The Market Mind Hypothesis, page 16
Related is the decrease in the number of new company formations and listings, diminishing the healthy cleansing by creative destruction.19 The Sunday Times reports that start-up rates have been falling in 16 out of 18 western economies over the last few years.
This is also reflected in the growth in zombie, or undead, companies. Deutsche Bank estimates that their share of total US companies reached almost 20% in 2020. Zombie companies are dead except for the fact that cheap money keeps them alive (e.g. via bailouts). Being undead, they merely pay the interest on their debt. Unfortunately, because this allows them to compete unfairly they weaken healthy companies, thus further infecting the economic system like true zombies. In a 2018 report the BIS concludes: “Lower rates boost aggregate demand and raise employment and investment in the short run. But the higher prevalence of zombies they leave behind misallocate resources and weigh on productivity growth …”. (Banerjee and Hofmann, 2018)
The industry behemoths employ various tactics, particularly tweaking the machine of bureaucracy. Corporatocracy, and more specifically crony capitalism, spawns policy making by friendly entities, using revolving doors between the public and private sectors; its members are not elected democratically or selected competitively. Their decisions are thus not subjected to public scrutiny, respectively market (e.g. bargaining) forces. They are blind to conflict of interests because they see their interests as ‘aligned’. It translates into mechanisation in the real economy mostly by means of favoured (e.g. lobbied) ‘solutions’, including automatic (e.g. ‘IF-THEN’) budgets, guarantees, subsidies, and other measures. For instance, after a number of interim phases TBTF (Too-Big-To-Fail) banks have now morphed into TBTC (Too-Big-To-Care) because a central bank decides that IF a bank is about to fail, THEN we bail it out even though they may be TBTS (Too-Big-To-Save). The drawback is that these policies often become addictive or predictable and thus can lead to economic misbehaviour, especially moral hazard. While this is sometimes recognised and corrected, more often such schemes are inherited and deemed hard to remove, leading to ‘automatic renewal’. Again, the underlying current of growing concentration of wealth, e.g. via stock ownership, is a related worry.
3. Investment
The mechanical view is held by many investors (e.g. Dalio, 2015), with Wall Street supplying the engineering tools. Mechanisation, backed by the assumption of quantification, manifests itself in two forms which I touched on briefly before. First, it appears as complex (derivative) products that are ‘structured’ and modelled on computers. One example is the notorious CDO (Collateralized Debt Obligation)—made famous in the movie The Big Short20—which promised investors payments from pools of loans packaged in various risk tranches. It turned out many CDOs contained toxic loans such as subprime mortgages or even other CDOs (CDO-squared) and they eventually contributed to the GFC. John Thain, former CEO of the NYSE and later Merrill Lynch, explained the dangers of such machine-dependency:
To model correctly one tranche of one CDO took about three hours on one of the fastest computers in the United States. There is no chance that pretty much anybody understood what they were doing with these securities … I think that the degree of complexity that was created in the securities, and the lack of anybody’s ability to really understand … was simply an error and a bad thing. (Thain, 2009)
Second, it is increasingly applied in investment management by means of mechanical (or computerised, also known as systematic) investment strategies that are ‘monitored’ by—mostly pro-cyclical—value-at-risk (VaR) policies. For example, the stigmergic-like algorithms behind so-called momentum strategies work along the lines of ‘IF the index goes up (down), THEN we buy (sell)’. The origins of mechanical investing go back to earlier crude strategies, like portfolio insurance that contributed to the 1987 crash. Nowadays this includes passive investing, high-frequency trading (HFT), liability-driven investing (LDI), robo advisors, and automated trend-following (e.g. CTA) approaches, as well as more advanced variations like volatility-control and risk-parity. Theoretically these strategies are supported by modern finance, a nested discipline within mechanical economics. Unfortunately, as the former derivatives trader Satyajit Das (2010) pointed out, modern finance is incomprehensible to ordinary men and women, which is often exploited by the industry. Still, the amount of assets managed by such strategies has grown significantly, a trend that is expected to continue. Advocates argue this is justified by low-cost access/liquidity, relative performance, and other ‘benefits’. However, as a form of momentum trading passive investing causes its own version of market concentration in popular indices. For example, the S&P500 has experienced a record decline in the number of companies required to replicate its performance (which is the essence of index-tracking). This is because these strategies, in their artificial mechanical way, direct capital flows into the least number but often biggest index components to achieve such replication, all in a bid to minimize expenses. This can create a positive feedback loop.
As indicated in Chapter 1, a larger problem is that many mechanical strategies only use internal market data for their trading without reference to the fundamentals that determine asset values. Consequently, as they become dominant Mr Market turns inside himself, as it were, caring less about the economy or the world for that matter. Stated differently, and keeping Hayek’s arguments of distributed local knowledge in mind, any trickle down of fundamental news via (assumed) information efficiency is diminished—if it occurs at all. Crowded inward-looking strategies as well as the related concentrations in wealth, market making and so on privatise information and further decrease the number of conscious market participants, and thus idd-minds. This leads to an unhealthy narrow-mindedness in Mr Market, with knowledge and awareness not sufficiently distributed. In other words, these strategies do not do discovery and do not contribute to bridging the economic mind~body. The main reason some of them have been ‘successful’ is because they piggyback on the wider and growing mechanical treatments that operate on the economic mind~body. This makes them largely speculative and, in some cases, parasitic or predatory.
The GameStop and other meme-stock sagas are illustrative for this, and I will summarise an earlier article I wrote for Jackson Hole Economics here, which highlighted key conclusions and other insights from an MMH perspective. I have also been guided by Charlie Munger’s dictum that showing the incentives can tell you the outcome. Overall, it yields a broader message—aimed specifically at policymakers—on how to fix our broken markets:
1. Investing: Automation makes investing seem effortless but if investors are not incentivised to learn about investing, its outcome will not be understood and will frequently disappoint. Einstein advised to make things “as simple as possible, but not simpler”. The latter was a warning, one not heeded by mechanical economics. Mechanical economics has made investing easier and simpler—through exchange traded funds (ETFs), smartphone-based trading apps and cheap margin debt. But on the back of an absurdly complicated theory, mechanical approaches have also made markets more complex, for example via derivatives, black-box algorithms and distorting monetary policies. There is now growing tension between these two developments. For instance, while information asymmetry has always been an issue in markets—institutional ‘smart money’ invariably has the edge—the inequality in financial literacy and investor education is a big problem. In the case of GameStop, even if the instigators were experienced and well-informed traders, the majority of followers—the proverbial ‘greater fools’—were not.
2. Business models: Retail investors are not the customers of Robinhood and other commission-less trading venues. Instead, HFT market makers—like Citadel and Virtu—are their customers and form the heart of their business model, called Payment-For-Order-Flow (PFOF). Basically, the flow of orders from retail investors is the product that Robinhood sells to HFT firms. The latter subsequently make money from such flows in a mechanised, riskless manner that many consider predatory. Although HFT firms deny front-running,21 executing personal orders by free trading comes at a hidden cost, similar to sharing personal data from free services on the internet. In short, Robinhood’s claim to “democratise investing” sounds as hollow as Google’s infamous “don’t be evil” motto.
3. Control: Worrying linkages between family offices, hedge funds, market makers, and trading platforms have been revealed by this saga. Something similar occurred between SVB and its venture capital backers. Crucial plumbing and liquidity of financial markets is increasingly dominated by a cabal of unlisted private entities, owned by the top 0.1% of the top 1%. Opaque rules by clearing houses further obscure a transparent view of collateral, leverage, margin, and other risk factors. Actions are taken behind closed doors. In MMH’s terms, Mr Market’s body receives unsupervised treatment—including organ shut-downs (arbitrary trading outages) and shock therapy (self-serving bailouts), often obscured from public vision.
4. Conflicts: In their criticism of both short-sellers and the Reddit retail crowd many commentators have pointed to the numerous instances of conflicts-of-interest. One that stands out was the ‘investigation’ planned by US Treasury Secretary Janet Yellen. It is awkward for two reasons. First, as former Fed Chair she is co-responsible for creating the easy-money conditions that over-greased the market-trading machines. Second, as widely reported, after retiring Yellen got paid almost US$ 1million by Citadel for guest speeches. Call me cynical, but by the time you read this I suspect that this investigation has either been cancelled or has not produced any impactful results.
5. Manipulation: In the real economy this includes earnings manipulation which seems especially prevalent ahead of recessions. Accounting professor Messod Beneish of Indiana University developed an indicator—the so-called M-Score—that attempts to flag this risk by tracking various balance sheet and profit-and-loss variables. It recently reached its highest readings since the late 1970s. In terms of track record, it previously raised red flags at Enron and Wirecard prior to their accounting scandals and eventual bankruptcies. Crucially, manipulation also occurs in the financial system. According to securities laws market manipulation means that an artificial price is created or maintained in a security. Regulators will investigate, for example, whether manipulation took place in meme stocks. However, such laws do not apply to central bankers. Yet appearances matter. Central banks set bad examples by buying distressed assets, bailing out fellow bankers, setting negative interest rates, ‘tolerating’ Libor rigging (Verity, 2023), and creating positive wealth effects for the already rich, all of which fuels anger and frustration among the 99%. As an aside, should financial education improve, thereby raising awareness of these matters, that anger will only grow.22
6. WIT because YOLO: Whatever-It-Takes because You-Only-Live-Once. These are two mantras of the retail trading crowd (perhaps inspired by Mario Draghi’s earlier commitment to saving the euro). The meme-stocks sagas are a symptom of the larger issue. It appears that a significant number of these traders have been motivated by factors beyond profit maximisation, including revenge. That may be irrational, but it is also a warning. There is a genuine risk that attention-induced trading (e.g. Barber et al., 2022) turns systemic, particularly now that social media chats can swiftly refocus on more dangerous short-squeeze candidates. Before long, radicalized traders may find ways to sow financial stress or even create a more devastating (digital) bank run—the finance equivalent of France’s gilet jaune (yellow vest) protesters. This is hardly a fruitful direction of travel.
These three areas of policymaking, business, and investing do not stand alone. The MMH asks a simple question: “what happens when you treat the market, which is a collective extension of conscious minds attempting to discover prices, as an automaton?” One can then recognise various symptoms of serious maladies affecting our economic mind~body. Take the MMH’s emphasis that investing involves practical dualism: a mental ‘thinking’ side (like investment decisions) and a physical ‘action’ side (like trade execution). Those incentivised to facilitate the latter, like HFT-firms, do not care much about the former. Stated differently, they care about quantity (frequency of trading; the more the better), not the quality. Making investing physically more ‘efficient’ doesn’t necessarily improve investors’ mental understanding and resilience. Arguably, it makes them more vulnerable. It is another example of the imbalance between the mental and physical.
The ongoing market mechanisation that turns Mr Market into a ‘gamed’ machine is compounding the unintended consequences. There are many straight-forward measures that policy makers can take to address the issues raised above. One improvement would be to separate financial firms and their regulators by using much stricter rules to prevent lobbying, revolving-door appointments, post-retirement speeches/endorsements, and other conflicts-of-interest. Another would be a Tobin HFT-tax. In Chapter 12 I offer more suggestions. Longer term, the MMH posits that economics needs to revise its flawed paradigm that justifies and motivates self-reinforcing mechanisation. By extension, genuine investor education—taught through a new curriculum—is needed to improve investment outcomes. And that requires more than hearings and investigations conducted by conflicted parties.23
I want to return to the importance of balancing internal and external data and the risk of diminishing market efficiency, in this case, informational efficiency.24 The financial economy’s informational efficiency operates in two directions in that regard, outward and inward. It operates outwardly in terms of funding efficiency: efficiently allocating capital for the real economy, which requires that it focuses on economic or external information. The financial economy operates inwardly in terms of arbitrage: no free lunch, which requires that it focuses on market or internal information. The financial economy’s informational efficiency, the extent of correctly assessing states of the world, thus follows from its ability to consider both external and internal information. Where things go wrong is when the financial economy becomes dominated by market participants who only focus and trade on internal information. Again, this is increasingly the case due to the growth in passive investing, other systematic strategies (like CTA trend following), as well as derivative strategies (like so-called option-gamma trading) that impact the underlying securities.
An additional aspect arises from the level of consciousness involved in generating this information. Based on earlier work by Hayles (2017; see also Appendix 1-A) on the technological unconscious in trading, Beverungen and Lange (2018) investigated “the ways in which the ‘costs of consciousness’ are accounted for and negotiated in high-frequency trading”. They conclude that “traders actively develop modes of awareness accounting for the costs of consciousness, and that the necessary ‘stupidity’ of high-frequency trading algorithms as well as competition pose limits to the full automation of financial markets”. Still, and more broadly, removing consciousness by outsourcing to mindless machines does not help adaptation by the overall economic system to states of the world. Meaningful efficiency is not about HFT in that regard.
More generally, short-term efficient (e.g. promoted by the EMH) does not necessarily mean long-term optimal. Neither does market health necessarily improve by raising the number of transactions or lowering bid-ask spreads. Increased conscious participation by a growing number of idd-minds is. Why? Because, apart from distributing knowledge, it diversifies awareness and sensemaking. Knowledge may apply to so-called small world situations with risk (but remember reflexivity25). However, we can only hope to be aware and try to make sense of a large world with uncertainty. Such heterogeneous mentality is the source of true liquidity and when it dries up, it reflects a cognitive black-out and disengagement. There is also a related data issue. Prices further back in time were set by conscious humans via discretionary trading, that is, “human volume”. More recently, prices are increasingly set by machines through systematic trading. Apart from the statistical implications of such sample difference, we should question the broader meaning in terms of the reflected mentality-as-information in prices.
I highlight these areas because that is when the problems start: when mechanical economics’ view turns into machinations namely actual treatments, or rather mistreatments. At that moment initially innocent thought experiments, laboratory trials, and computer simulations with flawed assumptions turn into real-world ones with damaging tangible impacts that have become very expensive for society. This is all very questionable:
Surely the man who would undertake to treat human society merely as material for scientific manipulation, to control it by finding the laws of its response to stimuli and devising stimuli to provoke the responses he might desire, would have to be classed as a monster or an imbecile. He might have abundant intelligence, of the scientific sort, but would be lacking in “sense”. (Knight, 1925a, p. 389)
Specifically, manipulation equates to actual mistreatment of the mind~bodies at all levels in the economic system because it distorts26 perception (of reality) and risks suboptimal behaviour. This will be explained further but in short, think of your own mind~body. It is healthy if it is free (e.g. for your mind to think and for your body to move) and unhealthy if not.27 What we will diagnose is a form of iatrogenesis, where a treatment that should cure instead causes and/or exacerbates a disorder in the mind~body. Consider these medical examples. Fentanyl is a medicine, an opioid painkiller that can lead to addiction. Hypnotherapy is used to treat conditions or change habits but can result in self-deception manifesting as false memories. As indicated, in the case of Mr Market the mistreatment is in the form of manipulation, particularly price manipulation. This is primarily due to financial repression, financial engineering, and other interferences that supposedly serve ‘the greater good’ but actually limit discovery. Those who commit economic iatrogenesis act against economics’ Hippocratic Oath: primum non nocere (first do no harm) to the economic mind~body.28 This argument follows from accepting the Extended Mind Theory, because it requires that we update our notion of ‘abuse’, to include manipulation and other (intentional) harm done towards the extended domain of our minds that forms part of the market mind (for the general argument, see Carter and Palermos, 2016).29
