Alpha, Excellence, and Video Games?
The title does indeed say video games. Figured I’d start the new year off right with some weirdness. Hopefully the article below is at least vaguely interesting and justifies very serious finance professionals reading an article referring to the activities their kids are likely enjoying currently while off from school. Anyways, on to the article with a brief subscription plug if you’re so inclined (premium entirely optional)
Intro:
My background to getting into finance is quite non-traditional, so it is probably no surprise that my opinions are as well. One of those opinions is that it is criminally under discussed what excellence is and how it is achieved in a more general sense.
Much of financial education content is specifically limited to the realm of better understanding finance. As an example, if someone asks “how do I get good at picking stocks” the answer is generally to point them specifically to material on picking stocks. In my case it was books on securities analysis and valuation, Buffett letters, assorted fund stock write ups and letters, etc. All of this is good content to be clear, but it’s hard for education to go beyond this point. We have CFA exams and analyst courses and numerous other educational avenues, all great for learning standards in the space, but what educational avenues are there for going above and beyond?
To set expectations, due to the nature of markets it’s basically impossible to have a course that teaches a secret to excellence which lasts over a durable time frame just due to the nature of best practices being constantly adapted. Say you found a great purely technical trade, BTC goes up 1% on Tuesdays at 6:52am or something. If you tell everyone about this, they buy ahead of it, now your trade equalizes out to neutral expected value. Rather self explanatory of course. Which also creates the natural question “if it just gets arbitraged in the end then what’s your point?”
The point is based on a couple theories that I’d go so far as to say are basically axioms of markets:
Markets get more efficient over time
Inefficiencies will always exist to some degree
Specific inefficiencies are temporary and will vary in scope
It is impossible to “prove” anything is an inefficiency without solving it
“Prove” here basically means if you can show it is inefficient, the market will adapt at some point.
All of these have numerous implications but to stay on topic my point is that while inefficiencies always get solved eventually, it is still possible to find and exploit inefficiencies. Said inefficiencies cannot be easily defined or agreed upon by nature, so the key question is how we find them? This is of course basically saying “how do we get alpha” which is sort of the entire job of a stock picker, so it is not as if an attempt isn’t being made. It’s just interesting that generally we don’t start very general in how “alpha” is achieved, we focus on very specific financial information, which is why education is specifically finance oriented as mentioned above. The result is frequently people learning what I once saw referred to as “card tricks” of investing, wherein they take an example of what worked for someone at some point, and continually try to apply it regardless of changing circumstance or a lack of support from first principles.
Some real life examples:
“X is cheap because the PE is 10 and I’ve been told a reasonable PE is 20 when rates are 4-5%”
Any sort of technical analysis. If any sort of chart extrapolation is possible, why would a computer not do it better?
Also applies to pretty much anything I’d bucket as “shitty quant knockoff investing” like spreads on large caps vs small caps, multiple arbitrage between competitors, trailing average multiples, etc. Basically if you aren’t answering a “why” and are just assuming numbers revert to something else, it’s probably shitty quant knockoff investing. There are lots of people with lots of money and time very interested in solving that problem with far more resources than you will ever dream of.
This is why you’ll frequently see threads or other content popping up saying the most important information in a stock pitch is the “why” and how the poster learned this at some point in their investing journey. The “numbers” that are obvious are basically never going to be an edge, yet the myth perpetuates. As always the key question when a lot of people believe something you think is false, is “why”.
In this case it’s likely mathematical. If you were to throw a dart at a board of the S&P 500 on January 1st of 2023 and held til the end of the year, your expected return would be vaguely 11.5% YTD. You also could have hit Nvidia and been up 236% or Enphase Energy and been down 50%. While this is rather elementary the obvious point is that expected value can vary drastically from outcomes. Many popular “technicals” posters will stumble into a good trade here and there and use that as leverage to gain an audience. The same can happen with pro investors at funds as well. “We went levered long META 0.00%↑ and NVDA 0.00%↑ due to multiples compressing below historical averages” or something of that nature. Now of course what they say isn’t always what they mean and in many cases the actual thought process is dumbed down for marketability, but that isn’t the point of this article. The point of this article is what do you, the individual, do in the financial information economy, to try and garner an edge while much of the popular and marketable discussions are around irrelevant factors?
And that’s where we transition to video games.
Video Games and Excellence:
You have likely heard at some point in your life something along the lines of “everything’s the same”. In this case, I believe video games and finance and pretty much any competitive activity are all quite similar at the core. In very simple terms you have a set of background information and prior beliefs, then must leverage those to execute on a task in a given timeframe. Whether that is beating your son at Mario Kart on Christmas day or beating the indices by >10,000bps is of minimal consequence (besides perhaps the competitive pool unless your son is quite good at Mario Kart).
We are using video games here because I think they exhibit a few unique and important characteristics compared to other competitive ventures. Some examples:
Video game outcomes are largely objective in nature, you either “win” or “lose”.
There are a multitude of single player games or games where individual performance is strictly measured, eliminating arguments around the effects of other participants.
There tends to be very good record keeping at the high end. There have been community leaderboards and such for decades with decently well audited results. Finding similar data for most hobbies is prohibitive or impossible.
Many video games are very low in randomness or iteration is so high as to minimize significant impact on results.
As a result of the above, video game information economies are quite robust which makes analysis more doable even as an external observer
Since the goal is to try and understand what enables excellence in a more general sense, I figure that video games are perhaps one of the best places to analyze. In finance spaces it can be hard to disentangle the actual reasonings behind trades, if there was edge versus simply positive variance, there is less data on outcomes and the paths that lead individuals to said outcomes, incentives are to be less open with information, etc.
Now in video games of course there are not direct “markets” frequently, the competition tends to be in far simpler systems. Thus if you are not inclined to believe that all competition is vaguely similar in nature despite differing outputs, you can freely stop reading and save yourself some time. If you can entertain the idea, then let’s dive into how excellence is achieved, which hopefully provides some insight into how alpha is achieved.
How To Be Excellent:
Generally when discussing this topic the focus tends to be on mentality in finance. Stuff like don’t be delusional, don’t lose control of your emotions, don’t be stupid, etc. While all true and good ideas, imagine if you were say playing a highschool sport, got pretty good, want to play D1 in college, so you hire a coach. The coach just tells you “don’t be stupid”. The typical response would be “duh” followed by finding a new coach (of course actually not being stupid and actually not being delusional can be very difficult, but the sentiment is practically universally agreed upon).
Part of the issue with finance is that information is never perfect and feedback tends to take quite awhile. It’s much harder to know if you are delusional because there isn’t an immediate objective feedback loop on whether you’re right or wrong. Additionally given the variance involved you can look totally right while being totally wrong. So this is a topic worth plenty of discussion, however I don’t believe I can meaningfully add to it at this time, so we’re going to assume you figure that out on your own somehow and focus on what to do assuming you do have a solid mental baseline.
To start with, I believe there are basically two forms of edge, information edge and execution edge. They are rather self exomplantory, but we can try some examples:
Info Edge:
Finance Version: You have alternative data that indicates what earnings will be next quarter
Video Game Version: You know a shortcut in the level which allows you to complete it faster.
Execution Edge:
Finance Version: Your connection speed is faster than others so you can queue orders quicker.
Video Game Version: You have a quicker reaction speed than your opponents.
As we can see the video game versions are perhaps slightly less arcane. If you have a shortcut you have a shortcut. If you have alternative data who knows if it’s even correct or relevant. Anyways, let’s break down each.
Execution Edge:
Before diving in, we can just preface with the fact you really really really shouldn’t try to rely on execution edge, especially as an individual. Hopefully this highlights why.
A key point is that video games have a far lower incentive for perfect execution than markets. By standard economic utility models it’s probably a significant loss for anyone to dedicate themelves to playing a game perfectly, whereas in finance the reward for significant alpha is significant capital. Yet the level of competitiveness we see in execution is rather staggering.
A great example is Super Mario Bro’s, a 1985 platformer game from Nintendo featuring world famous Mario. I dare say this exemplifies the key market theories I outlined at the start.
Markets get more efficient over time
The most recent update to the world record time for this game was in September of this year, nearly 40 years after release.
Inefficiencies will always exist to some degree
Despite millions of hours of play and hundreds or thousands of very serious speed run attempts, with perfect inputs known, the best run is still not technically perfect.
Specific inefficiencies are temporary and will vary in scope.
The remaining possible time save of 0.35s is quite minimal compared to overall run time. Some previous findings have completely changed how runs are done (to avoid too much specificity, you can see the speed runner walk through a wall at one point which saved significant time).
It is impossible to “prove” anything is an inefficiency without solving it
Once improvements are found to be repeatable, they become the baseline expectation to be competitive. In this case the “glitch” that allows walking through a wall became standard practice for all of these players shortly after being found.
To skip over all the technical jargon, the player with the most recent record completed the game in 4:54.631 to claim the world record. He had first claimed a world record in 2020 with a time of 4:55.430. Effectively he spent 3 years or so of consistent play, hundreds or thousands of hours of attempts, to shave off less than 1 second from a ~300 second run. An improvement of 0.3% with 3 years of effort, that is still worse than computers by a full 0.6 seconds, so maybe in 3 more years he can get it?
Spending thousands of hours for a fraction of a percent improvement to still lose to a computer in a system that is drastically less competitive than financial markets. And you want to generate significant alpha via execution advantage? There’s a reason quant funds are levered multiple times over, and I promise you the gap between a quant fund and an individual is much larger than even this speed runner and the average Mario player.
Thankfully for all of us, execution advantage is not the only way to generate alpha.
Info Advantage:
Realistically I believe most “alpha” falls into Info Advantage. As a disclaimer, some may differentiate between info and judgement, as they believe them to be different things. Personally I view info and judgement as one in the same. The way you parse any new info is based upon your previous experiences and some innate traits. It’s pretty similar to “taste” in art or design wherein you just kind of get a feel for what’s good and not good, you also get a taste for what info is true or not, who is lying or not, etc. If you’d like a good breakdown on this I highly recommend this video but it’s far too much to touch on here, so we’ll just condense judgement into “info advantage”.
Anyways, it’s pretty easy to see that computers are just far better at anything to do with execution. They can take in vastly more data and have consistent output. Probably the most popular asset management draw recently has been pod funds which effectively toss a bunch of math on top of human portfolio managers and rather consistently perform quite well despite high talent turnover. If the best market neutral “pure alpha” funds are just highly levered math frameworks, you’d think that sort of proves the point.
The same is sort of trivially true in video games and increasingly more systems. Chess players at this point learn from the computers, not the other way around. Computers in video games can just be frame perfect and have an entire speed running community dedicated to them called TAS (Tool Assisted Speedrun). Said runs are basically impossible to beat under most circumstances as inevitably humans trying to mimic optimal computer pathing do not have the consistency nor the computational power to outperform.
The most interesting cases are where humans end up beating computers and why they do so. A couple of notable excerpts:
From August 13 to 21, 2007, the fastest unassisted speedrun of Pokémon Blue was 4 minutes faster than the best TAS due to a new trick that allowed walking through walls.
From January 12, 2020, the fastest unassisted speedrun of Donkey Kong Country was 810 milliseconds faster than the best TAS due to a new trick that allowed Diddy Kong to grab a DK barrel and the throw the DK barrel near a hidden barrel at the beginning of the last level, which skips the entire last level and level-finish animation as well.
The technicalities of these tricks are rather irrelevant, what’s important is that despite the possibility of completely frame perfect computer assisted runs, it was still possible to beat them via the discovery of new methodologies. I believe this lesson is absolutely true for investing and just about everything else as well. Computers are very very very good at doing things people already know. Buying stocks at a 5x PE? Buying stocks at certain spreads on peer valuations? Anything concrete you will absolutely lose to computers on average, and thus computers manage large sums of capital, and thus you have far less opportunity. For an individual, “alpha” comes from finding the new and weird behavior that computers are not yet trained on.
Naturally this is of course true and you see allusions to it rather frequently. “Be contrarian”, “undiscovered opportunities”, “turn over lots of rocks”, etc. This doesn’t really get into the “how” though, which is frustratingly elusive. Sadly it is also extremely hard to describe, hence me writing and article trying to use video games as a metaphor.
So how do some of these players find things that are new and create alpha? What is that process like? That’s the real question we need to be answering. It isn’t magic of course, they didn’t simply divine it one day. In essence I believe you achieve excellence via the following multi-step process:
Basic Mastery
Refining
Existing
Refining
Inspiration
Refining
Hypothesizing
Refining
Experimenting
Refining
Planning
Refining
Execution
Refining
Testing
Refining
We’ll get into detail on what each of these means shortly, but I do truly mean multi-step process. Each step is important in it’s own right and a good process must be in place to facilitate the maximum output from each activity. As an example, it will be hard to experiment with game mechanics if you don’t own the game. It will be very easy to experiment with game mechanics if you can play it 1 frame at a time to slowly generate perfect inputs, which is how many TAS are created. The same will be true for finance. Excel or some other such tool makes it drastically easier to analyze companies than it was 70 years ago pre internet. If you are not trying to create alpha at every step in the process, how will you generate alpha? Imagine having to hand draw out every model and hand calculate, you’d never accomplish anything. Look for excellence at every step in your process.
Anyways:
Basic Mastery:
In any context basic mastery is going to be understanding what game you are even playing and how to play it. If you don’t have 10 fingers maybe a game requiring 10 fingers will be difficult or impossible. Similarly, if you are trying to analyze a Chinese A share from Manhattan while not speaking Chinese, you may have some difficulty. An important thing to realize is that there are lots and lots of people on this planet that spend lots and lots of time doing things. Trying to spin up some activity for the first time in an afternoon and expecting to be good at it is basically an impossible endeavor. The baseline of performance and understanding is generally pretty high.
A popular finance context: No you probably can’t guess the future spot price of oil from your apartment in NYC so it probably isn’t worth your time and attention because anyone that good guess that consistently would rather quickly make billions arbitraging that market. Hence you are not understanding the game your playing nor how to play it if you are in the stock picking business and spending a large amount of time debating oil prices.
As a stock picker your job is essentially to understand businesses and what drives their performance. That will have numerous sublayers such as your math and modeling skills, your ability to understand people, your understanding of game theory, your knowledge of the market they operate within, etc. Can you meet the bar for all of these hurdles? Each question can go extremely deep, say just headcount.
What’s the current headcount? How is that distributed?
How much does headcount need to grow to grow the business by 10%? 20%? 100%?
How quickly can they onboard headcount?
How quickly can they get rid of headcount?
Will headcount growth or shrinkage be disproportionate based on department?
How will the business culturally handle headcount growth?
What will the impact be of headcount growth on end consumers?
What is everyone even working on? Is it reasonable?
How much are people getting paid? Is it reasonable?
What is headcount retention like?
You can go quite deep on this, and the answers aren’t always transparent, nor do they always matter! Do I care what the average salary is for employees at Google? You tell me, Chris Hohn obviously cares, so why does he care? Will it change? Can it change?
Basic mastery here can probably be best summarized as achieving good baseline analytical skills and cultivating a mindset that is dedicated towards finding out the important variables that drive business performance. Get good at asking questions, get good at reading people, get good at answering questions, etc. How that plays out will depend on your judgement of the situation. Once you think you’re somewhat there, refine refine refine refine. Any time anything isn’t how it went in your head, reflect on it.
Existing
Existing is probably the closest I’ll come to behavioral discourse in this article. Some people are way too caught up in constantly digging for ideas. People are generally pretty efficient, otherwise the EMH would not be supported so broadly and capitalism itself would likely be a colossal failure compared to communism and not the other way around. This phenomena that people vaguely know what they’re doing better than some random “expert” has replicated quite consistently, so I strongly caution against having ideas. Ideas should be a rare thing and most of what you do should probably just be mimicry.
As an example, will you gain alpha if you spend every waking hour trying to draw lines on a chart in a way that will outperform quant funds? Probably not, but you can easily convince yourself of that if you get a big head.
A good way to think about this is to try and be somewhat scientific with how you come across good ideas. What were you doing when you got that spark? A lot of times the answer is “nothing” or “walking” or “talking” or some other such random activity. There’s a rather popular phenomena in sports of just playing better after taking a break. It’s probably worth some consideration why that is.
If the think about info advantage and how that coincides with “judgement” we can think about how judgement is formed. It’s rather hard for it to be anything other than the sum of your past experiences overlaid onto your “baseline” innate traits. I imagine it would be rather difficult to have variant judgement if existence is not varied and made the most of. Being present in the moment and thinking about things will likely serve you better than never thinking about anything and autopiloting.
It’s a hard idea to pin down and there’s been much penned on it already via self help and meditation and emotional control and nature vs nurture and far more. At the end of the day,, you need to be somewhat thoughtful with the information you take in, take in quite a bit of information, and consider why things are the way they are, while still respecting that people are pretty efficient. Good luck!
Inspiration:
How do we get inspired? This plays heavily into existing clearly. There really just isn’t a good answer, as if you could simply become inspired formulaically then progress would be somewhat hilariously exponential. To me the key seems to be thoughtful existence and a good baseline understanding of what the environment is sometimes leads to inspiration. In a video game context it can be hilariously simple. A player mimics the current best players to become quite good, then finds something quirky.
In the above example where an exploit was found to walk through walls in the game Pokemon Blue, it’s actually somewhat formulaic how these things are found and it has nothing to do with how well you play the video game! To avoid being too technical, the game basically expects certain inputs, and produces weird outcomes when unexpected inputs happen. It’s more of a computer science question and figuring out the technical constraints of a game cartridge code made in the 90’s. You can create a rather neat little chain of how the exploit to beat the computer came to be:
Game cartridges are small and computing in the 90s was quite limited, so they made do with not fully fleshed out QA that could not be patched afterwards due to a lack of internet connection.
People figured out how to emulate game cartridges on computers which allowed for a better testing environment.
A particular interaction was known about that forced the player character to move and changed what buttons were usable at any given time.
Efforts were made to move the player character in unintended ways.
Specific inputs were found that overwrote memory values which controlled which buttons were usable at any given time, allowing walking through walls, thus creating a new optimal path.
An oversimplification, but that’s the gist. Essentially due to a much better testing environment and some tinkering by individuals with informed priors that forced movement could be whacky in overriding what buttons are able to be pressed, a new route was found, thus alpha. Finance will follow a pretty similar path.
A rather basic example are shitty algorithmic trading tools. As it so happens from ~1980 to the 2000’s if you simply sold stocks and bought bounds when they went down a bit more than usual, then did the reverse when stocks started to appreciate, you drastically outperformed the market. Of course this was due to a far less competitive quant environment mixed with falling interest rates that led to bonds just perpetually increasing. That relationship of course caused some issues in 2021 and 2022 with severe interest rate hikes leading to bailouts for lazy pension funds and the like. At it’s core however, if you were astute in the 80’s and for whatever reason figured out what future interest rate policy would look like and the relationships between bonds and stocks, you would have done quite well!
Finance of course is going to have a less than perfect information environment so it isn’t as guaranteed an inspiration is actually good inspiration. It is however still testable over longer time frames and by first principles, which we’ll get to below.
Hypothesizing
Naturally inspiration is worth nothing if it doesn’t become actionable. Hypothesizing is the process of actually turning an inspiration into an idea. CDLX and BLND somewhat fit into these buckets for me - I have had the general thesis that bank data is worth a lot and banks want to digitalize and provide more consumer value for awhile now. That idea on it’s own of course is not actionable, but looking for businesses that fit that trend is actionable. By no means is it guaranteed those ideas work out, but at least they are actionable ideas and we can go further in the process.
This is probably the simplest step of all really, but the essence of the step is that you need to turn your inspiration into something actionable via attaching some ways it can be expressed. What’s the best way to express the idea? What are key variables to tell if the idea is correct? If it’s incorrect? Establishing some guidelines beforehand is generally a good idea.
As an example say you randomly came to the conclusion that rates would go down in 2024. What would be the best way to express that trade? What indicators point to you being right or not? Unemployment? Inflation? Do you buy homebuilders? BLND? CVNA? Banks? Treasuries?
In my opinion many people are too focused on single companies when sometimes your same thesis is better expressed elsewhere, but that’s just my hypothesis.
Experimenting:
The key to figuring out if a hypothesis is worth anything is experimenting. For this one we can stick with just finance since it’s rather simple. Say you have a thesis that a company is a good investment. What are the key variables that will drive that investments performance? How can you be confident they are correct. Often times as a minority shareholder you have no direct control over the company, so experimenting directly can be hard, but the idea still applies.
CVNA is a decent example - when researching the company I did numerous little tests of their product to try and prove or disprove different ideas. There was a plethora of fake headlines that they were say faking vehicle sales or something along those lines. It was rather easy to actually go and see what was happening with individual units to prove that no, nothing fishy was going on with vehicle sales and the product was actually quite good. It turns out the digital first car company had a fully superior digital car buying product than the physical retailers like KMX.
With some companies it’s as simple as just going to visit. Hindenburg for example published a short report a few months ago on Tingo Group, TIO, for being a fraud. They came to this conclusion based on physical trips to supposed Tingo vendors and partners with non-existent offices and the like. This is a pretty clean example of inspiration>experiment pipeline providing variant results.
There are numerous ways to experiment, alt data, site visits, consumer surveys, product tests, etc. Determining the key variables and how to test them well is key to variant performance. If your data is bad or your interpretation is bad, then it’s possible nothing else matters. The key here is you probably need to keep asking questions until you actually understand, achieve “Basic Mastery” of alt data, of consumer surveys, of statistics, site visits, etc. This is where “circle of competence” discourse tends to lead to as a shortcut. It’s hard to invest in something when you can’t comprehend how to experiment with your hypothesis.
Planning:
Planning is pretty broad, and admittedly all of this is pretty broad. By the time we get here we in theory have an idea we’ve tinkered with enough to have some confidence in. You can think of it mainly as setting yourself up for the trial by fire. For the game analogy in some cases it’s pretty simple. You’ve tested all your theories and mechanics, now put together a plan tying it all together step by step. Part of this will involve some reflection on how to best handle your hypothesis. You’ve figured out how to walk through walls before anyone else, what do you do with it to be #1?
For finance you may notice in some theses or pitches the author includes possible future scenarios and their pre determined reactions. “If earnings doesn’t grow 15% next year I need to revisit my thesis”. “If this business segment is not growing topline 30% I need to revisit my thesis”, etc. What does revisiting the thesis look like? What kind of variables could come up that you allow for thesis creep? CVNA is a good example here. We needed AEBITDA to improve when I was buying in 2022, but I wasn’t exactly sure how that was going to be accomplished. I believed it was going to be from cost reductions primarily as they exited unprofitable markets which could be done in theory rather quickly compared to growing into those markets.
On the flipside, CVNA actually invested more heavily into ramping ADESA and in progress IRC’s along coastal markets such as the North East, PNW, and California, then ramped those markets to profitability with price taking and convenience reductions. If my plan had been “costs come down or I sell” which is actually quite common by those looking to avoid any semblance of thesis creep, I would have missed out on the >1,000% appreciation in 2023.
There is never going to be an ahead of time 100% correct plan, but having some semblance of an idea how you are going to handle things is likely in your best interest. At the risk of sounding cliche this should allow for better emotional detachment and logical response, which is key.
Imagine being a pro athlete and having no plan or contingencies. You’d probably get fired pretty quick.
Execution:
Self explanatory. You’ve had a stroke of genius, experimented to try and prove a hypothesis, planned your actions, go execute.
Buy your stock, play your game, etc. All that matters is you’re a guy in the arena trying things (or at least you can tell yourself that if it goes poorly).
Testing:
Testing here is different than experimenting. The terminology could probably use some work, but the idea is basically seeing if the cohesive idea works live and continuously trying to break it as new information arises. In our video game examples above, once you find a new trick and execute on it, you have to keep testing out new theories or you’ll just lose to someone else eventually. All alpha is temporary after all. Many of the examples I’ve used are relatively simple games older than most Twitter users yet they’re still being innovated on.
From a stock picking POV you can think of this as continuing to read earnings and expert calls and new alt data and new management interviews and playing with the math in your sheets and going through redlined 10-K’s looking for anything of signal. Calling management, calling competitors, figuring out what matters, asking about what matters, getting shitty answers about what matters, always asking “why”.
These reasons are why you’ll see investors say their edge is curiosity or how much they love stocks and business or something along those lines. They can’t help themselves from trying to solve the business puzzle.
Bringing It All Together:
While I’ve said a lot of words, I believe the overarching point is rather simple.
Always be curious in every facet of your life
Always reflect on how you can be better at everything you do
Incessantly refine every facet of your investing process from idea generation to diligence to purchasing to updating info, etc.
Hopefully this article was remotely helpful, I rather frequently use extremes and analogies to try and illustrate what happens at the edges of thought experiments to gauge what happens in the middle. This can be confusing so I don’t hold it against you if this article was a waste of your time. I also would add a disclaimer that by no means do I believe myself an exceptional investor, yet. Hopefully I’m on the path there, but it’s hard to say until it happens. I do believe I’ve achieved alpha and excellence in other pursuits, which hopefully informs learning this one.
Feel free to subscribe if any of that made any sense or was remotely compelling. Cheers.
The edge against funds and institutions is that retail doesn't have any mandates.
They don't have to diversify, hedge, allocate certain capital to sectors, etc.