1. You need to know what you are looking for – both to enter the market and to exit.

2. You need to know what the variations on #1 are – and what they are not!

3. You need to know what is imprecise about what you are looking for – it is more than you think.

4. You need to have thought about the imprecision long before you sit in front of the screen and certainly not just as you push the button.

5. You need to know what it will feel like if you turn out to be wrong.

6. You need to know how you will handle, manage, learn from and deal with that feeling.

7. You need to know what it will feel like if you are right – and again, how you will handle, manage, learn from and deal with it.

8. You need to know how to operate on the premise that “perfect is the enemy of the good”.

Ok

1. Markets are only human

2. The numbers are only a clue

3. The brain knows it

4. The brain believes it is in the jungle fighting for survival

5. The brain knows how to survive and uses all kinds of mechanisms to do so

6. It’s language is FEELINGS. Period.

7. Consciousness is feelings. Perception is feelings.

8. This makes the ultimate market data one of feelings – yours and theirs. Everything else is a proxy.

9. Because it is the jungle, it is a fight for survival.

10. This makes it emotionally intense.

11. Emotions have good info and can also be fueled by memories projecting an expectation.

12. The ultimate goal is to tell the difference.

13. The only way to do that is to get as good at reading your own feelings as you are at the charts.

14. It ought to be easier actually – as you are in total control.

15. It feels harder.

16. It won’t be if you put in just 50% of the effort.

17. You STILL need a game plan.

18. But it is the skill – SKILL – in executing it that will determine the winners.

19. Skill is different than robotics.

But in reality, the numbers of the market are but an illusion.

Markets are only the vacillating prices that other human beings, using the same mathematically based tools, are willing to pay. For example, what can be expensive one day can be very cheap the next if a trend has ensued.

It is only a matter of perspective. And perspective is a matter of the judgments you make.

Judgments on the other hand will be influenced by both impulsive feelings and by intuitive feelings – or pattern recognition. The trick is to have all the data on the table so you can tell the difference.

In order to do this, us market participants need to do a couple of things – give up the notion of a iron-clad trading plan based purely on historical probabilities and replace it with a trading plan based on historical probabilities (yes you read that right) AND a systematic way to leverage your judgment under uncertainty. This way you can make a decision about factors that may now be in play for the future probabilities. I mean who thought the VIX could stay over 30 for 6 months? … I am just askin.

Now in order to do this successfully, you have got to learn to optimize your judgments – which means spending more time focused on deciphering and understanding them than you spend on deciphering and understanding the charts.

This is revolutionary trading psychology – and it works.

The major points I want to make – and they apply to all traders – are

#1) Start with the core question – the Social Markets Hypothesis question of “what will the other guy do?” – in the timeframe you care about.

#2) Realize that numbers of any sort are just a tool to help in understanding the answer to question 1. No algorithm or program anywhere can account for the vicisstudes of human behavior and in order to most effectively deploy the algorithm/system, one needs to know its limititations. Without acknowledging its limitations, you have no resources when the tool is inadequate.

#3) Learn to research and evaluate internal feeling based data. Both value it and beware of the risks it brings – a double edge sword. Instinct delivered through the conduit of feelings can tell you things your deliberate analysis cannot (due to human brain’s ability to recognize a cat versus a dog) but unexamined feelings can lead you astray via their natural tendency to inject risk management (fear) or take the simplest route (the same thing will happen next as happened last).

Sophisticated modelers have powerful tools at their disposal but they still have to answer the same question that discretionary less capitalized market participants do. Anyone will be well served to make decisions in concert with their brain’s view on the ambigous data of human markets!

The “thing” that is different about both this hypothesis and my way of looking at the markets is this. I begin my thinking with the question of “what are they or will they be doing”? In other words, I always ask the human question FIRST.

I do this for a couple of reasons. 1) It is actually the only thing we care about – except 99.85% of the traders and investors never think of it. 2) Recent research shows that “Theory of Mind” (the ability to understand the other) is a more applicable skill to market prediction than thinking in probabilities.

Now I have almost 15 years of trading experience and I learned from a tape reader (btw …what is tape reading but “reading” what “they” are doing), so I can look at my screens and unconsciously come up with a good answer to the human question. But it is totally fair to ask me to deconstruct it?

For example, the $TICK and the Adv/Decline line or chart are both revealing what traders in the NYSE cash equity markets are doing. Now, who trades those? Basically you have two maybe three groups – the vast retail public and the hedge funds (and prop desks). It used to be that the S&P futures would lead the cash market but it is my experience that in recent years, this “cash” leads the futures. I attribute this to lots more short-term trading by hedge funds.

2nd – the Naz and Russell are different animals from the S&P 500 – so I consider those traders/groups as a different group of people. If they are turning the ship in the exact same way at the exact same time, then the boat is going to move. If they are pulling in a little bit of the opposite direction, then the boat won’t go so fast. It might even do a 180. When I look at the VIX (which I haven’t lately but not for any great reason), I consider that a 3rd group – the equity option people. Same ideas though.

Now all of this can be labeled under convergence/divergence but I like to have my decisions be based on the most irreducible analysis which in the markets is always people – i.e. it can’t get any simpler than that.

So take elements like volume. Well now, what is volume but a representation of how many people traded how many contracts in a given period or at a given price? (which is why I like the market development/market profile method) You can count on the price coming back to a high volume area or an area that everyone knows (yesterday’s low) and being defended. Why do gaps work? Well… because there are less trades there to be defended – and because now we all expect them to work. In other words, real people who bought or sold at a certain price – or who had the opportunity to get in or out at a price but didn’t – will react when the market gets back to that price again. Some will be adding to defend and some will be adding out of relief (it works short or long).

You can also ask questions like “are there as many people selling at this price now as there were this morning or did that huge volume bar just wipe out all the shorts” – at least until a group of real people have time to recover and re-load.

So… do I use probabilities? At this point, I really only use them intuitively. Do I think they are valuable? YES but nevertheless, the real clue is that the numbers (and the bars and lines they draw) are only a clue – not the real answer.

Hope that helps – DKS

But beyond linguistics and back to the regularly scheduled program of Psychological Capital, why does ambiguity matter – particularly to sophisticated traders?

Ambiguity matters because it is the hallmark of markets. At any point and from any perspective, markets of all types are always ambiguous. They are never ever certain – no matter how many algorithms or sophisticated studies of historical probabilities a trader wraps around them. These techniques lure us into thinking that our results can be certain – i.e. we have a 67% chance of generating X points if we get long or short according to this relationship of these four factors – when in fact we cannot be certain.

How much time will it take? How much “negative drift” will it incur? Just these facts alone mean the possibilities are essentially endless and therefore the question is at its core ambiguous.

But why does THAT matter you say if you have tested your 67% chance and believe in it? Well first because how immutable are your beliefs? Confidence levels (feelings mind you) are variable i.e. creating more ambiguity even if this portion is actually in your psyche versus in your data.

This dilemma if you will is the exact reason that the vast majority of traders lose money. In order to be successful, it is necessary to understand, appreciate and even love the ambiguity. It is also necessary to know what to do with it. And for the latter it helps to know how your brain handles it – which is not as a serially updating computer solving a calculus or even a basic statistics question.

Faced with ambiguity, your brain naturally resorts to filing through unconsciously stored patterns and communicates with you through your feelings as much as your thoughts. Which adds even another challenge to this already daunting mental exercise of taking money from other traders (that is what the game is btw – alas, but for another post).

So… what have you been taught to do with your feelings? Discount or ignore them, right? Now you are in the position of purposely overlooking the very data you need to fully interpret what is going on in front of you – at which point, in this sea of ever-changing ambiguity, you are lost. This is why sometimes seems if you just took the opposite of every trade you would make money. How many traders have said “if I could only use myself as a counter-signal”?

The question is – instead of me lecturing – how do you handle these facts of trading?

In his milestone paper “Portfolio Selection” published in the Journal of Finance in 1952, Harry Markowitz, the pioneer of “modern finance,” recommends to use the Expected return-Variance (E-V) rule, both as a working hypothesis to explain investment behavior and as a guide to “investment” – as distinguished from speculative behavior. This rule implies that an actor who considers yield to be good, risk to be bad, and speculation to be banned, should diversify in such a way that his portfolio lies in the “efficient frontier.” The idea is very simple. When building your portfolio, combine the securities in such a way that for a given expected return (E) of your portfolio, its variance (V) is minimum. This defines all the efficient combinations (E,V). Then, according to your degree of risk aversion, pick one such combination – a risk averse person will prefer a low E – low V portfolio, whereas a risk lover will choose a high E-high V combination. The beauty of this rule lies in its apparent readiness. Given a probability distribution of yields of the various securities, computing the set of efficient (E,V) combinations is straightforward. But this is misleading, because it dodges the main issue: Where do the expected returns and variances estimates come from? In other terms, how do we set our probability beliefs?

At the end of the paper, Markowitz himself recognizes that he has been silent about the origin of these beliefs throughout:

“To use the E-V rule in the selection of securities, we must have procedures for finding reasonable expected returns and variances [...] I will not pursue the subject here, for this is “another story.” It is a story of which I have read only the first page of the first chapter. In this paper we have considered the second stage in the process of selecting a portfolio. This stage starts with the relevant beliefs about the securities involved. We have not considered the first stage: the formation of the relevant beliefs on the basis of information.”

Dodging the question of belief formation is murderous

It seems that fifty years from this paper, we are still stuck in “the first page of the first chapter.” Quite ironically, after Markowitz’s paper, such E-V rule has become ubiquitous in real-world finance, despite this inherent indeterminacy. Why does such belief indeterminacy matter? After all, is it so complicated to use a mix of statistical analysis and practical judgment, so that we derive sensible probability beliefs? In the first instance, one can use observed yields and volatilities from the past, to get statistical estimates of the true yields and variances. The problem is elsewhere. By emphasizing belief formation related to the expected returns and variances, we are missing the key point. Belief formation about world uncertainty is the issue, which is totally buried by Markowitz here. Markowitz implicitly assumes we live in a Gaussian world of “mild uncertainty,” where price changes are characterized by stability around the average. In such a world, Markowitz’s “E” and “V” are relevant objects. But what if randomness is not mild at all in our world? What if there is not such a thing as “value,” and returns have infinite variance? In such a world, sensible people do not average but rather arbitrage between times and places. If so, by putting forward “E” and “V,” maybe Markowitz leads us to wrongly interpret the world we live in.

Returns uncertainty, viewed from the empirical side

And there is good evidence that it is the case. If returns were Gaussian, we would have 68% of small price changes within one standard deviation of the mean, 95% within two standard deviations of the mean; and outliers (large changes) would be extremely rare: according to the Gaussian model, index swings of more than 7% should come once every 300,000 years… Rather, empirically, we observe too many large and too many small changes in the prices (this is what we call “fat tails”). Moreover, we observe irregular trends of large changes followed by clusters of small changes. That is, trouble runs in streaks (a wild day might be followed by a wilder day). This means that “persistence” is far larger than expected, would the world be Gaussian. As such – at least for investing horizons from two hours to six months, the Gaussian hypothesis is the wrong interpretation.

Uncertainty in the finance world: Where do we stand?

What is the correct model of uncertainty in finance then? There is no definite answer to that paramount question. Actually, two distinct routes are possible.

Nonstationarity

The first one assumes nonstationarity. For instance, the widespread use of GARCH models is to capture the foregoing phenomenon of persistence, while staying within the Gaussian boundaries. The idea is to introduce changes in volatility – that is, instead of considering one single Gaussian distribution for the returns, consider multiple ones, each characterized by its own level of volatility. When real world volatility soars (resp settles), make the Gaussian curve grow (resp shrink). This indeed enables to fit the data. Poissonian uncertainty has also been suggested as a model to replace the Gaussian (Brownian) model. Gaussian risk involves a high probability of a small change, while Poissonian risk involves a small probability of a large change (jump).

The multifractal model

The second route is the one taken by Beno?t Mandelbrot, the father of fractal geometry. It avoids to assume nonstationarity. Rather, he proposes real world randomness to be best described as “slow.” The Gaussian model entails a randomness that is too mild. Conversely, “wild randomness” is characterized by an extreme degree of unpredictability: tails are huge, “everything can happen,” whereby there is no way to forecast the returns (both expectation and variance of price changes are infinite).

Slow randomness is in between: there is no stability around the average, and tails are fatter than in the Gaussian world. Mandelbrot’s model of real world uncertainty is remarkably elegant – and I’m not (only) saying that because Mandelbrot is French. Not only his framework generates the fat tails and persistence phenomena observed in real data, it also suggests using quantitative tools to rigorously measure (1) how fat the tails are, and (2) the degree of persistence in the returns.

Alpha and H

Two parameters summarize these two dimensions, alpha for the size of the returns, and H for their sequences (dependence). The first parameter comes from modeling the tails with a Power Law. For x large (we are at the tails), It sets the ratio of probability of a return larger than n x over the probability of a return larger than x to be n^{-alpha}. Intuitively, the smaller alpha, the fatter the tails (i.e, the larger the instability: the realization of an outlier moves the average).

With a Gaussian distribution, alpha equals 2; under wild randomness, it is 1. Alpha in between points to a world of slow randomness, the one we presumably live in many instances. The second parameter is very intuitive too. It says something about the sequences of the price changes (runs) rather than their size. The question is how much the past shapes. In a Gaussian world, over a given period (say, 10 years), the range between the highest return and the lowest one is sqrt(5) times the empirical standard deviation of the returns from one year to the next. However, when randomness is characterized by persistence, the high-to-low range widens not by a square-root law but as a H power, with H larger than 1/2. This means long term dependence, and captures the fact that turbulence clusters.

Blueprint: Assessing real uncertainty is like constructing “dikes”

No model is universal. Plainly, interpreting properly the nature of the randomness we face in our investment decisions is context-specific. So, how do we choose between the models? We don’t have to be blind about uncertainty. Once we no longer take for granted the Gaussian assumption, all we have to do is appraising properly the world we invest in. That is, we can track — and perhaps forecast – how turbulent the market is becoming, using fractal geometry and the alpha and H measures.

In “The Prince,” N Machiavelli compares “Fortune” to a violent river, and suggests ours constructing dikes to protect ourselves. What does it means for finance to construct such dikes?

Just that we need to assess alpha and H.

And in practice?

If alpha is smaller than 2, then we should definitely think again about the E-V rule. (Same thing if H is different from 1/2.) If so, we should ignore “E” and “V” and merely focus on both alpha and H, with which to evaluate true risk.

Sadly, things are not that simple, because measuring? alpha and H is in practice very difficult.
In 1991 Andrew Lo reported that Mandelbrot’s tests for H can confound long-term memory with the effects of short-term memory. And the measures of H are not robust: there is no consensus as for the S&P 500 index for example: the estimates for H vary from 0.53 to 0.74. Furthermore, the degree of dependence varies a lot from one type of financial asset to another (gold prices, oil markets, foreign exchanges, might have long memory, whereas cotton, British government bonds, do not). So, overall, it is unrealistic to hail alpha and H as new yardsticks for finance.

We’d better acknowledge our ignorance: Doubt is good

To be honest, it is impossible to claim with certainty that one model is the correct one at a specific moment in time. Accounting for model uncertainty is the hallmark of modern econometrics and (truly) modern finance. Is it a retreat into – at least lucid – blindness? I don’t think so.

The only relevant question is referred to by Nassim Taleb as “tinkering:” How to make sound investment choices in a world we don’t understand? Modern finance tackles this problem, by putting forward rules of behavior under “ambiguity.” By “ambiguity,” we academics precisely refer to these situations of missing information about “the rules” of financial investment, the one we model as a big game of betting under unknown odds (more on this here).

F.Nietzsche wrote in his “Ecce Homo:”

“No doubt, certainty is what drives one insane.”

I think “modern finance” drove us insane because it has forgotten to doubt.

‘Ambiguity’ is the Hallmark of Trading and Investing

The situation of taking a position when the odds are uncertain because of missing information is referred to by economists as “ambiguous”. F Knight in his book Risk, Uncertainty, and Profit was the first to emphasize ambiguity in 1921. Ambiguity (otherwise known as knightian uncertainty) is the hallmark of finance and should be acknowledged as such. To quote Nassim Taleb, the “rules of the game” are unknown in the financial arena.

What does this actually mean? Just that in many if not most trading and investment situations, the odds are not objectively known, and players may have little information and hence also little confidence regarding the true odds.

Offhand, such lack of confidence might seem counter intuitive: after all, observing relative frequencies should allow one to infer the underlying probabilities, don’t you think? Indeed, take a particular asset as being an urn from which you can draw a red or black ball where the red pays more but it isn’t known what the proportion of red to black is. After sampling the urn several times, one feels much more confident about its odds.

This is all nice in principle, but real world finance is far trickier and there are several reasons for our inability to confidently judge the probabilities in practice. True, we can observe the performance of a particular stock every period (be the relevant horizon one hour, one day, one month), and infer something about the stock’s odds.

But, these odds themselves change. Recent leading-edge econometrics literature has revealed unexpected jumps to affect stocks and bonds at an extremely high frequency.

In addition, a certain kind of probability is inherently subjective and cannot be inferred from observing relative frequencies: what about the chance of a landslide for Barack Obama on November 4th? Here the lack of information is irreducible and has to do with conflicting evidence.

Further, behavioral studies have shown that people feel they are missing information when betting against another person who is better informed. And even when there is no better informed opponent, people act as if there is.

To What Extent Does This Matter?

For all these reasons, taking a position is not a decision about known odds but a decision with ambiguous information.

Why does this matter? Because choice depends on how much relevant information is missing or how ignorant people feel compared to others, as Chip Heath and Amos Tversky first pointed to in a beautiful paper (1991).

Mr. Spock Does Not Care about Ambiguity…

At first glance we may assume the reverse, that is that traders and investors won’t act differently in the face of risk and ambiguity. Indeed, standard economics invites us to do so, because expected utility theory totally ignores the importance of confidence in judged probabilities. Its bosom stance is that the probabilities of outcomes should influence choice, whereas confidence about the probabilities is irrelevant. The proof is very clean and ushers in a misleading view of decision-making under uncertainty.

Here is the logic of expected utility theory. (Readers who don’t like Mr. Spock can skip this bit without any damage — homo economicus being characterized by Mr. Spock is due to Richard Thaler.)

Suppose two assets, a risky one, which will deliver 100 dollars or 0 with equal probability, and an ambiguous one, which will deliver 100 dollars if Mr. Obama wins the election, and 0 otherwise.

Do you prefer to invest in the risky asset or the ambiguous one? Now consider a symmetric ambiguous asset which will deliver 100 dollars if Mr. Obama loses and 0 otherwise. Again, ask yourself whether you prefer to invest in the risky asset or this ambiguous one.

Choice consistency leads to choose the ambiguous asset in the second case if you have preferred the risky asset in the first case. Why? Because for expected utility theory, choosing the risky asset in the first instance reveals your probability that Mr. Obama will win the election to be smaller than 1/2. Therefore, you should prefer the ambiguous asset in the second case, since for you the probability that Mr. Obama will lose is higher than 1/2.

… But Human Beings Do

By ignoring the influence of confidence in choice, expected utility theory is missing a key point. Most of us will invest in the risky asset in both instances. This is Ellsberg paradox, first revealed by Ellsberg in his paper “Risk, Ambiguity, and the Savage Axioms” (the “Quarterly Journal of Economics”, 1961).

Actually, the premise that confidence about the probabilities is irrelevant is wrong on both the behavioral and the neural level. Under ambiguity, the brain is alerted to the fact that critical information is missing and that the ensuing uninformed choice therefore is more potentially dangerous.

A milestone study by Ming Hsu and colleagues, published in “Science” in 2005, has revealed the level of ambiguity (when choosing between a risky bet and an ambiguous one) to be positively correlated with activation in the brain areas known as the amygdala and the orbitofrontal cortex, and to be negatively correlated with activation in the caudate nucleus within the striatum, well known to be implicated in reward prediction.

An Evaluation System in the Brain that is Sensitive to the Levels of Uncertainty

Further, in their study, activity in the caudate built more slowly than activity in the amygdala and the orbitofrontal cortex. This is strong evidence for the existence of two connected systems. Upstream, a vigilance system sensitive to the level of uncertainty in the context (the OFC / amygdala complex), signals uncertainty to the anticipatory reward system downstream (the striatum). In other terms, the OFC and the amygdala evaluate uncertainty and modulate the expected reward signal in the striatum. Interestingly, in the same study, the authors further demonstrated that OFC-damaged subjects do not distinguish between the risky bet and the ambiguous bet, thereby acting in a way that is consistent with expected utility!

Inhibition of Impulsiveness when Facing Ambiguity

Further study by Scott Huettel and colleagues at Duke University, has confirmed that specialized neural mechanisms are involved under ambiguity and that they are well dissociated from those implicated in risky situations. Interestingly, this study — published in “Neuron” in 2006 — links the inferior frontal sulcus, within the lateral prefrontal cortex, to decision-making under ambiguity. This is interesting to more than one extent. First, this region — and others around — has been shown by Etienne Koechlin and colleagues, in a study published in “Science” in 2003, to be crucial for contextual control, when one needs to resolve the multiplicity of possible scenarios to set one’s own rule for behavior.

Remember Taleb: in finance the rules of the game are typically unknown, so we have to construct them…

Further, in the same study, Huettel and colleagues have found that the ambiguity effect in the inferior frontal sulcus is less pronounced in those subjects with a higher degree of cognitive impulsiveness — as measured by the BIS impulsivity scale. Although this is purely correlational, it is tempting to conjecture that the inferior frontal sulcus plays a role in inhibiting impulsiveness here, presumably by sending an alerting signal — pointing to a lack of confidence — to the striatum downstream.

So What?

Acknowledging the prevalence of ambiguity aversion in finance has already had far-reaching implications. For instance, ambiguity aversion might explain the well-known home-bias in investing, as well as the equity premium puzzle – this route to explain the equity premium puzzle has been explored by Larry Epstein.

We would argue that outside academia as well, traders and investors should pay special attention to the underpinnings of their behavior when deciding under knightian uncertainty.