How I Research: Macro to Micro

Most equity analysts start with the company. They open the 10-K, build the three-statement model, calculate DCF, compare multiples, and write the note. The macro section is a paragraph at the top of the report that says something about GDP growth and interest rates. It rarely changes anything about the conclusion.

I work the other way. I start with the macro signal and work down to the security. Not because I think macro research is inherently superior to bottom-up analysis, but because I think the signals that matter most are the ones that arrive first at the physical layer of the economy and last at the financial layer. By the time an earnings miss confirms what shipping rates were saying six months ago, the trade is over.

This page explains the framework I use across everything on this site. The live signal dashboard, the Horizon 2040 research, the maritime and chokepoint analysis, the macro geopolitics work, and the blog articles all follow the same logic. Here is what it looks like.

Layer 1: The physical economy

The foundation is physical signals. These are the data points generated by the movement of actual goods through the world economy: shipping rates, commodity prices, energy flows, trade volumes. The distinguishing feature of physical signals is that they cannot be managed. The Baltic Dry Index does not have a CFO. Copper does not issue forward guidance. LNG spot prices are not revised three months later by a statistics office.

Every week, I check a core set of physical indicators. The BDI tells me whether global industrial demand is expanding or contracting. The copper-gold ratio tells me whether markets are pricing growth or safety. LNG spot and forward curves tell me whether energy supply is tightening before equity analysts update their models. The VIX tells me whether options markets are pricing complacency or fear. The yield curve tells me what the bond market thinks about the economy's trajectory.

I track 21 of these indicators on the Signals dashboard, pulling live data from FRED. The point is not to predict where any single indicator goes next. The point is to read the aggregate state of the physical economy in real time, before the financial economy acknowledges it.

Layer 2: The geopolitical overlay

Physical signals tell you what the economy is doing. They do not tell you why, or what might change. That context comes from the geopolitical layer: understanding which supply chains are fragile, which trade routes are contested, and which structural shifts are underway that have not yet been priced by financial markets.

This is where the chokepoint analysis and the macro geopolitics research fit in. When I see LNG prices tightening, I want to know whether it is cyclical demand or a structural change in European energy sourcing. When copper rallies, I want to know whether Chinese fixed asset investment is driving it or whether it is an electrification demand story with a longer tail. When shipping rates spike, I want to know whether it is Red Sea rerouting absorbing capacity or genuine industrial expansion.

The geopolitical layer is where most financial analysts stop reading. They see "geopolitics" and file it under "unpredictable, therefore ignore." But the geopolitical forces driving commodity supply, Chinese rare earth processing monopoly, Hormuz transit risk, Russian energy weaponisation, food system fragility, are not unpredictable. They are structural. They are observable. And they create pricing dislocations precisely because most models treat them as noise.

Layer 3: Sector implications

Once the macro signal and the geopolitical context are clear, the question becomes: who is exposed? Which sectors face margin compression from this energy cost increase? Which industries benefit from this supply chain rerouting? Which equity markets are pricing in assumptions that the physical economy contradicts?

This is where the research becomes directly actionable. A few examples of how the layers connect:

Red Sea rerouting since late 2023 has increased Europe-Asia shipping distances by 30-40%, absorbing vessel capacity and raising container costs. That is Layer 1. The geopolitical context (Layer 2) is that Houthi attacks are unlikely to stop and the rerouting is effectively permanent for planning purposes. The sector implication (Layer 3) is that European manufacturers with Asia-dependent supply chains face structurally higher logistics costs that compress margins, and the companies most exposed are the ones whose equity analysts still model pre-2023 shipping cost assumptions.

Another: the BDI declined from 3,400 to 1,600 between October 2025 and January 2026. That physical signal (Layer 1) suggested industrial demand was weakening. Chinese PMI data confirmed it (Layer 2). The sector implication (Layer 3): materials and industrials equities priced for continued expansion were vulnerable. The signal was visible two months before the sell-side downgraded its earnings estimates for the sector.

The pattern is always the same: the physical signal arrives first, the geopolitical context explains it, and the sector implication tells you where the mispricing is.

Layer 4: Individual securities

Only after the first three layers narrow the funnel do I look at individual companies or funds. By this point, I know which sectors are exposed, which direction the risk runs, and what the physical economy is signalling about forward demand. The security-level work then becomes much more targeted.

At Omega, this is how I approach equity mutual fund research. Before evaluating a fund's holdings, I check what macro regime we are in. Is credit tightening? Are commodity costs rising? Is the INR under pressure from crude imports? A flexi-cap fund with 40% allocation to materials stocks performs very differently depending on whether the BDI is at 3,000 or 1,600. The fund manager's stock selection matters, but the macro regime they are operating in matters more for forward returns.

The same logic applies to the Angel One valuation model I am building. Angel One's revenue is structurally tied to Indian retail trading volumes. Those volumes are a function of demat account growth, SEBI regulatory regime, and market sentiment, all of which are observable through the signals I already track. The DCF model matters. The assumptions driving that DCF matter more. And those assumptions come from Layers 1 through 3.

Why inversion matters

The standard equity research process works bottom-up: start at the company, check the sector, mention the macro. My process inverts that: start at the macro signal, check the geopolitical context, identify the sector exposure, then look at the security.

The inversion catches dislocations that bottom-up analysts structurally miss. A bottom-up analyst covering European industrials may not track the BDI or LNG forward curves. They do not need to, their coverage universe does not require it. But when those signals turn, the earnings estimates they built without that input become wrong, and they do not know it until the quarter reports. By then, the signal has been visible for months to anyone watching the physical economy.

That is the edge I am building toward. Not better financial models, the modelling is table stakes. Better inputs. Earlier inputs. Inputs from the layer of the economy that cannot be managed, forward-guided, or revised after the fact.

What this looks like in practice

Every Friday, I spend about 90 minutes running through the signal set. I check the 21 indicators on the dashboard, flag any that have moved meaningfully, and update a running log of what the aggregate state is telling me. If something diverges, equities rising while credit spreads widen, or copper falling while industrial PMIs are stable, I note the divergence and track it.

That weekly discipline produces two things. First, an ongoing, real-time read on the physical economy that makes any bottom-up analysis I do better-informed. Second, a log of divergences that historically tend to resolve in the direction of the physical signal. Those divergences are where the most interesting research questions come from, and they are the starting point for most of what I publish on this site.

The ships do not care what the consensus says. That is why I check them first.