From Latency to A.I. Algo Driven Capital Markets
Over half a century has passed since Electronic Communication Networks (ECNs) disrupted the traditional floor-based model of stock exchanges. Ushering in the era of electronic trading. Since Regulation National Market System (Reg. NMS ) was adopted almost 20 years ago, the honorable goal of promoting venue-by-venue competition and fair price formation across securities markets turned into a latency arm race . Pushes market data and connectivity costs to rise exponentially.
In the quest to gain an edge over competitors, trading venues offer different rebates (e.g. enhanced market-making discount), introduce a speed bump (e.g. liquidity enhancing access delayed), proliferate order-types (e.g. midpoint-extend-life order), come up with new business models (e.g. market-on-close) and create other privileges (e.g. exclusive access to certain pegging orders). People jockeying around to make money by altering the queuing and wait times at the "checkout counters". This has widened the gap between the haves and have-nots in the past.
It is a fitting moment to reflect on the evolution of capital markets. Several trends are emerging that could push today's market structure from a focus on latency to one driven by algorithmic and artificial intelligence (A.I.) technologies:
If you can’t beat them, join them (outsourced execution):
A decade ago, former US SEC Chair Mary Jo White famously said, “deemphasize speed as a key to trading success.” Whether it is regulatory inaction, or the current administration’s proposing a wrong prescription , market participants today still find it hard to beat the high-frequency trading (HFT) firms. So long as the Consolidated Tape is NOT a reasonable compromise if not a close substitute of Exchanges’ proprietary feeds, everyone is and will continue be subservient to telecom infrastructure vendors. There is not enough alpha for the HFTs to justify the costs and benefits in doing just proprietary trading. Thus, HFTs get into the business of outsourced trading execution services. With stock exchanges optimally restricting access to price information by exploiting the inelasticity in demand of proprietary products, in turn, many choose to collaborate with the Haves for outsourced execution rather than compete.
This “collaboration” aims to maximize/ segment order flow for negotiation of tier rebates and other incentives that pose potential conflicts of interest and BestEx issues. The latency arm race would only subside if time-lock encryption is adopted to protect time sensitive data from being decrypted prematurely. The voluntary collaboration among the outsourcers and HFTs at echo chambers further complicates the markets. Aside from using transaction cost analyzers, liquidity sourcing and other tools or “bandages” to fabricate the fragmented markets, market participants are increasingly leveraging advance technologies, such as A.I., algo wheels, quant models, etc. to navigate and stay afloat in the markets.
Despise echo-chambers’ polarization and trends toward a more diverse group of participants
The 20th century marks the beginning of a cyber punk era. Elites (Corpo) advertise their selected truths popular among those who rely on corporate thinking rather than developing an ability to think independently . In the opposite spectrum, the rebellious mobilize the online communities (street kids) to orchestrate significant market movements. This is exemplified by the MEME stock phenomenon , where the na?ve were feeling enlightened or motivated by their “leaders” to do a gag that would otherwise be prohibited if it occurred at a broker-dealer, then top market-makers were being lambasted to advance the rebellious’ controversial agenda.
There are the digital nomads, infusing “trust” (analogous to MBS credit enhancement) into digital assets while they are building related financial infrastructures to rent seek on transactions flowing across. Foreign adversaries would like to see the US engaged in “unhealthy” competition to erode the US's prominent market position. Many do not seem to realize the emerging threats against capitalism or dare to admit it. DeFi and De-dollarization movements are on the rise and reap benefits out of chaos. Witnessing this dynamic, market participants would need their algo and A.I. to guide them in discerning who’s who and doing what, that impacts the markets.
One size does not fit all and mass customization
Different trading venues (lit exchanges, dark pools, systemic internalizers, single dealer platforms) are like different streaming platforms providing various contents (e.g. block trading, exotic, passive, conditional) that fit the appetite of respective subscribers. While we are generally supportive of standardization and harmonization of regimes, policy makers should also be warned that open access and interoperability could inadvertently be a path to a monopoly or reinforcing the elites’ oligopoly that hurts fair competition.
SEC Commissioner Peirce reminds “Hardwiring a technology into a rule runs the risk of preserving that requirement far after that technology’s expiration date” [amid Bloomberg is offering the open source FIGI reference data for FREE] with respect to the US joint data standards proposal in implementing the Financial Data Transparency Act . Also, no matter how Data Expert Group(s) are working with regulatory authorities in calibrating the consolidated tapes, reference price arbitraged is inevitable when there is more than one de facto NBBOs under the US competing consolidators model (or EBBOs in the UK and EU). If the public markets opt to serve only the most liquid and static form of investments, the private markets will prevail for its flexibility to dynamically customize deals for the investing communities.
From automated intelligence for economy of scale to decentralized federated learning
Some said “A.I. stands for Automated Intelligence”. We agree that automation benefited the elites to achieve economy of scale in the past. However, federated learning and real-time analytic platforms (RTAP) are more efficient and effective to provide timely decisions and rapid responds in modern dynamic markets.
Today, many resources are devoted or wasted in sourcing alternate data. Too much data and inherent problems (timestamp synchronization) of data imprecision causing centralized intelligence to take forever to achieve ‘golden source’. Then, people are juggling to insert or remove nodes for tunning/ data corrections (e.g., Support Vector Machine requires labeled data), while not knowing valuable insights may inadvertently be removed from the dataset. In turn, inexactitude in trade sequencing caused analytic results based on vector measurement or visualized heat-maps to be erroneous (false +/-).
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The trend is empowering decision points to the field . Non-developers are now able to use no/low-code generation with A.I./ large language models to do some ad-hoc tasks for WYSIWYG rather than relying on the development team. Whereas the development team uses these tools to take on more complex projects, concentrate on quant modeling, reverse engineering of others’ algorithms, better Boolean logic, taxonomy, and whatnot.
Market Volatility, A.I. Hallucinations and the surprising similarities between Music and Trading:
Trading teams are poking holes in the initial results of A.I., which they are right to do so because there are different machine learning models. Firms should not go blindly with neural network, deep learning black boxes, then go about craving for evermore data to feed the models. While extraordinary ? market volatility and suspicions of A.I. hallucinations persist, there is also a growing recognition of the opportunities presented by newfound signals and liquidity amid chaos.
The performance of machine learning will improve over time. It may discover unknown and hidden opportunities which were previously nonsensical to humans. It is a paradigm shift to transition from a latency-driven capital market to one powered by algorithmic and A.I. machine learning technologies. Be open minded to explore the surprising similarities between Music and Trading . The sound that data makes is well suited to time series.
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Data Boiler Technologies, LLC is a Type C organization member of the European Commission’s Data Expert Group . Between my patented inventions in signal processing, analytics, machine learning, etc. and the wealth of experience of my partner, Peter Martyn, we are about Market Reform, Governance, Risk, Compliance, and FinTech Innovations to create viable paths toward sustainable development and economic growth.