“One class of rules of thumb is one-reason decision making: Find the most important reason and ignore the rest.“ Gerd Gigerenzer in “Risk Savvy“
We have entered a world where good investment decision making has become increasingly difficult as future uncertainty has skyrocketed unexpectedly. The world economy has been put into a coma while monetary and fiscal stimulus is trying to keep the patient alive with staggering amounts of money the like of which we have never witnessed before. This is “the irresistible force meeting the immovable object“ (Gavin Baker). Which side will prevail (and when) is unpredictable, but remains the key question as we try to judge the probabilities of possible future developments.
Is it possible to make good investment decisions facing such a conundrum?
Risk and Uncertainty
Quantitative investment strategies that utilize historical data to grapple with the complexities of financial markets have become widely popular. But these approaches find their limit when they encounter a situation that simply has no (or too little) historical data that they can compare current events to.
There is a trap inherently lurking in the quantitative process: the illusion of certainty. Exact numbers imply that certain knowledge of the range of risks and rewards is possible when very often it is not. Whenever we breach these theoretical limits (in financial markets this happens much more often than it should according to quant statistics), it becomes obvious that risk is not the same as uncertainty.
Under conditions of uncertainty, not everything is known, and it is impossible to calculate the best option. Here, simple, smart rules of thumb can lead to better decisions than complex calculations.
“The best decision under risk is not the best decision under uncertainty“. Gerd Gigerenzer
Gerd Gigerenzer does a great job of advocating approaches that favor simplicity over complexity in conditions of uncertainty in his excellent book: “Risk Savvy“. One of his most useful concepts is the use of “smart rules of thumb, that aim to be roughly right instead of precisely wrong“ when tackling complex, non-stationary problems like the financial markets.
Through trial and error in the evolution of my own investment approach, I have come to the same conclusion and you will find references to this philosophy in many articles throughout my blog.
Risk vs Uncertainty
RISK: If risks are known, good decisions require logic and statistical thinking.
UNCERTAINTY: If some risks are unknown, good decisions also require intuition and smart rules of thumb.
Gigerenzer advocates that: “The more complex a risk is, the simpler a solution we need to find.When we face a complex problem, we look for a complex solution. And when it doesn’t work, we seek an even more complex one. In an uncertain world, that’s a big error. Complex problems do not always require complex solutions. Overly complicated systems, from financial derivatives to tax systems, are difficult to comprehend, easy to exploit, and possibly dangerous. And they do not increase the trust of the people. Simple rules, in contrast, can make us smart and create a safer world.“
Ok now, this sounds like good common sense in theory, but still leaves an open question: How do I find the simplifying rule of thumb that can be really helpful in my particular situation? Especially under conditions when uncertainty has skyrocketed and the possibilities for future oucomes are extraordinarily widespread.
This is definitely not easy and every investor in the world tries to gleam an insight to use to his advantage right now, but there are some ideas in here that are very useful. We can get back to the basics and screen out a lot of useless information contained in the daily barrage of news and speculation, because in an uncertain real world situation a smart and simple rule of thumb relying on less information will often beat a complex algorithm.
It is all about finding the information that really matters. Figure out what matters and ignore the rest.
At the moment just the tiniest difference in interpretation can lead to a vastly different prediction – we are sitting at a watershed moment where small real world events can decide whether an economic downward spiral or a rocket fuel recovery ignited by stimulus becomes our reality.
It would be a mistake to place too much weight on current observations and interpretations as this can easily lead to opposing conclusions with a very high conviction while both outcomes (or any course in between) still have a very high probability to come to pass. Solid facts are rare and all current models are hamstrung by large degrees of uncertainty in their inputs.
To avoid to be torn between these possibilities it helps to fall back to basic, sound ideas.
For this purpose rules of thumb are more appropriate and likely to lead to better outcomes, because they acknowledge that the greatest unknown we face is true uncertainty.
In the current case my investment decisions ultimately boil down to the question: which force is stronger?
- Rule of thumb #1: Don’t fight the FED (enormous liquidity injections drive asset prices higher)
- or Rule of thumb #2: Really deep bear markets happen when the economy seriously contracts
To reach a decision, I´ll continually compare the size of FED & fiscal stimulus to the length and depth of the expected recession over the coming months with each new puzzle piece of information coming in.
Ultimately the path we will take is unknowable – at least for the moment – and I expect that we will continue to shift between favoring the narrative of one side or the other, as we have seen mirrored in the extraordinary price moves (up and down) across assets in the last weeks.
Things we do know
In addition to our specific current situation, I have a couple of favorite heuristics that help to cut through the noise at any time.
Even in a realm dominated by uncertainty we do know quite a bit for sure. We can use of these certainties to construct very effective common sense filters to narrow down which investments to include for closer consideration.
Carry and Base Rates
A great example is the carry rule: carry is a term used to describe how much an investment costs or pays out when its price doesn’t change – e.g. management fees, transaction costs, dividends or option premia.
„We know the current yield and holding costs of an investment. If we only consider assets or strategies that pay us for simply holding them (or at the very least cost us a comparatively small amount), we skew the chances for success significantly in our favor.“
On the other hand when you see (or are offered) an investment product where the actual costs involved are unclear, you can be quite certain that somebody is making a lot of money of this product – and be equally sure that this person is not you.
Of course not everything can be known as well as costs, but some things are more reliable than others when using a statistical analysis of past occurrences. We can anchor our decisions in probabilities derived from statistical studies that use few and simple rules on a large sample size: the base rates.
A strong example is the equity risk premium: The basic long term analysis (1928 – 2019) of US equity indices shows that we can expect to see a higher price for stocks one year from now with a probability of 74% – that is a distinct advantage in an uncertain world. We can use this base rate as a simple rule of thumb to be reliably sure, that long-only equity index investing is generally a good idea – which certainly helps when we are going through painful periods of large drawdowns.
Using the equity risk premium as the basis we can narrow down into more specific base rates to fine tune a buy-and-hold investment process. Looking at volatility and drawdown distributions, we find that highly volatile periods historically occurred primarily when equity indices were in a long-term downtrend. For example a majority of the worst and best days are clustered in these periods. To avoid these unproductive times, we can use a trend-following approach looking at specific indicators like a long-term moving average.
Diversified across many asset classes this approach has withstood the test of real world implementation for decades – an analysis even takes its success back over a 1000 year period…
Bayesian decision tree
A bayesian process fine tunes a decision by adding new information to basic probability distributions. Stacking statistically solid edges across different methodologies and time frames has a number of advantages:
- Base-rates influence all trading decisions: usually these are the most reliable elements and have the strongest impact on results in any strategy. At the same time the human mind has a strong tendency to ignore or underweight basic probabilities in the decision making process.
- Any kind of probabilistic assessment over any timeframe can be combined in this process. Should a single analysis not actually have an edge, as is often the case, it will do limited harm as only the combined stack of edges leads to a final trading decision.
- The rough rule of thumb to average all the probabilities derived from different types of analysis will preserve the combined edge there is as well as a complex statistical model. Intuitively we can weight the best cues stronger, if we wish to do so.
Distilling a statistical model, which gets very complex quickly when trying to analyze vast amounts of information, down into a simple step-by-step process can preserve the essential advantage of the model without falling prey to its blindness to the unknowable.
Stay tuned for an upcoming article to use this idea to build a probability map for equity markets across time frames – for an initial idea how to create it read this article.
Thank you for reading & all the best
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