I am an Equity Research Assistant Vice President at Barclays Capital, covering LatAm and Agribusiness. My work is centered on fundamental research, industry dynamics, and understanding how businesses evolve within broader economic and competitive systems.
What has kept my coverage especially interesting is the complexity of the underlying markets. In agriculture, even seemingly external variables such as energy prices or weather patterns can reshape cost curves, margins, trade flows, and sentiment very quickly. That constant interaction between macro conditions and company fundamentals is a large part of what makes the space so intellectually engaging. While my coverage spans both Agribusiness and LatAm, I have increasingly gravitated toward LatAm, which is now my primary focus.
It is also one of the most demanding landscapes to navigate on behalf of clients. The opportunity is substantial: under-followed companies, structural growth, and dislocations that simply do not exist in more efficient markets. But capturing that opportunity requires accounting for far more variables than conventional U.S. coverage. A single thesis can span multiple countries, each with its own political cycle, regulatory regime, and policy risk – and shifting governments can reshape the outlook overnight. Currencies move independently and can swamp an otherwise sound operating story, while sovereign credit conditions, inflation, capital controls, and local funding markets all feed back into how companies are valued and financed. Clean comparables are often scarce, forcing valuation work to lean on judgment as much as precedent, and the analytical challenge is compounded by thinner public disclosures and lower market depth. Low trading liquidity, in turn, means that even a well-reasoned view has to be weighed against how, and whether, it can actually be expressed in the market. Guiding clients through that environment requires framing the full set of risks and rewards clearly, which is what I find most satisfying about the region. The work demands more than identifying upside; it means understanding whether a thesis can withstand the broader risks of emerging-market investing. That perspective was reinforced on a trip to Mexico City this past March, where meetings with corporates, clients, and local Barclays traders deepened my grasp of the market backdrop and helped further ground the theses I develop across my coverage.
Over nearly three years at Barclays, I have had the opportunity to write a number of thematic and flagship pieces across my coverage. I wrote on Argentina and the catalysts that could position the country to become a more important agricultural and biofuels producer, incorporating variables such as the ENSO cycle, which at the time was driving extreme flooding followed by drought. I also wrote a longer piece on the Mexican presidential election and how policy and macro outcomes could flow through to earnings, followed by work on U.S. elections and the implications of U.S. policy for Mexico's economic backdrop. One of my favorite pieces was our 2026 Mexico outlook, which examined a far more challenging setup after two years of broad-based strength in the country. Most recently, I published a broader piece on U.S. biofuel policy following the EPA's Set 2 rule.
Outside of those larger thematic pieces, my day-to-day responsibilities look much like those of any sell-side analyst: meeting with companies, speaking with clients, maintaining models, and refining views as new information comes in. What has always drawn me toward investing more directly is the opportunity to pair research with action. I like having a view, pressure-testing it, and then being accountable to the outcome. That combination of analysis and judgment is a large part of what I find most compelling about markets.
I developed an interest in financial markets at an early age, influenced by my father's work as an equity trader. From the outset, I was drawn to markets through a research-oriented lens. I was always more interested in understanding the business, industry structure, strategy, and economics behind a stock than in following price movement on its own.
That foundation still shapes how I invest today. My process is grounded in fundamental analysis but informed by a broader macro perspective. I actively manage personal capital using the same framework, with a strong emphasis on disciplined positioning, risk management, and identifying asymmetric opportunities.
My personal trading has also become an important extension of how I think about markets. I spend most of my time in areas where the opportunity set is defined by rapid change, technical complexity, and significant dispersion–semiconductors, quantum computing, indices, and the broader biotech/pharmaceutical ecosystem, particularly where drug discovery and clinical progress can meaningfully alter company trajectories. Most of my exposure is expressed through levered products, both long and inverse, which gives me a very different vantage point on market structure, volatility, positioning, liquidity, and risk management. While the instruments are different, I find myself drawn to this risk profile for many of the same reasons I am drawn to emerging markets: the return potential can be substantial, but outcomes depend on navigating multiple layers of uncertainty at once. Timing, sentiment, factor exposure, drawdown control, and the path of the trade matter as much as the underlying thesis. I have found meaningful success trading this way, but more importantly, it is something I am genuinely passionate about and find deeply rewarding. I enjoy the discipline required to form a view, size it appropriately, manage risk in real time, and remain accountable to the outcome.
I am also building an AI-supported investment system designed to strengthen how I research markets, evaluate opportunities, and learn from prior decisions. The system is intended to function as a disciplined portfolio decision-support layer: it ingests market, sector, company, and macro information; evaluates ETF, levered ETF, index, hedge, and cash exposures; and helps translate research into structured risk/reward scenarios. Over time, I am developing a more advanced research lab around it, including neural-network-based tools designed to test scenarios, study historical analogues, evaluate changing market regimes, and improve how the system weighs probability, timing, and risk across different setups. A central part of the project is that it can analyze my own trades, decisions, and thought process, helping me identify patterns in how I form views, where I am strongest, where I need to improve, and how I can become a better analyst and trader over time. The goal is not to automate judgment away, but to make the investment process more rigorous by forcing every idea through a consistent framework–what changed, why it matters, how it affects earnings or multiples, where consensus may be wrong, what the upside and downside look like, how the position should be sized, what would invalidate the thesis, and what can be learned afterward. It rests on the belief that strong market judgment compounds when research, trading history, risk management, and post-outcome review are captured systematically.
The project sits at the intersection of investing, software, AI, and portfolio construction, and reflects how I increasingly think about markets as an adaptive system where fundamentals, liquidity, positioning, policy, psychology, and probability have to be evaluated together.
At the same time, I pay close attention to trading behavior–how positioning, sentiment, and market conditions influence price action across both institutional flows and retail participation. I have found those dynamics to be meaningful catalysts and, in many cases, important contributors to returns.
The most compelling opportunities, in my experience, tend to emerge when strong underlying fundamentals align with favorable trading conditions–when the analysis is right, the setup is there, and both are pointing in the same direction.