4 Criteria and examples of useful strategies

Filtering for high probability, profitable strategies as the building blocks for a portfolio. 

My goal is to combine asset classes, return factors and trading strategies into a diversified portfolio, that yields high risk-adjusted returns.

But how do I figure out which strategies to concentrate on?

Sourcing ideas
There is no need to reinvent the world. A lot of original thought actually recombines existing knowledge. I don´t mind using inputs and backtested results from different sources (preferably books and papers by practitioners with an academic background). I look at this research skeptically and check results independently – many widely used concepts (especially in the short-term realm) have no practical merit.
Drawing from high-quality outside sources of other people´s previous experiences, gives my own ideas a solid foundation and often a healthy reality check.

Before implementing strategy rules with real money, I often backtest by hand going trade by trade. Although this low tech approach is cumbersome and it is hard to be exact, it has some distinct advantages: you can visualize the strategy unfolding and begin to get a feeling for the emotional rollercoaster of drawdowns and profits it brings with it. It boils down a selection of possible strategies to the most promising ones, as only those will be worthwhile testing like that. It keeps curve fitting in check, as it is not possible to run through countless variations of a strategy. And it is possible to do right away, without having to clear high technological hurdles first.


Is there a simple test to decide which investment ideas and strategies deserve closer scrutiny and which approaches to rule out altogether (even if there are influential market participants and commentators in support of them)? That would be a powerful method to cut through the noise.


The base rate counts.*
The base rate is the prior probability distribution of outcomes. For me this has been the most eye opening heuristic in recent years. It´s a meta level test that serves as Occam´s razor** for an investment approache´s fundamental merit – does it show strong promise or can I just ignore it?
This is how you can use it in practice: all strategies have to pass the test of having a basic probability to yield positive returns that is strongly in your favor in their simplest form: A positive edge.
Look for strategies that tend to produce superior risk-adjusted returns*** in the long run, even when using only the most general rules and parameters or even none at all (e.g. simply being long equities). If you can figure out a solid reason for their sustainable outperformance, then these are investment approaches that really work.

How to do that in practice?
Collect information on the long-term historical risk-adjusted return of different investment approaches: asset classes, return factors, trading strategies etc.. Radically narrow your investment universe to strategies that show favorable basic probabilities across long periods of time. These are opportunities with a natural edge – you can select the ones with the best historical results and combine them into a portfolio using additional criteria.
To be able to do this, it is necessary to find resources**** that provide systematic rules and the resulting historical statistics or to generate the statistics yourself.
A purely discretionary approach won´t be able to generate these guidelines before we actually implement the strategy or at least paper-trade it.


Why go with basic probabilities?
There are enough ways to invest out there, that will give you positive long-term return expectations through historically persistent risk premia (returns over the risk-free rate) and strategies, that withstood the test of time and that have a compelling explanation. There simply is no need to fight an uphill battle against basic probabilities in search for an edge.
Going against the base rate makes it much harder for a strategy to succeed. Your skill must be consistently above average (and that includes all professional managers out there) and even then your portfolio will be less robust, as mean reversion kicks in sooner or later. Chances are, that your portfolio will underperform or lose money.
Randomness and uncertainty are the major factor in investing and we always want to have basic probabilities on our side.

For me this has proven to be an extremely powerful filter, it has completely transformed my approach to investing and my success. I don´t aim to generate alpha (through unique sources of return), but instead concentrate on combining many diverse sources of beta (different market returns), including alternative beta (through well known return factors and trading strategies).


Interestingly enough there are many commonly touted investment approaches that do not pass the base rate test.
Perhaps the most prevalent and most eye-opening example is selecting individual stocks:
Statistically about 65% of stocks underperform their index. The best performing stocks in a broad index perform much better than the other stocks in the index. That means average index returns depend heavily on a relatively small set of winners. No wonder that studies keep telling us, that almost no active fund manager consistently outperforms his benchmark over time – not even speaking of non-professional individual investors: Your stock picking skills must be in the top 35% to even match index returns, therefore you are going against basic probabilities and are likely to underperform.
The statistic implies that using low cost index ETF to capture the market´s equity risk premium will be superior (with a probability of about 65%) to trying to pick the best stocks – that´s what I concentrate on.
With very specific expertise and skill value can be found in individual equities, but it is a very efficient environment as many of the most highly skilled market players compete here. It would make most sense to me, to concentrate on the most inefficient areas of the market (e.g. micro cap, deep value or emerging markets).
But more pronounced inefficiencies are likely to be found on the asset class level.
Most deviations from a market cap index, that have shown long-term outperformance, can be explained by different return factors (e.g. value, momentum etc.) and can be accessed through a wide range of securities targeted by ETF that screen for these specific factors.

This particular filter has the valuable advantage of blocking out a lot of the confusing media noise and to bypass an incredible amount of intricate information about individual companies which is unlikely to lead to outperformance – it allows us to concentrate on more useful ideas.


Another, more obvious, example is the whole universe of very short term (day-) trading strategies, luring with the promise of instant wealth from a small capital base. Many people (including me) find that idea attractive and feel drawn to it, even though almost everyone knows intuitively that it is unrealistic: something that looks too good to be true probably is.
Different sources put the number of day traders that consistently lose money between 80% and 95%! Why would you try to go against such odds? Could you be falling prey to overconfidence bias or a grandiose marketing scam? How much more sophisticated than the average player are you realistically? Picture hedge fund quants with Ph.D.´s on the other side of this zero-sum game. But even if you are in the top 25% of the most skillful market participants, you are still a long way away from profitability given the basic odds. You would have to be in the top 5% to 20% and even then you would just start to be profitable – a far way from beating the market average.
Then consider the issue of regression to the mean and the power of arbitrage: how sustainable is your edge over time even if you experience profitable periods? How fast will the edge erode, because of other investors discovering a profitable opportunity and quickly arbitraging it away? Which part of the performance is due to luck, which part to actual skill?
As I can´t answer any of these questions positively for myself, any short-term strategies, I look at, have to derive their edge from a basic risk premium or from consistent human behavior, that can be shown to have existed for decades and is likely to continue to do so. I want to see quantifiable rules and long term strategy statistics from different sources and those are hardest to find in the short term trading space – that in itself is a red flag.


What makes these thoughts interesting, is to turn them around – akin to Charlie Munger´s famous inverse thinking. How is such a consistently high loss rate or underperformance possible (as that seems very inefficient)? And most importantly: where does the money flow to?  After all the sum of the trading game is zero (after commissions, spreads and taxes).
We want to be where the money flows to.
A consistently bad strategy or consistently losing asset is very useful, if it is possible to find ways to take the opposite side.


Where can an edge be found? 
It is not that hard – being able to beat the average retail investor would be a valuable edge, wouldn’t it?
A simple example of a favorable base rate is buying and holding broad equitiy indices: the expected return above the risk free rate is the equity risk premium and that has been positive on average over long time horizons: around 5% real return per year with a long term Sharpe ratio (a measure for risk adjusted returns)*** of 0,3-0,4.
Because of behavioral biases, as many studies show, the average investor distinctly underperforms these market returns – a passive investor beats the average investor over time.

But strategic, active investing has the potential to further tilt probabilities in our favor.
Overview of long-term positive risk premia, that can potentially be harvested:****
The data is averaged from different resources and these are rough numbers only – which is good, because the past can only provide an uncertain approximation of what the future may bring:


  • Risk free rate – the baseline against which to measure returns
  • Asset Classes (stocks, bonds, real & alternative assets): Sharpe ratio: 0,2-0,6
  • Return Factors (value, momentum, low volatility, illiquidity etc.): Sharpe ratio: 0,5-0,8
  • Strategies (trend following, carry, volatility selling etc.): Sharpe ratio: 0,4-1,2



But where are these higher risk-adjusted returns from return factors and strategies, compared to passive asset class returns, coming from?
Outperformance comes from capturing money flows created by consistently underperforming strategies. For us to be able to make investments with market beating expectations, someone else needs to make consistently bad decisions that lead to returns below the market average.
Logically any outperforming trading strategy should target other investor´s mistakes. Another possibility is to find areas where other market participants lose money willingly, because they have different incentives (e.g. hedging a portfolio). This makes for a very persistent edge as these players don´t aim to improve to avoid losing.


Examples of strategies that exploit consistent underperformance by other market participants:
  • Performance chasing leads investors to increased buying at market tops, combined with panic selling at bottoms. This leads to a behavioral performance gap for individual investors of more than 4% underperformance compared to the market average according to the Dalbar study***** (Which is why simply holding an equity index would beat the average retail investor). That means an outperformance of nearly 4% annually (that is 50% higher than the historical average for equities!) is theoretically possible for investors who do the opposite. That money flows mostly into value strategies, that pick up securities that have fallen to excessively low valuations and momentum strategies, that knowingly participate in the early stages of performance chasing before mean reversion kicks in. Both strategies have a risk-adjusted return, that is more than 50% higher than the average index return over the long term.
  • Loss aversion leads investors to the losing strategy of cutting winners and letting losers ride: money flows into trend following strategies (based on the principle of cutting losses and letting winners ride).
  • Short-term trading creates consistent losses: money flows into long-term strategies (patience is a strong edge in today´s market) or strategies that can define those losses and take the opposite side.
  • Investors systematically overpay for both insurance (hedges) and lottery tickets. This can be used to our advantage by providing tail risk insurance through selling index options, and selling far-out-of-the-money call options as lottery tickets, if we are willing to take on the risk of rare, extreme events.
  • Forced selling (margin calls) and buying (fund inflows) identify very good opportunities especially at the extremes of crisis and bubbles.
  • Many institutional investors must follow certain rules. For example they often are obliged to sell corporate bonds that are downgraded from investment grade (BBB to BB); these subsequently often perform well. The same mechanism can be seen with companies that are added or deleted from equity indices: new index members subsequently underperform the companies leaving an index.
  • Professional investors´ avoidance of career risk, provide opportunities in unconventional strategies, unfashionable ideas and over different time horizons, if investors are willing to tolerate intermediate, prolonged underperformance at different times than the market.
  • Multinational corporations hedge foreign currency exposure. This gives an edge for FX carry strategies.


Through constant research I build and refine a pool of possible strategies, that all clear the basic hurdle of having a highly probable positive return. Next I continue with the task of selecting the most suitable strategies from my toolkit and use them to build a diversified portfolio that fits my goals.



*Great ideas on such useful concepts and on higher level thinking in general can be found in „Think Twice“ and More than you Know“ by Michael Mauboussin.


** Occam´s razor: “Among competing hypotheses, the one with the fewest assumptions should be selected“.


*** I use the Sharpe ratio as a measure of risk-adjusted return as it is the most widely used measure. More about my interpretations and the limitations of the Sharpe ratio and why I concentrate on risk-adjusted returns rather than absolute returns in part 7: Realistic goals and expectations.


**** A great resource on risk premia is the book „Expected Returns“ by Antti Ilmanen, it´s a tough read, but so worth it.


continue with part 5: Combining strategies into a diversified portfolio