By Alfredo Baraldi
Abstract
This study examines the evolution of technology in high-frequency trading (HFT) and the role of scientific research in financial innovation. Through the analysis of empirical data and theoretical models, the research demonstrates how the “race to zero time” has reached nanosecond levels, driven by advanced hardware innovations such as Field-Programmable Gate Arrays (FPGAs) and speculative execution. The study also analyzes how companies like Capital Fund Management (CFM) utilize academic research to develop innovative systematic strategies. The results show that the share of orders modified within one millisecond has increased from 11% in 2019 to 17% in 2024, while the integration of theoretical research and practical applications continues to redefine financial market efficiency. The research concludes that the balance between technological speed and scientific rigor represents the future of algorithmic trading, with significant implications for market structure and financial regulation.
Keywords: High-frequency trading, FPGA, market microstructure, market impact, speculative execution, technological innovation
1. Introduction
The evolution of modern financial markets is characterized by a dual revolution: the relentless race toward execution speed and the systematic integration of scientific research into trading strategies. This transformation has brought high-frequency trading (HFT) to operate on nanosecond timescales, while systematic trading companies employ world-class research teams to develop increasingly sophisticated mathematical models.
The analysis of 2.3 billion DAX futures orders on Eurex reveals a surprising trend: the share of operations modified within one millisecond has increased from 11% in 2019 to over 17% in 2024. Simultaneously, analysis of a single stock on Euronext (BNP Paribas) shows an almost threefold increase in order modifications under 100 microseconds in the same time period.
This technological acceleration coincides with growing sophistication in financial research. Capital Fund Management (CFM), with its nearly 100 researchers and 200 developers, represents the epitome of this convergence between academic rigor and practical application, where leading global experts in trading theory and market microstructure translate theoretical research into profitable systematic strategies.
This study examines this dual evolution through three analytical lenses: the technological transformation of trading infrastructure, the integration of scientific research in financial strategies, and the theoretical and practical implications for market efficiency. The objective is to understand how the convergence between technological speed and scientific rigor is redefining the very nature of financial trading.
2. Theoretical Framework and Scientific Foundations
2.1 Market Microstructure Theory and the Impact Function
The theoretical foundation of modern HFT is based on understanding the impact function, a central concept in the research of Jean-Philippe Bouchaud and the CFM team. The impact function describes how the act of trading itself influences prices, representing a subtle but substantial cost that scales with trading volume.
As Bouchaud explains: “It’s a very subtle effect, but it’s huge, so even though this is very small compared to volatility, it’s a substantial fraction of the cost for us, and it’s a cost that scales. It’s our main problem in terms of dollars extracted from the market.”
The famous square root law of impact relative to trade size represents one of the most important results of Bouchaud’s academic research, providing the mathematical foundation for order execution optimization. However, complexity increases when multiple operators implement similar strategies, creating the “co-impact” phenomenon.
2.2 Optimal Execution Models and Adaptive Algorithms
CFM’s theoretical research focuses on optimizing execution algorithms to minimize total impact. Unlike other companies that use broker algorithms for execution, CFM develops all its execution systems internally, requiring deep understanding of market microstructure.
This strategic choice reflects the importance of proprietary research in modern HFT. Bouchaud emphasizes that “for us, market structure is really important, because unlike other companies that go through brokers who execute their trades, all execution is done internally.”
2.3 Market Efficiency Theory and New Empirical Evidence
CFM’s work challenges the economic dogma of market efficiency, according to which prices perfectly reflect new information. The 2017 monograph “Trades, Quotes & Prices” (co-authored with Julius Bonart, Jonathan Donier and Martin Gould) embodies CFM’s guiding philosophy, which opposes traditional theory through empirical evidence and innovative mathematical models.
The research demonstrates that CFM’s high trading volume provides a unique observation point not enjoyed by typical buy-side companies, allowing observation of statistical patterns that emerge only when averaging over a large number of operations.
3. Technological Innovations: The Race to Nanoseconds
3.1 Hardware Evolution: From Microwaves to FPGAs
The previous generation of HFT relied on technologies like microwave networks to reduce latency between multiple trading venues. However, in a competitive environment, even microseconds proved too slow. Specialized chips called Field-Programmable Gate Arrays (FPGAs) have now become the industry standard for reducing latency from microseconds to tens of nanoseconds.
David Taylor, CEO of specialized HFT provider Exegy, explains: “Companies pushing into the 10-nanosecond realm aren’t just relying on FPGAs – they’re designing custom integrated circuits with breakthroughs in optical-electrical conversion and low-level networking to capture every precious nanosecond.”
3.2 Speculative Execution: Innovation and Controversies
One of the most controversial innovations in modern HFT is “speculative execution” or “speculative triggering.” This technique allows FPGA chips to start orders and then, based on sub-microsecond signals, cancel them leaving them incomplete before they are processed by the exchange.
Vincent Akkermans, former senior developer at Optiver and now co-founder of TenFive AI, describes the technique: “Using FPGAs to start processing and transmitting orders before the complete market message is received represents a paradigm shift in trading speed – pushing the limits of how fast orders can be executed.”
3.3 Integrated Architectures and Hybrid Computing
Modern trading companies combine FPGAs for the ultra-fast “tick-to-trade” loop with additional GPUs or CPUs for broader risk models and AI-based predictions. The net effect is a hybrid computing approach that unites microsecond-level decision-making with broader analysis.
Alastair Richardson, AMD sales director, envisions chiplet architectures that enable more modular designs: “Using a chiplet process – breaking a processor into smaller pieces – instead of the traditional monolithic method of large piece of silicon, we can build complex processors, with more capability and more efficiency.”
4. Empirical Analysis: Evidence from Performance and Markets
4.1 Performance Metrics and Transaction Costs
Empirical analysis reveals that modern HFT has achieved extraordinary performance levels. Virtu Financial, one of the industry leaders, showed profitable trading days in 1,277 out of 1,278 days over a five-year period, representing a 99.92% success rate.
Renaissance Technologies’ Medallion Fund provides the most convincing evidence of long-term performance, with an average of over 35% annual returns after fees for three decades through sophisticated mathematical models and quantitative strategies.
4.2 Impact on Execution Costs and Bid-Ask Spreads
Empirical studies consistently demonstrate HFT’s positive impact on market quality. HFT strategies reduce execution costs by 25-33 basis points per trade compared to non-HFT strategies, with higher trading frequencies correlating with overall reduced execution costs.
After Canadian authorities imposed HFT fees in April 2012, bid-ask spreads increased by 13% market-wide and 9% for retail investors, providing clear evidence of HFT’s positive contribution to market liquidity provision.
4.3 Market Fragmentation and Regulatory Challenges
In the United States, Regulation National Market System (Reg NMS) has forced companies to route to the best price across multiple venues, making speed crucial for instantly updating quotes and capturing mispriced orders. Europe mirrors this fragmentation under the Market in Financial Instrument Directives (MiFID I and II), albeit with greater complexity regarding data fees and pan-European connectivity.
According to Taylor from Exegy: “Market fragmentation in Europe is a significant challenge, connecting to all relevant markets can be two to three times more expensive than in the United States.”
5. The Role of Research in Systematic Strategy
5.1 Mathematical Models and Signal Generation
CFM represents the paradigmatic example of how academic research can drive innovation in systematic trading. The company employs almost no traders, relying mostly on algorithms designed by its team of 100 researchers and 200 developers.
The central challenge identified by Bouchaud concerns “co-impact”: “Sometimes the fact that they started trading improves the signal. In other cases, it makes a signal disappear or makes it too fast to be traded, or increases your costs to trade the signal.”
5.2 Competitive Dynamics and Arms Race in Research
The co-impact problem fuels an arms race between CFM and other systematic hedge funds. “This is the reason why we and our competitors, I imagine, have to hire more and more researchers because we have to innovate, get more models, understand better all these aspects to avoid in-sample bias, alpha degradation, and crowding.”
This competitive dynamic highlights how technological innovation in modern trading is intrinsically linked to research and development capacity, requiring continuous investments in highly specialized human capital.
5.3 Integration of Big Data and Alternative Data Sources
CFM and other systematic companies are increasingly integrating alternative data sources, from social media sentiment analysis to satellite imagery. Academic research provides the theoretical framework for extracting signals from these complex datasets, while technological innovation enables real-time processing.
6. Implications for Market Structure and Regulation
6.1 Effects on Liquidity and Price Discovery
Empirical research demonstrates that HFT generally improves market quality under normal conditions, reducing spreads and increasing liquidity. However, during stress periods, HFT-provided liquidity decreases by 40%, with spreads widening by an average of 10 basis points.
Analysis of sub-millisecond modified orders reveals behavioral patterns that raise questions about market structure: whether this extreme speed actually improves market efficiency or simply represents wealth redistribution through technological advantages.
6.2 Regulatory Considerations and Transparency
European exchanges have incorporated features to allow speculative execution by HFTs within certain limits. Jonas Ullmann, board member of Frankfurt-based Eurex and COO, explains: “Speculative execution refers to certain latency arbitrage strategies when market participants start sending an order before having fully processed incoming market data.”
To exploit the strategy, HFTs must occupy a gray area between raw messages and fully formed orders to the exchange, where nanoseconds count. Exchanges have implemented technical features like “discard IP” to maintain market integrity.
6.3 Balancing Innovation and Stability
Stephane Tyc of McKay Brothers emphasizes the importance of balance: “Deterministic systems – those that behave in highly predictable ways – can support more precise risk management, but like anything, too much of a good thing has trade-offs.”
A small amount of jitter can help level the playing field; too much, and you get chaotic and inefficient markets. Some venues that could be extremely deterministic have chosen to introduce intentional randomness to prevent predictability from becoming an exploit.
7. Future Perspectives: AI, Quantum Computing and Beyond
7.1 Artificial Intelligence Integration
The future could see more frequent use of AI. While neural networks may be too heavy for the tightest loop, they can assist in slightly upstream decision-making or in scanning large datasets for predictive signals.
Richardson from AMD highlights that “we’re now seeing a new latency race involving AI, with some tasks still better served by a dedicated FPGA or ASIC, while others lean on CPUs, GPUs or NPUs to make more complex AI inferences.”
7.2 Physical Limits and Future Innovations
If something can slow the hunt, it might be the laws of physics. Market data cannot travel distances faster than the speed of light, leaving only extremely clever network design and hardware optimizations to squeeze incremental gains.
However, as the last decade has demonstrated, major leaps – from microwave relays to transatlantic shortwave signals – can arise when profit potential is sufficiently high.
7.3 Technology Democratization
Taylor from Exegy points to the stratification forming in this space: “While the top-tier HFT players operating at nine nanoseconds are few and stable, the next dynamic tier – trading at deep sub-millisecond speeds across multiple assets – is where fierce competition and innovation is really taking place.”
Tyc adds that “that plug-and-play simplicity destroys the barrier to entry.” Even if the absolutely fastest level remains exclusive, widespread adoption of low-latency technologies is permeating all corners of trading.
8. Conclusions and Implications for Future Research
8.1 Summary of Main Results
The conducted analysis reveals that the convergence between advanced scientific research and technological innovation represents the dominant paradigm in modern financial trading. The evolution from HFT measured in milliseconds to nanosecond operations, parallel to the systematic integration of sophisticated mathematical models, has created a new financial ecosystem.
Empirical data confirms that this evolution has brought measurable benefits: reduction in execution costs, improvement in bid-ask spreads, and acceleration of price discovery. However, new challenges also emerge related to market stability, access fairness, and the need for adequate regulatory frameworks.
8.2 Theoretical Implications
From a theoretical perspective, CFM’s research and HFT empirical evidence challenge traditional market efficiency models. The impact function and co-impact phenomenon suggest that market microstructure is more complex than predicted by classical theories, requiring new analytical frameworks.
The square root law of impact represents one of the most significant contributions of applied academic research, providing a solid mathematical foundation for execution strategy optimization.
8.3 Practical Implications for the Industry
For the financial industry, the results suggest that future competitive advantage will increasingly depend on the ability to integrate high-level scientific research with advanced technological innovation. Companies that succeed in combining world-class research teams with cutting-edge technological infrastructures have higher probabilities of success.
Speculative execution and FPGA technologies represent the current frontier, but AI integration and evolution toward hybrid computational architectures will define the next phase of development.
8.4 Recommendations for Regulators
Regulators must develop frameworks that balance technological innovation with market stability. Transparency in access and behavior rules, as implemented by Eurex with “discard IP,” represents a promising approach that allows innovation while maintaining market integrity.
It’s crucial to avoid extreme determinism that can create unintended effects on market structure, instead maintaining a balance between predictability and randomness that preserves competitive fairness.
8.5 Directions for Future Research
Priority areas for future research include:
Development of more sophisticated theoretical models for co-impact and interaction between multiple algorithmic strategies. Empirical analysis of AI integration effects on market dynamics and price discovery. Study of quantum technology impacts on trading competitive structure. Research on optimal market structure that balances efficiency, stability and fairness.
8.6 Final Reflections
The “race to zero time” is not simply a technological matter, but represents a fundamental transformation in the nature of financial trading. As Akkermans observes: “It’s crazy how much brainpower is dedicated to shaving nanoseconds – showing that in a competitive market, even the smallest advantage is pursued relentlessly.”
However, true value doesn’t reside only in speed, but in the ability to combine speed with scientific intelligence. Companies like CFM demonstrate that rigorous theoretical research, when applied systematically through advanced technologies, can create sustainable long-term value.
The future of algorithmic trading will be defined not by who is simply faster, but by who succeeds in most effectively integrating scientific research, technological innovation and deep understanding of market dynamics. In this emerging paradigm, speed becomes an enabler rather than an end in itself, serving more sophisticated strategies based on solid scientific foundations.
As Tyc from McKay Brothers concludes: “It’s not a religious debate for us, it’s a matter of fit-for-purpose design.” This pragmatic philosophy, which balances technological innovation with practical application, probably represents the key to successfully navigating the continuous evolution of modern financial markets.
This study represents an analysis of current trends in financial markets based on empirical data and academic research. The authors acknowledge that the technological and regulatory landscape continues to evolve rapidly, requiring continuous updates of the analysis and theoretical frameworks presented.
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