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AI and Autonomous Finance Brings New Startup Opportunitiesby@ahrwhitford
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AI and Autonomous Finance Brings New Startup Opportunities

by Archie WhitfordJune 28th, 2023
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The Efficient Markets Hypothesis (EMH) posited that financial markets are perfect reflections of all the information available to its participants. If you were to subscribe to this theory, you would believe that it is difficult (or impossible) to consistently achieve above-market returns by exploiting mispriced securities.
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In this lead image, Stability AI attempts to Anime style-gen ‘Finance bros dealing with the efficient markets hypothesis in the face of AI’


The Efficient Markets Hypothesis (EMH): Then & Now

In 1970, one of the most famous and divisive pieces of knowledge in the history of markets was authored. In a paper excitingly titled ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, Chicago economist Eugene Fama posited that financial markets are perfect reflections of all the information available to its participants.


If you were to subscribe to this theory, you would believe that it is difficult (or impossible) to consistently achieve above-market returns by exploiting mispriced securities. The average punter theoretically could never find a ‘diamond in the rough’ mining stock because as soon as any new information becomes available (privately or publicly), opportunistic investors will take advantage of it and buy, sell or keep shares accordingly.


While there is some merit to the foundations of the argument, plenty of investors have proved that it is possible to achieve consistent above-market returns in ‘The Information Age’. If you were to subscribe to the EMH, the main determinants of their ability to outperform may be attributed to factors such as:


  • Execution Systems. Participants with advanced execution systems can capture advantages from new information quicker and closer to its source. Furthermore, these systems can bolster returns by limiting losses from transaction costs and slippage.


  • Market Reshaping. Investors can reap higher returns if they can correctly make bets on companies whose value can increase based on their ability to create novel technological outcomes or entire market innovations (e.g Apple, Uber). It is debatable whether this is even an advantage in a perfect information environment.


  • Information Asymmetry. Not all market participants receive information simultaneously. Institutional investors and industry experts are often ahead of the curve. So are company employees and politicians.


  • Behavioural Factors. Skilled investors could theoretically exploit irrational market pricing caused by herd mentality, ‘noise’ trading and any other number of human psychological flaws.


  • Market frictions. Regulation, fees, taxes and a wide range of other (mostly) top-down market factors that may differ from country-to-country or sector-to-sector create arbitrage opportunities that wouldn’t be available in a completely free market.


What can we takeaway from these supposed enemies of the EMH?


Very few of them actually have to do with market information itself.


While there are plenty of market pricing determinants captured within the EMH that are entirely dependent on information-heavy factors, there still remains a distinctly mimetic and human flavour to markets.



The Bell Curve : r/ASX_Bets


How do ‘efficient’ markets change in the coming decades?


Sam Altman’s thoughts on the EMH. Taken from @sama on Twitter.


The entire information economy itself is about to be flipped on its head. Some (including the man responsible for the Tweet above) believe in a future where intelligence is too cheap to meter.


Many advanced market participants are already leveraging artificial intelligence to capture every informational advantage they can. I believe that this will become table stakes for market participation very shortly as advanced tools & methods become open-sourced and democratised.


OpenBB is a great example of an early effort towards this end.


Given that this advantage will likely be obfuscated away, how does ubiquitous, more powerful AI change markets?


In essence, I believe that most answers to the above question can be summarised under one umbrella sentence.


AI’s biggest impact on markets will be that it changes what constitutes an informational advantage.


Firstly, this will occur as AI opens up information for market factors that were previously less information dependent. The key to this trend will be natural language processing (NLP). In the recent era of finance, centralised data aggregators have been key players in markets as oracles for all different types of models.


With NLP, anyone can create their own indices from data that doesn’t necessarily rely on reporting from central banks or centralised stock exchanges.


Personalised information delivery, also enabled by NLP, will ensure that any information coming through can be customised within the context of the models that actors are using to filter or process investments.


Secondly, market structures will change as agent-companions give us the ability to produce and disseminate net new knowledge at scale (I discuss this in further depth in a previous piece here). It seems whacky to think of now, but there is a genuine possibility that with the knowledge production capabilities that agents may accrue in the future, everyone will produce their own information asymmetries.


Lastly, once agents become the dominant players on markets, the dimensions of non-informational efficiencies like behavioural factors change drastically. If we outsource the financial world to agents, human error & emotion is removed. Rationalists rejoice. Only, this seems unlikely to happen.


If agents are all programmed to act the same within a market the market itself becomes homogenous. This would lead to 1:1 correlation, high stability and extremely low returns. There is an argument that this might be a good thing for living standards, forward predictability and general stability. Maybe the world might look better if we had functioning resource allocation mechanisms not solely optimised for excess returns.


However, it is undoubtedly an affront to progress. Contrarianism is a necessary ingredient in pushing the dial forwards. This is why it is rewarded in the markets of today. Rather than a homogenous hivemind, I expect that an agent-led financial market would develop its own irrational idiosyncrasies as they chase novel discoveries, even if this is not in aim of returns.


Some examples of how these elements above may play out in the real world and the tools that will be used in these markets:


  • Open-source models for guidance on potential regulatory changes
  • Open-source risk management and hedging protocols
  • Markets for betting on new knowledge creation
  • Collaborative cohorts of agents competing against one another to test models
  • Autonomous mechanisms for protecting against and punishing market manipulation
  • A wider spread and penetration of prediction markets to take advantage of qualitative information asymmetries
  • Markets for speed-to-market on new knowledge discoveries


Just as importantly, what won’t AI change in the context of markets?

AI will change human behaviour. By providing us with omniscient buddies, the nature of the ways that humans search for meaning and communicate with one another will change drastically. This statement is also true for the mobile revolution, but expect it to be amplified this time around given.


What won’t change is scarcity. For as long as we live in human bodies, we will require food, clothing and shelter. The scarcity of these will mean that these markets still exist. The question then becomes how AI will change the methods in which value is transacted across these domains? Some predictions:


These resources will see mass securitisation. Creating liquid, fair markets for these assets allows them to be transacted at something approximating real market values - indicating a fair allocation of resources.


These resources will see mass tokenisation. Autonomous agents will need to exchange them with autonomous currency. This will become the means for facilitating these markets.


We will gain more data about these real-world assets than ever before. In order to create fair markets for these assets, moral hazards need to be eradicated. This will be done by developing advanced pattern matching and observation systems and open-sourcing these to allow anyone to evaluate any good. Before you inspect a house, you will know that its low price can be attributed . You will know that apples are being discounted at the market because they are rotten near the core. Every consumable good and service will not only be accounted for, but explained.


Interestingly, even in the new internet humans will not be the only creatures who scarcity applies to. Agents will require their own financial markets to allocate finite resources such as compute, memory, access to real-world actuators and much more. Even if they don’t have the human ego urging them to outcompete their neighbour, they will still need to replicate these markets for themselves.


Information will matter in these markets just as they will in human ones, but there may still be room for irrationality and behavioural nuance on the agent level. Strap in for a wild ride.


Requests for Startups

Attribution Systems for Actionable Information. Where information comes from is important. This is even more so if it can provide a well-reasoned, contrarian viewpoint to what is reflected in the market.


A primary example of this is the effect that short-sellers reports can have on the market. See Hindenburg’s exposition of Adani for a prime example of this. A market participant worked to discover new information about a security that was both a) not publicly available and b) not a common input in algorithmic trading inputs (human error and corruption).


By uncovering and disseminating this information, Hindenburg enabled holders of Adani shares to capture a more truthful value of the company whilst also creating significant opportunity for fast-moving short sellers to profit.


The impact of Hindenburg Research's revelations on Adani stock. Source: Statista.



While Hindenburg prepared and promoted this report with the aim of improving upon their own short position in the stock, the advantage it accrued to other honest actors in the market should be rewarded.


While skin-in-the-game is important, there should also be sufficient incentives for independent researchers to uncover impactful truths about certain businesses. I believe that the best way to do this is by designing direct information-to-money pipelines that allow people to be directly rewarded for providing contrarian truths.


How can this be done and correctly attributed for?


The best idea I have as it stands is that of universal APIs or plug-ins that people/agents can trade directly from. As they parse articles or reports for potentially valuable information, they can execute transactions directly from the webpage which are then immediately attributed to its author(s). From this attribution, authors can earn commission if it can be proven that they aren’t receiving risk-free rewards from the entity they are promoting.


Beyond being good for contrarian creators that add value, such a mechanism is also a really neat way of seeing which information is valued within markets. The big question remaining, however, is how to deal with the issue of shilling? Similar affiliate attribution systems exist in markets today but are extremely scammy and tend to be predatory towards retail investors. How can we make information attribution systems that appeal to a more sophisticated crowd?


Open-Source Tools for Navigating Markets

This has been covered with a fair few examples highlighted in bold above, which indicates its likely prevalence and importance in the information-abundant markets of the future.


In order to gain any kind of advantage, market participants will have to i) rely on different sets of data to competitors, ii) apply different models to try and seek an advantage, iii) and iv) likely many other methods that we won’t think of until they come into practice.


Incentivised Markets for Truth

This sits here as a sort of aside to the two points above. While markets are able to make their own judgements about what they deem truthful information to act upon, there is still a need for incentives for people to not attempt to manipulate markets.

Again, there are also inbuilt human mechanisms for punishing liars and thieves. However, as with many of our inbuilt mechanisms, these are prone to manipulation. If incentives are built in from the beginning, actors have an immediate opportunity to gain from publishing truthful information with good intent rather than trying to manipulate others for their own gain.


Arweave’s persistent proof-of-provenance acts is an existing example of how these kinds of markets might play out.


While contrarianism is the greatest driver to human progress, it means little if there is no basis behind it. That is why we need these kinds of markets for truth.


Re-biased trading agents

A lot of doomerist AI discourse tends to think of autonomous agents as being a kind of hivemind that acts with complete, cold rationality. Yes, agents are designed to optimise for certain objective functions. But it is also true that different agents can optimise for different objective functions.


In a world where the proliferation of AI further accelerates the creation, dissemination and absorption of new information, the creation of agents with different behavioural biases arising from different objective functions can restore the necessary inefficiencies for a competitive market of ideas.


If the financial markets continue to exist as they do today (which is a subject for another day), trading agents designed with different biases and parameters in mind will be the primary combatants of herd mentality if we trend towards a market dominated by autonomous agents.


Securitisation of Underexplored, Real-World Markets

As it stands, we are significantly closer to zero-cost, abundant intelligence than we are to cheap energy, cheap food, cheap clothing, cheap housing or any of the other things that keep us fed, warm and alive.

Estimates of the global illiquid asset tokenisation opportunity. Source: Boston Consulting Group.



In order to create truly efficient markets from this securitisation of real-world assets, a few precedents will be required. This is a void that can be filled by clever entrepreneurs.


The first and most obvious opportunity is an exchange built for real-world assets. This would need the trust of (possibly agentic) market-makers, transparency on valuation mechanisms and standards for classifying any given category of real-world asset.


Secondly, the need for granular data in order to explain prices for these goods. As covered briefly above, no one should have to pay what they deem to be a fair price for a house only to find that there is asbestos in the roof. The issue the entrepreneur has to solve is how to achieve this granularity at scale.


This may be a problem that is solved asset-by-asset. The means for getting granular apple data are different than that for houses. Already we have seen a proliferation in supply-chain tracking platforms that may prove a precursor to a movement towards more detailed customer data. How are tracking elements implemented at source, even for things like supermarket goods? How can these be done at a unit-by-unit level rather than at the company level?


The data problem may be the most valuable to solve first. Once this is in place, it lays the necessary groundwork for functioning exchanges that customers and market participants can trust. Just as importantly, it also makes human consumer markets more trustworthy. This is something we could do with today.


Monetisation of the Informal Economy

This might seem a bit offbeat from a lot of the abstract, forward-facing ideas covered above. But it may just also be the most relevant and immediate problem to be covered here.


3 ways to get Africa's informal economy on the books | World Economic Forum

Even though their share of GDP seems to be gradually declining, informal activities still comprise a large portion of national income across the world.


The informal economy in developing countries consists largely of cash-heavy, compliance-light activities. These may include things like:


  • Street vendors
  • Informal labour & construction
  • Entirely unregistered business activity
  • Informal transportation services
  • Subsistence Agriculture


Some of these activities occur in the developed world as well. I, for one, used to work for a Fish & Chip shop that operated for 25 years without needing to register. I am not going to rat them out because I am fairly certain they will not have done so by now.


However, beyond annoying government revenue collection agencies, these kinds of activities still put money in people’s pockets and food in their mouths. But what about markets that demand a lot of time for little reward?


The core component of this section of the informal economy is unpaid care work, i.e housekeeping. The easiest example here is a stay-at-home parent who works a part-time job to feed a family whilst also having to cook, clean and take care of children. How or why might a market be tempted to consider rewarding these people?


A low-effort meme from yours truly


Idea #1: Peer-to-peer carework marketplaces. People are already dogsitting, driving and staying in other people’s homes en masse via peer-to-peer marketplaces. Babysitting has provided a way of outsourcing child care for short periods of time in exchange for value since time immemorial. Yet we have yet to see a successful model for peer-to-peer daycare that allows one parent to simultaneously take care of multiple children at once.


There are obvious psychological and regulatory battles to be fought here. It would have to be one of the highest trust, watertight KYC platforms in history. However, the value it could deliver by allowing informal care workers to kill two birds with one stone (earning an income whilst performing house care) would be immense at potentially GDP-level scale.


Idea #2: Social Impact Bonds for Care Work. A large part of the issue for informal workers from lower-income backgrounds is the liquidity issue. By not having enough money on hand to pay for short-term needs, these carers can be stuck in a form of poverty cycle.


But what if they had access to liquidity that didn’t necessarily incur the same kind of predatory rates that payday lending does? There is an exciting vision for would-be fintech founders to work with financial and government partners to devise a form of (hopefully tax deductible) social impact bond that can be used to finance the unpaid careworker’s short-term needs.


These bonds should be long-term with ambitious, pre-defined payback milestones. One example would be that the bondholder can take a small fraction of future income once the bondholder achieves the median GDP per capita. While this kind of idea may seem at odds with some of the free market ideals posited above, it is an opportune way to ensure that the future of our economy still allows people to put food on the table at any given time whilst not being dependent on government handouts.


Also published here.