The Market Mind Hypothesis, page 41
Victor Niederhoffer The Education of a Speculator
9.3.1
Introducing AVIR
For many years, neuroscientists have treated the brain as a mechanical, and later digital device. The Geiger counter was among the first mechanical devices it was compared to because the firing rate of neurons—as a measure of brain activity—seemed similar to the click rate of a Geiger counter—as a measure of radiation. Subsequently the brain was considered to be some kind of computer, consisting of hard- and software.
In contrast, my overall case is the mind-as-market embodied in the market-as-mind. Currently, via analytical methods (e.g. regression), prices are treated in a remote and sterile manner which reflects the physical aspect of their dual realisation. But what about their phenomenal aspect? How could this be investigated? Here the MMH’s potential for novel empirical research truly comes to the fore. For inspiration we first look at quantum mechanics. Quantum mechanics teaches that understanding light—as particle-like or wave-like—depends on how we investigate it. What if prices in markets are, metaphorically, like the photons in light? In other words, what if Bohr’s complementarity applies to investment research, in that prices have complementary physical and phenomenal aspects whereby understanding depends on the method of research?
This brings us to the second inspiration from music:
[Non-lyrical] music does not depict knowledge but embodies it. Instead of showing or telling us something, the music simulates a vital experience. The result can feel almost shockingly firsthand, as if it were happening to you … For the listener, this often means coming to feel what it is like to understand something—even if one cannot always say just what that something is. (Kramer, 2023)
Among others, it underlines the importance of flow, frequency, patterns, and rhythm in minds which makes a comparison to musical instruments seem obvious, with an orchestra as the collective grouping. In terms of such dynamics think, for example, of playing the piano: besides the number of times you hit the keys, the force and timing of those hits determines the sound, while in a group the other instruments complete it. Utilising James’s radical empiricism, it highlights the relationships and transitions (pauses) between data points. As I will show, a number of cognitive and investment experts have seen this similarity too.
What follows is my crude and preliminary attempt to suggest a complementary investment research method in that spirit. This section describes a skeletal framework for our Audio-Visual Investment Research (AVIR) Project. AVIR is a new investment research method2 we are developing, including software, which puts some of the theoretical concepts discussed in this book into practice. Specifically, it contains a suggestion or proposal for an experiment (Subchapter 9.3.7) that would test a few of the (implied) sub-hypotheses of my book. AVIR is meant to complement existing investment research methods and most clearly aimed at (clarifying what I mean by) “investigating market data in a psychophysical perspective”.
Various other sources provided inspiration for AVIR.3 For a start, it tries to help in answering Gigerenzer’s earlier question in a practical way:
We think of intelligence as a deliberate … activity guided by the laws of logic. Yet much of our mental life is unconscious, based on processes alien to logic: gut feelings, or intuitions … We sense that the Dow Jones will go up … Where do these … come from? (Gigerenzer, 2007, p. 3)
In the spirit of Niederhoffer (see opening quote), Ciardi (2004, p. 1) argues that “stock market environments, in which large numbers of changing variables and/or temporarily complex information must be monitored simultaneously, are well suited for perceptual research in sonification”. Marcovici (2014) showed something completely different. By sonification of prices and exposing them to the resulting sounds he trained rats to trade forex and commodity futures.4 A common thread for others was that they emphasised the role of the body, feelings, and intuition in decision making. For example, Borch, Hansen and Lange (2015) explored “the relationship between bodily rhythms and market rhythms in … the open-outcry pit (prevalent especially in the early 20th century) and present-day high-frequency trading”. They show “how traders seek to calibrate their bodily rhythms to those of the market” (see also subchapters 1.2 and 3.2).
Accordingly, AVIR is about questions like ‘how to get in sync with the market’s rhythm?’ and ‘how to sense its emotional excitement?’ These questions need to be answered both from a theoretical perspective, as well as from a practical one. Specifically, what tools are required to mediate this in a proper format, that is a format that improves the investor’s understanding of market movements? Moreover, AVIR is backed by insight such as that provided by Bruguier, Quartz and Bossaerts:
Our findings should also inspire research to improve visual representation of order and trade flow. Since humans often are better at recognizing the nature of intention in moving (animate or inanimate) objects (Heider and Simmel (1944), Castelli et al. (2000)), we suggest that traders may be more likely to successfully detect insider trading when order and trade flows are presented in a moving display, as opposed to the purely numerical listings commonly found in the industry. (Bruguier, Quartz and Bossaerts, 2010; emphasis added)
In a broader context, AVIR is prompted by the fact that we currently do not treat our unconscious (S1) as a system of abilities we can train to use in a disciplined way, like we do our deliberate system (S2). For S2 we use analytical methods with the support of powerful external tools but we have no such approach for S1. In the context of this chapter, it is like testing a subject on their overall understanding of music by providing sheet music that allows an analytical interpretation but denying them an instrument for an intuitive interpretation. It is thereby important to remember that both S1 and S2 inform S3 where their information (or knowledge) is realised in consciousness. And it is the transmission from S1 to S3—by raising awareness—that is particularly lacking.
To be more specific, whereas we use Excel, Matlab, Python, R, and similar software for our rational analysis until now we have had no such tools available for our intuitive synthesis. Part of the reason was the lack of a proper format with which to appeal to our S1 capabilities. As I will show shortly, by identifying audiovisuals as such a potential format, appropriate tools—albeit not originally meant for investment research—also become available as a result. These consist of advanced data-converters, so-called DAWs, combined with audiovisual software.
With AVIR’s planned software (see section 9.3.6) the MMH thus suggests a somewhat contrarian approach. First, it is contrarian in that it goes against the current fashion of relying on S2 and only using analytical (e.g. AI) tools. But it is also contrarian in that it enhances awareness of feelings which helps, for example, to enter trades that make one feel uncomfortable. An investor is confronted with a psychological challenge at two integrated levels in that regard. First, via ToM he has to read other investors’ minds, collectivised in Mr Market. In brief, he has to deal with collective psychology and, particularly, resist herd mentality. Second, he is confronted with the personal psychological issues, like biases, that are raised by investing while being part of this collective. Sometimes these issues have nothing to do with investing itself. In short, he has to deal with the emotions, varying from stress to relief, to commit money to convictions. This challenge requires ways of overcoming it, apart from simply exiting or not entering the market, i.e. not participating.
Overall, AVIR software will be developed to (further) strengthen the ‘moat’ of mental capabilities that are uniquely human. We are particularly strong in contextual assessment, intuition, and pattern recognition. Still, it is often argued that we overdo the latter, in that we see patterns where there are none. But what if there is a flip side to this: we cannot see patterns because we do not have the tools to support us in finding or recognising them.
Overall, successful tests would support that AVIR could improve the investment performance of subjects according to the test criteria. In other words, such an outcome would not only confirm the theoretical sub-hypotheses of my thesis, in particular regarding embodied cognition. It would also suggest AVIR as complementary to existing investment analysis (i.e. fundamental, quantitative, and technical). We plan to set-up and complete the experiment as part of our research programme (perhaps in combination with other [extended] tests). In the meantime I hope this proposal clarifies what I have in mind (and may perhaps inspire somebody else to perform it).
Ultimately AVIR’s central aim is twofold. First, help investors make better investment decisions (although, as I will explain below, it is unlikely that everybody will benefit from AVIR equally). Second, by providing a complementary, more contemplative, method of research AVIR can compensate for the current overreliance on, including overconfidence in, quantitative investment analysis, thereby possibly contributing to a healthier, more balanced market mind. AVIR software complements S2’s existing analytical software exactly because it enhances S1 capabilities rather than suppresses them. In my view, this form of augmented intelligence is where true synergy between ‘man and machine’ will occur.
Before discussing AVIR from section 9.3.4 onwards, I will provide some background and further clarify my motivation.
9.3.2
Background and Motivation
Like any conscious entity the market expresses a broad range of behaviours, varying from rational to emotional. We are particularly interested in those mental categories that originate in the unconscious (S1) and/or culminate in the phenomenal (S3) domain. As discussed in the Ouroboros metaphor, as endpoints of our mind’s “strange loop” both remain outside the deliberate domain, are irreducible to deliberate functions, and escape axiomatic capture. They thus make traditional investment analysis methods inappropriate. The prime example of such a category is mood. Specifically, market moods are not contained in a representational format (in contrast to, for example, memories or thoughts). Consequently, they do not become available to deliberate functions like examination and reasoning.
MMH’s psychophysical premise of the market not only throws up thorny philosophical issues but also practical challenges in that regard. The latter concern the research methods with which we could approach the elusive sensations that complete market states. Current practice, dominated by analysis, prefers not to deal with the S3 phenomenal overlay of market conditions. In contrast, the MMH sees S3 as the twilight zone where S1 and S2 are actually realised, mostly distinctly (‘I am emotional’/‘I am rational’) but sometimes complementary (‘This analysis confirms my earlier intuition’). Crucially, like its physics’ cousins, the particle and the wave, the unconscious and the deliberate both contribute to our overall understanding but manifest differently as distinct sensations in S3.5
There are a few important points to repeat and further clarify here, also aimed at other researchers of markets:
To recognise the market as a collective animated entity and to appropriately interpret its communication accordingly is the message that this subchapter is trying to bring across. Specifically, 4E cognition suggests that price qualia are also intersubjective.
On that note, the U.S. sociologist Robert E. Park coined the term collective behaviour and defined it as “the behavior of individuals under the influence of an impulse that is common and collective, an impulse, in other words, that is the result of social interaction (emphasis added)”.6 Translated in terms of the MMH, market behaviour is the behaviour of market participants under the subliminal influence of prices that are the result of price discovery by way of exchanges (i.e. trades).
Subliminal because some of these behaviours, including embedded nuances and intentions, are not immediately picked up consciously by the participants (let alone observers, for that matter). Specifically, market data can contain patterns which are non-random and have tacit meaning. That is, patterns can at a subliminal level reveal information about the more primordial expressions of the mentality of a market state, e.g. emotions. The subsequent revelation, let alone epiphany, is an experience which is dynamic: as trend, reversal, squeeze and so on. This dynamic character is important: market data, either recorded or live, needs streaming to convey such meaning. It is part of the overall discovery process, while distinct from any cognitive deliberations about the market state that take place in analysis. Such revelation also influences subsequent behaviour.
More broadly, primordial expressions are largely uniform across cultures and generations and therefore instantly recognised at the subliminal level. They particularly occur in forms of art because art is the epitome of such communication which can help to coordinate and shape society, especially via culture (Klamer, 1996; Mulgan, 2023). This is one reason that most economic narratives contain key elements of myths and similar eternal themes that art and other creative forms express. The ability of art generally to convey meaning depends on the objectivity of the symbolism used, thus underlining the power of numbers and their relationships as they are the ultimate objective symbols. An obvious example of this power is the use of the Fibonacci sequence and its resulting golden ratio (e.g. via fractals) in architecture, music, paintings, etc. (e.g. Hofstadter, 1979). Above all, comprehension of tacit meaning adds to experiential knowledge, gained through a qualitative synthesis rather than a quantitative analysis. Again, this is conveyed when market patterns are experienced dynamically with ‘live’ prices (i.e. for historic time series this means bringing recorded prices back ‘alive’ by streaming them).
In short, to comprehend the market’s full state we deal, first, with prices as the symbolic expressions of its mind. Being numbers they are the most objective symbols available for shared meaning across cultures and generations. Second, we focus on the qualitative aspects of these symbols via a non-analytical technique, aimed at:
grasping the total situation … For obvious reasons, a cognitive operation of this kind is impossible … Judgment must therefore rely much more on the irrational functions … that is on sensation (the “sens du réel”) and intuition (“perception by means of subliminal contents”) (Jung, 1955, p. 49; emphasis added)
It is clear that this interpretation of market dynamics is a far cry from the rational point-estimates and random patterns which the EMH advocates. By the same token, it should also be clear that both the method and the tools with which we traditionally research markets are inadequate to reveal tacit meaning in a format that appeals to the psychophysical functions associated with such understanding. Therefore I will also explain in this subchapter which method and which type of tools could potentially be used to achieve this: appeal to intuition and other S1 abilities to reveal in S3 the market’s subliminal messages, as well as its phenomenally manifested mood. If we accept that this is a potential capacity, then it can perhaps be nurtured and trained in order to develop it (as only a few seem to have a natural talent for it):
You are part of the market, you notice every small shift, you notice when the market becomes insecure, you notice when it becomes nervous … All this (amounts to) a feeling. When you develop this feeling, and not many people have it, the capacity to feel and sense the market … then they can anticipate (it) and can act accordingly. (Anonymous investor; in Knorr Cetina and Bruegger, 2000, p. 153; emphasis added)
Again, such undertaking—of developing and applying new methods and tools—should be viewed as complementary, not contrary, to analytical methods and tools of investment research.
Perhaps unusual for a proposal for an experiment, I regularly quote various experts because, as discussed, I have no empirical proof yet for my proposal. All I can do at this stage is to use these quotes to weave my arguments together. They support and clarify both the motivation and approach for AVIR as well as the proposed experiment to test it. Here I would like to use a quote from Damasio, which I freely interpret by replacing his “(living) organism” and “biological systems” with “market”, respectively “assets”:
The miniconcert of fear is ready to be played whenever the situation demands it … It may be helpful to think of the behaviour of [a market] as the performance of an orchestral piece whose score is being invented as it goes along. Just as the music you hear is the result of many groups of instruments playing together in time, the behaviour of [a market] is the result of several [assets] performing concurrently. The different groups of instruments produce different kinds of sound and execute different melodies. They may play continuously throughout a piece or be absent at times, sometimes for a number of measures. Likewise for the behavior of [a market]. Some [assets] produce behaviors that are present continuously, while others produce behaviors that may or may not be present at a given time. The principal ideas … here are: First, that the behavior we observe in [a market] is not the result of one simple melodic line but rather the result of a concurrence of melodic lines at each time unit you select for the observation. Second, that some components of behaviour are always present, forming the continuous base of the performance while others are present only during certain periods of the performance; the “behavioral score” would note the entrance of a certain behavior at a certain measure and the end of it some measures later … Third, that in spite of various components, the behavioral product of each moment is an integrated whole, a fusion of contributions not unlike the polyphonic fusion of an orchestral performance … something emerges that is not specified in any of the parts. (Damasio, 1999, pp. 87–88; emphasis added)
