Navigating Mexico’s equity market, where liquidity is sparse and intra-day volatility high in small and mid-cap names, can be challenging for emerging market traders.

Within the MSCI Emerging Markets (EM) Index, Latam accounts for just over 7% of the index while Mexico contributes 2%. As the second-largest market in the region, it is of particular interest for any trader with Latam exposure. However, like most emerging markets, the Mexican equity landscape is fraught with complexities that demand both strategic insight and a careful approach to execution.

The significance of high volatility and illiquidity

Emerging markets are inherently volatile and Mexico is no exception. For traders, this means rapid price swings that can either create opportunities or impose significant risks. The high touch nature of the market can exacerbate these issues, making execution more difficult and exposing participants to latency costs and potential information leakage.

Unlike major developed markets, Mexico has a relatively small universe of publicly-listed stocks. The majority are small- and mid-cap equities, limiting liquidity for institutional traders. Furthermore, listed companies can be majority-controlled by family ownership, restricting the available free float and making sizeable trades more challenging.

As a result, traders must navigate varying liquidity conditions and execution speeds, requiring a tailored approach to order management.

Sourcing block liquidity

Given the challenges presented by Mexico’s market structure, algorithmic trading has become a viable tool for traders seeking more efficient execution.

Of course, by automating order handling, algo strategies streamline execution, reducing the reliance on high touch trading that can be slow and costly. Additionally, algos dynamically respond to market conditions, ensuring that orders are executed at the most favourable bid-ask spread when liquidity arises.

Innovations in execution algorithms now incorporate real-time analytics, helping traders adjust their strategies based on liquidity shifts. Machine learning-enhanced algorithms refine execution paths, continuously optimising performance as market conditions evolve.

However, while algo trading is important, not all algorithms are created equal and access to liquidity remains a critical component of successful execution in the Mexican equity market.

This is where Liquidnet offers a distinct advantage. With an established global presence in 57 markets, Liquidnet has developed a deep focus on emerging economies, having been at the forefront of trading technology innovation for over 20 years and continuously refining execution strategies. That continues with providing block liquidity in Latin America, specifically Brazil and Mexico.

Additionally, its significant buy-side network, arguably one of the largest with over 1,000 firms, provides exclusive access to liquidity opportunities in Brazilian and Mexican equities that are not available through conventional channels. For example, in March, Alsea de CV Ordinary Shares traded on Liquidnet at 233.6% of their 10-day average daily volume (ADV), underscoring the platform’s unique liquidity advantage. On average, Liquidnet Members enjoy sourcing block liquidity in these markets well above 10% ADV on a given print.

By integrating cutting-edge algos with access to unique dark liquidity pools, Liquidnet enables traders to execute large trades efficiently while mitigating the challenges inherent in Mexico’s market structure. This combination ensures that institutional traders can operate with confidence, securing optimal execution while minimising market impact.

The future of trading in Mexico and Latam

As Latam continues to evolve within the global financial landscape, Mexico remains a pivotal market for emerging market traders. While the challenges are significant – ranging from volatility and illiquidity to transparency concerns – technological advancements in algorithmic trading and access to deeper liquidity pools provide viable solutions.

For traders looking to navigate these complexities, a combination of sophisticated algos and strategic liquidity access, such as that offered by Liquidnet, is crucial. As the market landscape shifts, leveraging these tools will be essential to maintaining a competitive edge and achieving execution efficiency in one of the world’s most dynamic emerging markets.

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For years, futures trading remained on the periphery of electronic execution innovation. Before the 2008 financial crisis, algorithmic development primarily focused on equities, with futures offerings often being a mere adaptation of existing equity algos. Following this, in the high-volatility post-crisis landscape, banks approached futures akin to OTC products, viewing them as an opportunity to capture bid-offer spreads through block pricing rather than as a market segment deserving dedicated execution research.

This lack of investment in futures execution tools left buy-side traders navigating an environment where traditional transaction cost analysis (TCA) models fell short.

The post-crisis shift

The period following the financial crisis saw the emergence of new players dedicated to refining execution in the futures space. Firms like Quantitative Brokers (QB) and BestEx Research disrupted the landscape by offering algorithms designed explicitly for the nuances of futures trading. Meanwhile, non-bank market makers gained a stronger foothold, contributing to improved liquidity and tighter spreads across many contracts.

Advances in futures margining efficiency, alongside persistently high OTC financing costs, further fuelled the buy side’s growing interest in listed futures. These structural changes created the ideal conditions for specialised execution tools to thrive.

Where legacy futures algos largely replicated equity-based logics, modern entrants built execution frameworks that factored in futures-specific liquidity dynamics. By integrating sophisticated order placement strategies, predictive analytics, and adaptive execution models, they significantly enhanced execution outcomes for institutional traders.

But are algos alone enough?

Despite these advancements, algo execution is only one part of the equation. The future of best execution in futures trading requires a seamless, intelligent transition between execution modes. Traders need access to automated execution processes that can shift dynamically between algos, request-for-quote (RFQ) and direct order book interactions.

A next-generation futures algo must do more than execute static strategies—it must replicate the decision-making process of a human trader. This means evaluating liquidity in

real time relative to historical data, understanding market microstructure shifts due to economic events, and leveraging relative value dynamics between correlated contracts.

Subsequently, a change in the current understanding of algos will occur, wherein they become more akin to agents— a single order may be filled across multiple modes, where a traditional algo is only one component as the agent responds to changing market conditions in real time.

At Liquidnet, the approach to execution extends beyond standalone algos to a holistic framework that integrates multiple essential components. The firm leverages BestEx Research to implement best-in-class futures algos, leveraging optimised execution logic to ensure efficiency. In addition, Liquidnet has developed pre-trade analytics in-house, allowing traders to assess real-time market conditions before execution. Experienced traders remain integral to the process, providing oversight and intervention when necessary to ensure optimal execution outcomes.

Beyond these elements, Liquidnet has built, across multiple asset classes, a robust liquidity network to offer deep and diverse market access. Equally, the firm plans to bring to bear its already establishedmachine and reinforcement learning capability to continuously refine execution logic, using historical performance data and real-time market feedback to improve decision-making. By combining cutting-edge technology with human expertise, Liquidnet aims to bridge the gap between automation and intelligent execution.

Moving towards intelligent automation

The futures market has long been underserved by traditional execution technology. While post-crisis innovation has improved algo performance, the key to the future lies in intelligent automation that enhances human decision-making rather than replacing it.

Liquidnet is committed to evolving futures execution beyond static algos, integrating real-time analytics, market-adaptive decision-making, and seamless transition capabilities to ensure traders achieve optimal execution outcomes. By shifting focus from isolated order book events within an algo execution to a comprehensive execution process, Liquidnet sees significant implications for futures TCA. They envisage audit trails close to natural language that will provide event by event commentary on an ongoing execution – the question ‘Why did you do what did at the time that you did it?’ will be answered alongside a TCA ‘tape’ that incorporates non-order book data, such as RFQ responses or implied price calculations.

In an era where liquidity is constantly shifting, execution decisions must be both data-driven and flexible. The next generation of futures execution technology must not only react to market conditions but anticipate them, facilitating the transition between automated efficiency and human expertise.

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AI is everywhere. From voice assistants in the kitchen, to our social media feeds and predictive healthcare, it’s reshaping how we live and how we work. Trading desks are no exception. The integration of AI and GenAI into trading workflows has revolutionised incumbent processes, transforming the way we handle and process data in particular. But as machines take on more and more routine tasks, we have to ask ourselves the question: where does the agency broker fit in this evolving system?

The answer lies in partnership, not replacement.

AI’s strength lies in its ability to process vast amounts of data in the blink of an eye and with astonishing precision. Modern trading generates mountains and mountains of information – price movements, global news, spreads, etc. – and AI can synthesise these inputs instantly. This enables algorithms to identify patterns and predict market shifts in moments.

The proliferation of tools powered by AI showcases just how versatile it is and gives the industry a glimpse of the technology’s potential. Generative AI tools that automate complex workflows and analyse unstructured data can and will enhance trading efficiency. On the risk side, we’re starting to see AI further integrated to offer real-time insights to help managers mitigate potential losses. 

But while AI handles the heavy lifting in terms of data and information processing, it does not operate in a vacuum. Trading decisions require context, strategy, and an understanding of nuanced client expectations – areas where machines still fall short.

The broker perspective

Humans and machines are not running the same race, nor should they. The unique strengths of each must be acknowledged and leveraged to create a truly effective trading ecosystem. AI excels in data processing, pattern recognition, and operational efficiency. However, it lacks the nuanced understanding, emotional intelligence, and strategic insight that human brokers bring to the table.

In the world of trading, client relationships remain a cornerstone of success. These relationships are built on trust, communication, and a deep understanding of client needs—factors that no algorithm can replicate. Clients often rely on brokers not just to execute trades. Whether it’s an illiquid market, a high-stakes block trade, or a sudden market event, the broker’s ability to assess the situation, provide insight, and make decisions under pressure is irreplaceable.

In this symbiotic relationship, AI empowers brokers to devote more time to what they do best: providing thoughtful, bespoke solutions. As trading grows more complex, the broker’s role is evolving into one that blends the precision of data-driven insights with the art of human judgment.

The partnership

AI should not be seen as a competitor but as a collaborator. The relationship between humans and AI is not one of replacement but of symbiosis, where the strengths of both are combined to achieve greater results than either could alone.

Think about a trading desk looking to identify patterns in market data. AI might flag an opportunity but it’s the broker who evaluates the context, applies a layer of human intuition, and determines the best course of action.

A successful partnership with AI requires more than passive acceptance. This symbiotic relationship, if embraced, will redefine efficiency and effectiveness on the trading floor.  Brokers and traders must actively engage with this technology, learning how to interpret its outputs, identify its limitations, and optimise its integration into their workflows. Upskilling to work alongside AI is not a choice but a necessity for the next generation of trading professionals.

Ultimately, the future of trading will be defined by this interdependence. Those who thrive will be the brokers and institutions that adapt to this new dynamic, leveraging AI’s strengths while honing their own unique skills. AI may be the engine driving the trading desk forward, but it’s the human brokers in the driver’s seat, steering toward opportunities and navigating challenges with a balance of data-driven precision and strategic intuition.

This evolution is not a challenge – it’s an opportunity. It’s a paradigm shift in how we think about trading.

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