Happy 2021. Or as I call it, Year of The Bifocals. Traumatic as that is, it still beats Year of The Bat. This year, I’ll try to broaden my writing unit from standalone essays to themes. I hope to cover 4-5 themes over the year, with multiple essays on each theme. My first theme is Long-termism and here’s my first essay on this theme.

Some time back, a well-regarded analyst published a rant on how Asian Paints’ quarterly performance was impossible to predict. Everything’s all over the place: volume, value, mix, margins, input prices. There’s no method or consistency. With gaps between estimates and actuals wider than that between Prithvi Shaw’s bat and pad, he said “I give up” and implied “How dare they”.

It struck a dissonant tone, as paints industry is as predictable as it gets. An acyclical, lucrative, stable oligopoly with no sudden moves or surprises, where Asian’s dominance has been brutally steady. Over decades, every well-run paints company has grown at 2.5x GDP or something like that. So, why was this smart young man so peeved? Because

Long-term ≠ ∑ Short-terms

A fancy way is describe our messy world is: complex adaptive systems are not reductionist. Unlike with turbines and engines, businesses and economies cannot be broken down into neat constituent parts and causal relationships between parts. We can understand messy world as a whole, without being able to fully understand inner workings or explain every little facet. One can make sense of it only at a higher level of abstraction.

Non-reductionism extends to time dimension as well. Attempting to break down a decade into years or a year into quarters is not merely tricky but counter-productive. Oddly, far future can be more predictable than near future. Surest way to get long-term wrong is by analysing it as a series of short-terms. Hence, inequality sign in my equation. We’re better served by working with a fuzzy sense of long-term prospects, without cascading the same into a series of explicit, precise short-term forecasts. Let me explain using the past.

For over a decade, my consideration set has been better companies in decent, slow-changing industries. Imagine we travelled back to the beginning and you forced me to forecast growth by threatening me with dire consequences (“no nendrankai chips ever”). For simplicity, let’s stick to non-cyclical businesses. My best attempt might have been: system has grown for decades at low double-digits in nominal terms; better companies are unlikely to do worse; steady industries won’t let them do much better either; so, organic business growth in same ballpark.

How does my hypothetical growth prediction fare, across industries. Consumer staples? Low double-digit. Consumer durables? Ditto. Building materials? Ditto. Auto? Ditto. Pharma? Ditto. Chemicals? Ditto. Traditional media? Shade lower. For-profit internet? Shade higher. IT? Low double-digit. BPO? Ditto. Last two aren’t even tied to same system. Across my consideration set, the story is eerily similar. While there are outlier companies or companies having outlier years, range of long-run outcomes is remarkably narrow.

Noticing this historical invariability was the trigger for this essay, because that’s not how I remember my lived experience. For all my philosophizing about long-termism, I am a slave to a quarterly information cycle. I was exasperated at wobbliness of quarterly progress. De-stocking. Re-stocking. Internal screw-ups. External shocks. Cost spikes. Windfall gains. Leads and lags in passing on cost increases. Market share gains and losses. High and low growth phases. Volatile margins. Infuriating distractions. Inexplicable divergence vs peers. Often, I had existential doubts about my investment thesis for a business that turned out boringly fine. As it happened, it was a roller coaster for businesses and emotions. Looking back, I wonder what all the fuss was about.

Some of this is explained by noise that averages out over time, but that’s not all. Signal itself is lumpy. Most aspects mentioned in previous para are features, not bugs. It’s the natural order of things. If you look at Sachin’s test career one series at a time, how frequently did he average sub-40? 40% of the time. Lesser mortals have way more volatile track records. That’s why Ganguly was a great captain. He gave good players time, not tension.

What’s the downside of looking at life as a series of rolling short-terms? Philosophically, we overweight recent trends over timeless truths. Appreciating the latter (this too shall pass; regression to mean; all business is cyclical; people don’t change; markets overreact at both ends) requires a fuzzy notion of time. They’re far from apparent on any graph whose x-axis has a quarterly scale. Practically, we get too hung up on precise path over general direction. This is more apparent at extreme crisis times. A philosophical view suggested that a few quarters plus or minus in returning to normalcy hardly impacted intrinsic business value. In contrast, modelling a precise recovery, with an attendant obsession over path and timeline, has an exaggerated effect on forward EPS and specious valuation metrics. Worse, if we click-and-drag from Armageddon, any path to normalcy has an implausible zero-to-infinity feel to it, until after it happens. We’re overwhelmed by questions of how, by when, which parts, in what sequence, to what extent, why. Even outside of crises, localized pessimism around a particular industry has the same effect. Steady industries such as IT and Pharma repeatedly lurched between doom and boom in market perception, as their prospects were viewed through a rolling short-term lens. I can picture seasoned management telling analysts and investors “please switch to decaf”.

Many don’t have the option to ignore short-term, given institutional constraints. However, it’s not a binary choice between long-term and short term. Merely keeping this inequality at the back of one’s mind is helpful, especially in moments when short-term outlook is extra murky. It’s a good counterweight to our natural and institutional tendency to overweight recency and herd into extreme fear or greed. It also counters another problem: in a future-discounting market where everyone forecasts, how to stay ahead of the curve. Early conviction is based on fuzzy, philosophical comfort rather than on defensible, detailed analysis.

Thriving in messy world requires a comfort with fuzziness, as it doesn’t allow precision, causality or reductionism. I’d extend this fuzziness to the time dimension as well. In theory, long-term being a summation of short-terms is a tautology. In practice, it’s a trap. It gets in the way of being roughly right and acting decisively. We’re better off with a ≠ sign in the equation.

PS. I deliberately used growth as metric of interest in this essay, as that’s the most popular candidate for forecasts. I don’t explicitly forecast growth over any time horizon, although I do check for disruptive headwinds. My (implicit) forecasts mostly pertain to sustenance of business quality. I also deliberately didn’t define long-term, to stay consistent with my fuzziness spiel.