How to be More Productive

How To Be More Productive (Investor Edition)

Systems, frameworks, and best practices for full-time investors

There is so much to read these days.

Investing can feel like trying to drink from a firehose.

In this environment, “being productive” is not about doing more things. It is about consistently spending your limited time and energy on the work that actually improves decision quality.

This article is a practical toolbox of systems, frameworks, and best practices that helped me do exactly that. Some of the ideas are about protecting focused time, others are about running a cleaner research process, capturing what you learn so it compounds, and turning analysis into good decisions.

Think of the pages ahead like a menu.

Take the tools that fit your personality, your workflow, and your constraints, then ignore the rest. If one technique helps you improve a decision, avoid a mistake, or increase your conviction even slightly, that benefit can compound over years into better investments.

Most importantly, this is not a claim that there is one “right” way to work. These are simply some of the approaches that have worked for me, and I wanted to share them in case they help you too.

[Printable PDF available at the end.]

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FOCUS AND TIME ALLOCATION

Deep Work blocks (maker schedule¹) – This means setting aside a few big chunks of time where you do only one demanding thing, with no interruptions. Think of it like closing your office door to write an important paper: phone on silent, email closed, and no hopping between tabs “just for a second.” For an equity analyst or full-time investor, these blocks are where the real value gets created: building or fixing a model, writing a clear thesis, or figuring out what you believe that the market does not (your “variant view,” your view that differs from consensus). Treat these blocks like a meeting you cannot skip², because if you do not protect them, they get eaten by calls, news, and random requests that feel urgent but do not move your analysis forward.

Time boxing + “definition of done” – Time boxing means you give a task a fixed amount of time, then you stop. It is the opposite of letting a task expand forever because you keep polishing it. A “definition of done” is simply deciding in advance what “good enough” looks like so you do not keep looping. In investing work, this helps because there is always more you could research: another chart, another scenario, another article. If you say “30 minutes to build a first-pass KPI table” and “done means I have the last 8 quarters, growth rates, and a notes column,” you will actually finish and move on to the next step. You can always come back later for a second pass, but the first pass gets you traction fast.

Weekly review (GTD-lite³**) –** A weekly review is a regular reset where you look over everything you are tracking and decide what matters next. It is like cleaning your desk once a week so you stop losing important papers under piles of clutter. For analysts and investors, the “clutter” is open loops: upcoming earnings, filings, conferences, guidance windows, management calls, model updates, and old ideas you have not revisited.

Stop-doing list – A stop-doing list is a running list of activities that feel productive but do not pay off, so you intentionally cut them. Think of it like a diet for your calendar: you remove the bad calories. For investing, common low-return habits include reading every single news item, endlessly formatting slides, rebuilding templates from scratch, or tracking too many names in too much detail. Writing these down helps because you stop arguing with yourself in the moment. When you feel you need to do the low-value thing, you can point to the list and redirect that time into research that actually changes your decision quality.

Impact-weighted scheduling – This is a fancy way of saying: spend your best thinking time on the work that can change your returns the most. In a similar vein, I like to say: maximize your returns on attention. Instead of letting your day get driven by whatever feels urgent, you allocate time based on what could move a real decision. For a full-time investor, “impact” can mean expected dollars. For an analyst supporting a portfolio, it can mean expected basis points, where 1 basis point is 0.01% of performance (if you can really measure it). In practice, you reserve your sharpest hours for the few names, assumptions, and open questions that could actually change position size, conviction, or timing. Things like email, formatting, and routine monitoring get pushed to lower-energy hours. The result is that your calendar starts to reflect what matters.

Figure 1: The Eisenhower Matrix time allocation. After the Do First tasks, block the important not-urgent work first, because that quadrant is where returns and decision quality compound, and it is the first thing to get eaten by “urgent” activity.

Theme days and batching – Batching means grouping similar tasks together so your brain does not have to keep switching gears. Context switching is expensive because every time you jump from “earnings updates” to “deep dive reading” to “calls” you burn minutes reloading the mental model of what you were using. A theme-day approach might look like: one day mostly for earnings and model updates, one day for deep research and writing, one day for maintenance and monitoring. You still stay flexible for surprises, but you try to avoid doing five different categories of work in the same hour. Over time, this reduces fatigue and increases throughput without adding hours.

Constraint-based calendar – This approach starts by setting a few hard rules that protect your capacity, then you schedule everything else around them. It is like budgeting money: you decide the fixed costs first, then decide what you can afford after. Examples of constraints: no calls before a certain time, at least two deep work blocks per week, only one new name per week, or no more than X meetings per day. These rules prevent the common investing trap of over-committing and ending up with lots of half-finished research and shallow conviction across too many names.

Meeting hygiene for analysts – Meeting hygiene means being strict about what meetings are for and what they produce. A helpful way to do this is to classify meetings as either “inputs-only” or “decisions-only.” Inputs-only meetings exist to gather information you cannot get elsewhere. Decisions-only meetings exist to choose a direction, approve a thesis, or agree on next actions. If a meeting does not produce a decision, a written artifact you will reuse, or access to a unique source, it becomes optional. This is not anti-collaboration. It is pro-focus. Analysts and investors often lose their best hours to meetings that feel productive but do not change any decision.

Energy management protocols – Energy management protocols are small routines that reduce ramp-up time when you start a research block and reduce friction when you stop. The idea is that your brain works faster when the starting conditions are consistent, like an athlete who uses the same warm-up before a game. A start ritual might be: open the same tabs, pull up the same dashboard, start the same checklist, and begin with the same “what am I trying to answer today” prompt. An end ritual might be: write a short recap, update the next action, save files with the date, and note what question you will tackle next. These routines sound simple, but they cut the wasted 10 to 20 minutes that often disappears at the beginning and end of every work session.

RESEARCH WORKFLOW FRAMEWORKS

Hypothesis-driven research – This means you start research with a few clear questions that can be proven wrong, instead of vaguely “learning about the company.” It is like walking into a store with a shopping list, rather than wandering the aisles hoping you will figure it out. In investing research terms, some examples would sound like: “Gross margin can expand 300 basis points because input costs are falling,” or “Unit growth will slow next year because the customer base is saturating.” Then every document you read has a job: confirm, disprove, or refine one of those statements. This approach saves time, and it also protects you from accidentally collecting only information that supports what you already want to believe.

Coverage Kanban – A Kanban is a simple board that shows where each item is in a pipeline. Imagine sticky notes moving across columns on a wall: not started, in progress, ready, and done. For coverage, it prevents the classic problem of “everything is kind of in progress.” If a stock is in Watchlist, you are not pretending you are actively researching it. If it is in Active Research, you are committing real time. If it is publish-ready (ready to pitch or decide), you know you have enough work to make a call. This clarity reduces mental load and makes it obvious when you are over-committed. Some investors I know use https://trello.com/for this.

Two-pass reading system – Two-pass reading means you read something twice, but with different goals each time. The first pass is a quick scan to understand what it is and whether it matters. The second pass is where you slow down and extract what you actually need. For an analyst, pass one might be skimming a 10-K to map the business segments, what drives revenue, and where the risks might be. Pass two is where you pull numbers, quotes that reveal incentives, details that change your model assumptions, and any contradictions. This method stops you from wasting energy doing “deep reading” on documents that are not actually important, while still capturing the valuable details when they are.

Checklist research (anti-forgetting) – A checklist is a repeatable set of items you review every time, so you do not miss the same things again and again. It is like the classic example of a pilot’s pre-flight check: even experts use it because memory is unreliable under time pressure. In equity research, checklists help with filings, earnings calls, proxies, and deals because the documents are long and your attention is limited. A checklist might remind you to check revenue recognition changes, segment restatements, stock-based compensation trends, covenant language, or purchase price allocation in M&A. This is a very well-known tool among investors, who typically develop their own checklists over years of experience.

Earnings and catalyst calendar – This is a forward-looking calendar of events that can move a stock, tied to the names you own or watch. Think of it as your “weather forecast” for volatility: you cannot control it, but you can prepare for it. For full-time investors, this includes earnings dates, likely guidance windows, investor days, lockup expirations, product launches, regulatory decisions, and major industry conferences. Having it in one place reduces surprises and helps you plan research around deadlines. It also helps you avoid making big changes right before an event without realizing it.

Primary-source priority ladder – This means you read the most reliable information first and the noisiest information last. Filings and transcripts are closer to the truth than commentary because they are direct records of what the company reported and said, with legal and reputational consequences. Commentary can be useful, but it is often mixed with opinions, narratives, and incentives. A practical “ladder” might be: filings, earnings transcripts, investor presentations, competitor filings, industry data, and only then commentary like blogs, social media, and hot takes. This improves signal-to-noise and reduces the chance you accidentally anchor on a story before you have the facts.

Mosaic triangulation – Investing is often “mosaic” work, meaning you build a picture from many small pieces rather than one perfect data point. Triangulation is the habit of checking important claims from at least two sources/angles so you do not get fooled by one biased source. The typical example, if management says demand is strong, you might look for confirmation in competitor commentary, channel checks, customer behavior, or third-party data. For a key KPI like retention or pricing, you want at least two independent lines of evidence. This reduces bad surprises and helps you gain conviction faster because your view is not built on a single fragile pillar.

Reference-class underwriting – This technique starts with the “base case” before you dive into what is special about the company. It is like evaluating a restaurant by first understanding the general economics of restaurants, then deciding whether this one is unusually good. For equity analysis, you write the base-rate story for that business model: typical margins, typical reasons companies fail, common competitive dynamics, reinvestment needs, and how cycles usually hit it (you can use LLMs for creating this base case picture). Then you ask, “What is truly different here, and is there evidence?” This prevents you from falling in love with a unique narrative when the business is actually likely to behave like its category.

Driver decomposition – Driver decomposition means translating any story into a small set of business levers that actually create value. Most company performance can be explained by a handful of drivers like price, volume, mix, retention, unit costs, working capital, and capital spending. If you cannot map a narrative to these drivers, it is often noise or a distraction. For example, “brand momentum” is vague, but it becomes useful when you translate it into pricing power, conversion rates, repeat purchase, and returns.

Second-derivative questions – Most people stop at first-level statements like “growth is slowing” or “margins are improving.” Second-derivative thinking asks about the change in the change. Is growth still slowing, or is the slowdown starting to stabilize? Are margins improving, but at a slowing pace? Markets often reprice when the direction of the trend changes, even if the absolute numbers are not great yet. If things are bad but getting less bad, that can matter more than “still bad.” Asking second-derivative questions helps you spot inflection points earlier and keeps you from reacting too late to turning trends.

Regime mapping – Regime mapping means identifying what kind of environment a company needs to perform well, and what happens outside that environment. Some businesses do best when interest rates are low, credit is easy, and customers spend freely. Others do well when commodity prices are favorable, or when ad budgets are expanding, or when housing is strong. You define the key regimes that matter, then you ask: what breaks if the regime changes? This improves productivity because it narrows your monitoring list. Instead of tracking everything macro, you track the few macro or industry variables that truly drive the company’s outcomes.