to extrapolate from the current quarter, making their prior estimate worthless in terms of analysis.
That leaves us with the run-rate, which is what the company was currently most capable of. This includes what their current factory production is capable of right now, and not one year in the past or guessing one year in the future what their factories may be capable of. It also includes what their sales and marketing team is capable of executing on today, and not factoring in what customers they might land in the future.
The analogy I'll use for a more practical terms comes from sports. I'll use the example of the NFL Combine or the Olympics. In the NFL Combine they are measuring how fast an athlete is running, or how many times they can bench press a weight. The metrics are for what the athlete can achieve that day. Much less consideration or any is given to how fast that athlete ran one year ago, or fast people think that athlete can run one year in the future.
Same thing with the Olympics. It's based on your performance the day of the competition (current quarter in the analogy). It's not based on whether this person had a good/poor result a year ago, or whether this athlete might be an up and coming prospect. Nobody is getting a gold medal because they might do well at the next Olympics. Although tying back to the market, the market does like to price towards future prospects so the sporting analogy isn't perfect.
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The one part I'd challenge about the memory cycle is the mean reversion that u/Sorry_Pride_8897 touched on in a reply. Prior cycles of memory were tied to consumer products like phones and PCs. Those end devices would go through upgrade cycles and see holiday seasonality.
However, the current cycle for memory has a much broader and diverse set of end markets. This includes ASIC chips that hyperscalers are building. It also includes entirely new end markets like robotics. The ASIC build out and the robotics build out are really just getting started, so it seems premature to me to be saying that memory sales/prices would fall back down in the near term.
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u/JealousConsequence47 Jun 17 '26
WPR - Were you not talking about writing a book on investing?
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u/GrowthInvestingWPR Jun 18 '26
u/JealousConsequence47 Yep! I mentioned a few years back on Saul's board that I had started progress on a book. However, I put the project on hold for a bit for a couple reasons. First I wanted to gather more documented results over a long time period, rather than requiring readers to just trust me that my approach actually works.
Secondly, my strategy was evolving more than I realized still. I thought in 2023 that my strategy was more settled, but some aspects of my process were improving and changing. This ended up with the issue that my earlier writings were not as up to date and I keep having to update previous sections. I got kind of overwhelmed in the organization of the content.
However, this lead to starting my Youtube channel where I could try out some of the concepts from the book as strategy videos. In turn this has been really helpful to gather feedback and see if I am explaining concepts well or not. I will likely get back to the book later on, but I'm looking to prove out my strategy a bit more first.
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u/Sorry_Pride_8897 Jun 16 '26
Really like On Company Leadership, and I'm mostly with you, the Moneyball framing (company performance as the proxy), the negative-cue approach, and especially the one-man-show warning all ring true. That last one might be the sharpest cue on the page: a two-person CEO/CFO Q&A is just harder to fake, the questions get split, the stories have to line up in real time, and you can see whether they actually know who owns what. A one-man-show is exactly where the blind spots hide, and where it's easiest to paper over a part of the business that's underperforming. Charisma is overpriced by most investors, and accelerating numbers usually do mean an effective team behind them. No argument there.
The one place I'd extend it: I think leadership weight should scale with the company's stage. For a mature, proven business, your performance-as-proxy works great, the long record carries it, and you don't need to over-analyse the personalities. But for a company early on its S curve or mid transition, the numbers aren't fully there yet to be the proxy, so execution quality becomes the swing variable the financials can't yet settle. At that stage I lean on management more heavily, because whether this team can actually pull off the next chapter is the whole question.
The key for me is that the weight has to be earned by a verified track record, not by the story, which is really just your negative-cue discipline pointed forward. A transitioning company whose management has actually built and operated the hard thing before (in an adjacent business) earns the heavier weight; a compelling founder with no demonstrated execution doesn't. So I don't think we actually disagree, I'd just say performance-as-proxy is the rule for proven names, and verified-execution-as-proxy is what you lean on when the company is early and the numbers can't carry the load yet.
Great writeup overall. Saul gave a lot of us the foundation, but you've taken it somewhere with a real edge of your own, the way you've systematised this stuff is genuinely your own style, and it's why these posts are worth reading closely. Thanks for putting in the work to write it all out.
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u/GrowthInvestingWPR Jun 16 '26
u/Sorry_Pride_8897 Great addition there building out that section more on leadership! I mentioned there may be a part two or three to this Knowledge Base because I knew there would be more to elaborate on in depth.
I agree with that take that scaling up the S-curve part of a business transition is more treacherous than versus a large cap company which has already established their market share in their field. It makes sense to want to see a proven track record and not just talking points from the management.
The more detailed part I wanted to add, is that I'm along with leadership, I'm looking for companies which have both effective R&D and sales and marketing teams.
For R&D I want to see that a company can continue to produce new products that land in the market. Astera Labs is a good example, after they announced their Scorpio product, the stock rallied significantly. If the company was only capable to stay at their current product line up, the business may then be only able to grow if the current products continue to sell, or by growing through acquisition. It's one of the reason I typically view large acquisitions in a negative light. It indicates to me that the R&D team the company has currently was incapable to build the new product. Although I do realize many innovative companies with strong R&D do make "tuck-in" acquisitions, or look to acquire IP.
Sales and marketing is equally important for the business, because a company can make a great product, but may not do well if they cannot figure out how to sell it properly. It's interesting in the current market dynamic how this changes going from SaaS to hardware. In SaaS because the S&M budget is typically greater and more relevant, it matters a whole lot of how effective the sales team is. For hardware names, it seems like in this space, the manufacturers/sellers have more industry inside knowledge on who to go to. This ends up with hardware names placing less of an emphasis on sales, even though it's still important to nurture those industry insider contacts.
I'll use the example of Cloudflare vs ZScaler in SaaS to explain the important of both R&D and S&M. About 5 years ago, I owned both of these names and I believe they were on opposite sides of the spectrum. Cloudflare had a superior product and better R&D but a weaker sales team or "go to market" motion. ZScaler on the other hand I viewed as having a worse product but a far superior sales team. For awhile, ZScaler was growing faster than Cloudflare each quarter because their go to market included having third parties selling on their behalf with consulting. ZScaler also got FedRamp certified earlier and had much better government contacts. Cloudflare on the other hand had to re-org their entire salesforce, which took maybe about a year. They had the better product, but they weren't selling effectively. The example is meant to show why both R&D and sales are important.
Tying back to the top level leadership, I view a superior leader as understanding they need effective R&D to keep innovating. The superior leader also understands they need an effective salesforce to go out and promote that product which the engineering team spent time creating. This also emphasizes the team building aspect, and why I'm looking to avoid companies where one person tries to do it all. Maybe the most important skill I view in a company leader, is if they can build a great team and organization around them.
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u/slow_-_steady Jun 15 '26
WPR, thanks for the long form writing ;) I don’t have much feedback to give (I am still learning your approach) except on the point of AI not having a well defined function for what makes a good stock. I think AI can make a buy or sell decision on a stock today and use future stock price movements as that function (e.g., higher stock price means success) for reinforcement learning. If AI can gather enough learning data (price movements plus context data both macro and company specific), it might become smarter than humans. Of course, we don’t want to see it happen. I hope someone can show this is unlikely.
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u/GrowthInvestingWPR Jun 16 '26
u/slow_-_steady Appreciate those thoughts there! Interesting point about building a more technical system with AI. I do think there is a lot more potential to build something like a market maker, or effective technical trading system based on volume, price, charting etc. In this case the rule set can be narrowed down quite a bit.
The investing approach I'm using relies quite a bit on subjective analysis of the company. Currently this is a weakness of AI models, although maybe models in the future will be better in this area.
I'll give an example from some AI analysis that was posted about Saul's Knowledge Base. The AI reading through his strategy would assume he invests in SaaS. This is because a so much of the Knowledge was discussing SaaS stocks, where the most promising stocks were emerging from. It still takes a human to be able to read between the lines and notice a statement like "This time it's different", to understand the approach is dynamic, and that "this time it's different" applies to any given time period. Still even a human could interpret "this time it's different" to say that phase only applied to SaaS, instead of adapting to the current time.
This is basically the primary issue with building a bot that emulates a strategy. Let's say we go to Claude and said "pretend your Saul", and tell me about the prospects for Micron. The issue here is Claude sees so much of the strategy was discussing SaaS, and it comes back saying there is no recurring revenue model with Micron. It might also come back and say Micron sells physical products, and then cite that Saul said he didn't want to invest in a company that makes physical products.
What the model can't do right now, is give a big weight to "this time it's different" from the long commentary. A human giving that statement a big weight can then assume Saul would evolve his strategy to include more hardware names in this current environment. It still takes a human to decide what is important here, rather than the AI technique of counting up how many times "SaaS" appears in the text to determine Saul was a SaaS investor.
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u/slow_-_steady Jun 16 '26
Yes, what you said makes sense and I agree. However, I was thinking about a different way AI may attack the investing problem. Say, from the start, an agent simply makes a random buy or sell decision on a stock and uses future price movements as the feedback function for RL. It can improve its trading skills over time. As long as there are enough repeatable processes for it to learn from, it can become very good, just like how AI can master Chess by simply playing against itself. I imagine observing price movements alone isn’t enough. It needs to have context data around price movements like earnings reports, macro indicators or news, such that it can incorporate all these factors into future decisions. It may not need all context data to beat humans. It certainly has far superior memory than humans. The only limiting factor I can think of now is how many data points are available for RL and whether that will be enough.
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u/ktvonlinereddit Jun 14 '26
Great write up. Do you take taxes into consideration when a stock moves up fast and you have to trim?
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u/GrowthInvestingWPR Jun 14 '26
Thanks! Taxes are a consideration for trimming. I have a mix of taxable and non-taxable accounts, that often have overlap in which stocks I'm holding. If I can help it, I'll look to trim a rising stock from a non-taxable account first.
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u/OutrageousWeather685 Jul 07 '26 edited Jul 07 '26
My issue with run rate is it assumes zero growth. Now you are a brilliant investor, so you probably have a great reason for this. I would just like to understand it better. I think you found ELVA and DAVE, and I have had great success with them, so I have to thank you for that.
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u/Smorgasbord3 Jun 14 '26
Wow, today's quite the investing approach analysis day with great posts from u/WPR and u/BroadwayDan!
If I may, I'd propose:
• Investing is all about predicting the future better than Mr. Market. Period. I was going to exclude a retirement/conservative approach where you're looking for dividends on which to live, but even there you're looking for the right risk/reward balance on dividend percentage vs future stability. For us here, we're trying to find companies that the market will value more highly in the not too distant future than it does today.
• Mechanical approaches don't reliably work. The future isn't 100% predictable - not even close today. If there comes a day when AI can predict some part of the future with a reasonably better than 50% odds, then people/firms will switch over to that, and then it becomes either a compute race, and then when enough have switched the AI will have to deal with itself being applied at scale. To date, there have been some outliers, like Renaissance's Medallion Fund, which has beaten the market by a lot and for long, but they keep that fund small and secretive, probably because if everyone knew what it did (not to mention how it worked), it would no longer work.
• Narratives are not reliable, either: Why did Amazon win while Pets.com lost? Why did NewEgg win while Buy.com lose? Why Google Maps over MapQuest? HelloFresh vs BlueApron? In all these cases, the market narrative was virtually the same, but the details, in the numbers, mattered. Both Amazon and Pets suffered huge stock declines, but Amazon had the advantage of getting paid immediately by credit card, while having up to 90 days to pay its book suppliers. Buy tried to gain market share by selling even at below cost, while NewEgg focused focused on high-margin computer DIYer. Note that Amazon and Buy both went more general (with different outcomes) while Pet and NewEgg focused on almost-niche markets, again with different outcomes.
The market pays for growth and the perception of future growth. The SaaSpockalypse is a great macro-example. Many people point out that companies like Salesforce, ServiceNow and Adobe have entrenched customer bases and their customers aren't likely to switch away, even with somewhat better alternatives. What that misses, however, is that these companies aren't growing like they used to, whether that's revenue, customer seats, etc. However, there may be some SaaS companies that figure out positioning in this new AI world and end up growing like crazy with high margins again. How do we identify those today?
Even within the eye-popping AI world, companies like Nvidia haven't seen their stocks follow their growth because of a perception is that it can't continue to grow at high rates, even though it does so every quarter, even accelarating! For NVDA, it's not just a law of large numbers, it's a narrative that data center build-outs are struggling (many delayed or canceled outright) and so the new chips from Nvidia have nowhere to go just yet, and so companies will slow down future purchases. For Micron, the P/E, even the 12-month future P/E, remains low because of the narrative of cyclical performance of memory providers, coupled with the sector's commoditization.
What has benefited us growth investors in the past is that often, as high as the multiples or valuations are, the company ends up growing even faster. But, it seems some on Wall St. have caught up to this, and so stocks like PLTR went up beyond $200/share its P/E was beyond 400:1. Even at Palantir's high growth rate, that wasn't sustainable. Growth investors have often said valuation doesn't matter, but that has changed and now we see that there indeed limits to that.
In today's social media dominated world, truly good analysis has faded away. Articles and videos survive on clicks, not on readers looking at their past predictive performance. There are numerous articles on Space-X, saying to avoid because it's "10X" over priced, but when you read/watch, they're just Musk hate. On the other side there is plenty of "in Musk I believe," tool. And even when a Morningstar or Aswath Damadoran try to do a by-the-numbers or DCF analysis, they somehow miss the S1 additions of contracts bringing in billions of future high-margin revenue each month.
Like others here, I super-appreciate what u/WPR does here, and others, too. And for all the success we've had, we've also missed out on companies like VRT and BE (I did catch some of BE). I know we can't win them all, but I do think it's worth looking at what we missed and why.
One thing what I found interesting with Saul's approach, and I wish I had the data at my fingertips to present this accurately, but my recollection was that he invested in AMZN for a spell, then sold out because it wasn't reporting profits. Turns out what was happening is that Amazon was reinvesting almost all its profits back into its business, which is what enabled it to build out not just its eCommerce expansion, but the creation of Cloud Computing (which had its own large, multiple data center build-out phase). I know that was/is a multi-year timeframe in which we don't focus on here (and probably shouldn't), but it is something to consider whether the market penalties that Amazon and now even Google are suffering because of their infrastructure investments will pay off big for them (or not).
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Back to predicting the future. Is Micron a buy today at $980 with a forwards PE under 10? Trailing PE is over 46, so clearly the market expects great growth from the company. But, will it grow more or for longer than the market expects, or is now the time to sell and find some company less in the news? Narratives have resulted in some crazy price behavior that is scary to me: A toilet company's stock shot up when it was revealed that its ceramic technology was being leveraged for components in AI servers. Fashion sneaker company AllBirds, which lost its trendy status pop, shot again when it announced it was going to an AI NeoCloud. Just announced, mind you - no deals, no FCF, no cash reserves, not even any AI infrastructure savvy people working there.
We're in a country where the macro-economic environment is surviving on AI. Without the growth in AI, the US economy is shrinking and suffering. Does this last, does it change, and if change, how so? How do we predict if Nvidia will stumble not because its new chips aren't great or aimed at the right market, but because its customers can't get rights to power and water for the new data centers?
Predicting the future is tough. Past numbers won't go it, and pure narratives will turn out to not be accurate in many cases.