Some off-the-cuff thoughts from 30,000 feet (both literally and figuratively, via coast-to-coast plane trip)…
There is a guy named Eli Goldratt whose ideas I like a lot. Goldratt is a sort of industrial management theorist. His specialty (from what I understand) is lean manufacturing – greatly increasing the efficiency of factories and businesses with high turnover. Trading has little to do with the physical making of “stuff,” but numerous parallels still apply.
Goldratt’s main idea is the “Theory of Constraints,” or TOC. The gist of TOC (as I understand it) is that a chain of processes is only as strong as its weakest link, and thus the best way to improve performance – and move closer to a goal — is to find and improve the key constraint, or most glaring area of weakness in the chain.
You do this over and over again, making the chain stronger each time, and with each cycle the potential for growth improves.
(This idea is also reflected in something called Liebig’s Law, which roughly observes that growth is bottlenecked not by the total amount of resources, but the “limiting factor” or scarcest resource relative to immediate need.)
A second critical realization of TOC is that, to maximize the whole, you cannot blindly maximize each part. What this means is that, to get the overall best outcome for a whole that is more than the sum of its parts, you have to consider less-than-best outcomes (tradeoffs) in certain individual areas.
In other words, a composite best result – the best net performance on the whole — requires a mix of tradeoffs due to the real world constraints of scarce resource allocation.
This is important because of the widespread misguided tendency to focus on local maximums. (Goldratt wrote a teaching story called “The Goal” to pound this point home.)
In a factory, this mistake shows up as each station manager seeking to maximize output on a micro level, without thinking about (or even being aware of) what levels of inventory and production make the most sense for the plant.
Because all the factory stations (i.e. pieces of the chain) are connected and work together, it is better to vary the capacity outputs of each station in accordance with strategic goals. The alternative to this is wasted effort and investment.
If, for example, the guy at station #1 consistently produces more widget components than the guy at station #2 can use, there is a constant surplus. The extra components produced by #1 thus wind up piling up in boxes. That surplus is a waste of material, a waste of storage space (as the excess has to be inventoried), and a waste of energy and machinery wear-and-tear.
It would be better all around if station #1 only produced exactly as many components as station #2 could use at any given time – even if that meant running station #1 at a lower capacity than it can handle.
Alternatively, one could look at station #2 as the “constraint,” or weak link in this particular chain example, with an eye for speeding things up. Assuming no larger problems, the highest ROI in this example would come from improving output at station #2.
The local maximum problem also shows up in bigger and hairier form at the whole company level, when various division or department heads focus on maximizing performance in their narrow and specific domain, rather than determining what cooperative actions are best for the organization as a whole. When the parts don’t flow together as seamlessly as they should, expensive waste and friction are created.
A simple example of this is an advertising company where the sales team generates more business than the production team can handle without getting jammed up, or a software company where the engineers crank out all kinds of cool features the customers don’t want.
In both cases, the sales people and the engineers can say “What’s the problem, we are doing a great job – isn’t this exactly what we are supposed to do?” But the mismatches can create serious problems — and eventually destroy profitability.
And lest you think this is boring industrial stuff, consider how it applies at the life level too. We all have mismatches in our own productive lives, where too much effort goes into one area of focus while another is left lacking – to the detriment of overall results.
It even applies to leisure pursuits and quality of life considerations. “Jack Sparrow,” to give a personal example, has at least four loves beyond trading and markets: Philosophy, snowboarding, poker, and the company of beautiful women (not necessarily in that order).
In order to live the best life, then – to optimize life on the whole – the “local maximum” problem once again applies. Even extra-curricular pursuits have to be balanced for the best composite result. Trying to “max out” any one of the four on a constant basis is an inferior proposition from a best life perspective, because time and energy are limited. “Too much” in one area becomes “not enough” in another.
So the goal, again, is to figure out the optimal allocation to all four – which is not equally distributed, and not static but dynamic over time. And one can also think about constraints in terms of personal life improvement, i.e. “What is the single biggest change or improvement I can focus on right now to enhance quality of life?”
In markets, a simple example of the “local maximum” problem is pure fundamental investors’ tendency to focus on doing the “best” research possible, i.e. digging too deep into a company and expending energy on minutia.
The problem with “digging too deep” is that time and energy are incredibly scarce resources, with excess information adding to superfluous confidence levels, but not actually improving the decision-making process, just as a warehouse full of surplus merchandise does nothing to improve the ROI of a department store.
(Of course, not digging deep enough – spreading one’s self too thin over a large number of pointlessly shallow wells – is highly problematic too.)
Getting back to the investor example: Beyond the threshold at which a justifiable decision to invest has been made, the habit of “too much digging” suffers from diminishing returns on time and energy investment.
And if extra digging results in a level of confidence that is inappropriate in comparison to historical probabilities – if extra work merely fuels overconfidence in the trade – then the extra time and energy expenditure may actually have a negative impact on returns. Zero dollars per hour goes to minus dollars per hour, especially if important areas are left lacking. (Think how many narrow-focus value investors regretted neglecting the macro in 2008.)
This means that accepting a less-than-local maximum in certain areas – learning to dig “just deep enough,” with a logical margin of safety in terms of effort expended – allows for freed-up time and energy resources to be allocated to other, more ROI-worthy parts of the chain, creating a more efficient result on the whole.
Of course, the above assumes thoughtful calculation as to what the ideal amount of effort actually is. Because remember, it is never just a question of “Does what I am doing right now make sense.” Given limited resources, it is also a question of, “Is there another area of focus with a higher rate of return?”
(All mentions of return denote risk-adjusted returns, by the way, but that should almost go without saying.)
Goldratt’s Theory of Constraints also speaks to why great traders, great managers and great business leaders tend to be innovative and observant as a general rule. In contrast, being thick-headed or short-sighted is a real handicap in life – it blinds you to the powerful cumulative impact of myriad small decisions over time. The challenge of maximizing total performance in a complex competitive environment almost invariably requires accepting a series of tradeoffs… and those tradeoffs never sit still.
Within a linked group of processes and R&D opportunities, cost of input calculations and high-ROI areas of focus can shift on a regular basis — especially if you are being proactive. Sometimes your key constraint is in one area; sometimes it migrates to another. “What should I be focused on? Where can I get the most growth bang-for-buck?”
Because it moves, you constantly target and track your constraint – like a falcon tracking a rabbit.
This is as true for a one-man trading operation as it is for a large and diversified shop. If you are diligent and competitive, there are always exciting opportunities for improvement. You are constantly forced to make the most of what you have: Unlimited resources are never available to anyone, and total ROI is always the top consideration.
In the long run, then, individuals who cannot think about their constraints creatively, competitively and even a little bit obsessively wind up falling far behind those who can. (This helps explains the logic behind the expression: “Bet the jockey, not the horse.”)
How does this apply in the context of trading specifically? As Mercenaries, our ultimate goal is to manage hundreds of millions. (We haven’t yet decided whether billions are in the cards.) This is not a materialistic goal – tacking an extra zero onto AUM here and there won’t change our lifestyles that much – but getting there is a fun and interesting challenge.
Pure trading can be an absolute blast, but so is the accelerated growth path that creates a constant flow of new experiments, new challenges and new opportunities. We’re excited about taking our bread and butter methodology, as executed in the Live Feed, and using accumulated profits to carefully expand into additional strategies.
We also look forward to hiring a new generation of PhD candidates – Poor, Hungry and Driven – and turning them into world-class traders and analysts over time, inviting others in to help us grow. And of course, we’ll continue developing Mercenary Trader into a powerful and unique “trader’s hub,” accessible to pros, semi-pros, and aspiring novices alike.
As we progress along this path, the Theory of Constraints will continue to provide strategic guidance and tactical food for thought. Maybe it could do the same for you…