Consider two examples
The immune system consists of millions of cells, each performing its own set of functions ranging from communicating the entry of pathogens inside the body to triggering other cells responsible for killing them. While each cell is simple and has its own functionality, the interaction between these cells leads to an intricate immune system that differs from the properties of its individual cells. More importantly, the same system adapts every time it is faced with a novel pathogen to produce killer cells to gobble its unknown enemy.[1]
Ant colonies comprise marching ants, each minimally intelligent and nearly blind. If we put ten ants on a flat surface, they will walk around in circles until they die of exhaustion. Yet put millions of them together, the group collectively forms a systematic organization that builds shelters and bridges and collects food. “The group as a whole becomes a superorganism with collective intelligence”.
How is it that simple entities (cells and ants) self-organize themselves through interactions into a collective whole that is vastly different from its individual constituents? We cannot fully explain the reasons for this emergent behavior, but such complex adaptive systems are more common than we think. In a world that loves quantification and hates the unknown, acknowledging the presence of Complex Adaptive Systems (“CAS”) will profoundly impact how we think and operate.
So, what is a complex adaptive system?
A complex adaptive system comprises the following characteristics:
There are entities within a system that make their own decisions or perform their own functions.
These entities interact with each other.
When entities interact, emergent behavior occurs, in which the system’s behavior differs from that of its constituent components (whole is different from the sum of its parts).
The system evolves and adapts over time to new information.
Deducing this, it is not difficult to conclude why the stock market and the economy are complex adaptive systems.
Each entity in the stock market, including promoters, institutions, individual investors, brokers, and market makers, acts in its own best interests (return expectations, time horizon, dopamine release, etc.). They interact with each other in the market to buy and sell securities based on those interests and objectives. This leads to the emergence of the stock market as a whole, different from the properties of its individual participants who trade on it. With every byte of information, different participants pull in different directions. Market adapts.
Humans are the fundamental entities of markets. Their nature makes it even harder to understand how the stock market works. Humans have “feelings,” unlike cells, which in most cases are programmed to act a certain way every time they encounter a pathogen. Context, circumstances, and state of mind affect how humans make decisions. Shai Danziger’s research found that judges’ leniency varied depending on the time of day, with offenders slated for decision shortly after breakfast having a 65% chance of gaining parole. These possibilities dwindled as the day wore on, with the harshest assessments occurring right before lunch. Following lunch, the leniency would resume.
Despite the evident quirks in human behavior, many traditional economic and financial theories assume that humans are rational. These theories have tried to model human behavior as laws in their quest for certainty without acknowledging the impact of emotions and biases in decision-making. Though inaccurate, mathematical models based on such an assumption lend a false sense of comfort to their practitioners.
That capital markets is a CAS is a meaningful progression from the current model of rational actors making rational decisions. Knowing that not everyone is acting rationally, that everyone’s actions are impacting the system in rather inexplicable ways, and that the process is leading to an unforeseen emergent behavior, has important implications. It also implies that we must alter the lenses through which we view the markets and investing.
Implications for investors
1. Complex adaptive systems cannot be solved or merely explained by linear reductionism.
Reductionism states that systems can be understood by fully understanding their diverse sub-systems or constituents. This approach has worked well in engineering and developing new technologies. For e.g. The engine of a car constitutes of valves, pistons, and cylinders. These constituents create a deterministic and predictable output and hence reductionist logic can be used to improve the efficiency of the engine. However, reductionism does not consider the network of interactions in a complex system where each constituent makes local decisions and adapts to the environment.
The "Smash Sparrow" campaign is a fascinating example of how using reductionist logic in complex systems can lead to poor outcomes. Mao Zedong, a former ruler of China, ordered the killing of all sparrows in 1958 because they adversely impacted farming by consuming a lot of grains and fruits. Simple reductionist logic. Citizens resorted to banging pans or beating drums to scare away the birds from landing, eventually dropping dead from the skies from exhaustion. Mao failed to consider that while sparrows ate grain seed, they also consumed many harmful insects and bugs. These bugs thrived as birds almost disappeared across the nation. Mao’s action threw the ecosystem out of balance, and the locusts took over, destroying the rice crop and causing a mass famine.
It is in human nature to resort to reductionist logic to explain CAS. We like precise answers. This is clear in the headlines for the daily market close when journalists list specific causes for market movement on that day, such as currency, the US market, China, FM, and so on, when the real explanation is usually that the movement is caused by several factors, none of which are known with certainty.
Similar to this, there is no shortage of experts attempting to forecast the direction of the market for the upcoming year using factors such as interest rates, rainfall, and planetary positions. Research says that on average, the success rate of predictions made by these “experts” is roughly equal to that of a dart-throwing chimpanzee[2]. Despite this, the general public pays attention to these experts because of the erroneous certainty and confidence with which they provide their predictions.
2. Uncertainty is constant, what changes is complacency.
“In this world, nothing can be said to be certain, except death and taxes.” – Ben Franklin.
Perceived stability breeds complacency while uncertainty is constant. Negative surprises reset the former. Was uncertainty lower a day before demonetization or in December 2019 before COVID hit the world?
The economy is not short on drama, from a lack of lab monkeys affecting research operation of corporations’[3] to wars and political agendas affecting global supply chains. We tend to doubt certainty only when confronted with unpleasant surprises, not realizing that such occurrences are more frequent than we believe.
The illusion of security that comes with a "stable” environment when everything goes as planned leads to complacency (think bull markets). High levels of confidence encourage risk-seeking behaviour with little regard for potential negative outcomes. One unexpected external shock is enough to snap investors out of their state of overconfidence. This is also the period when investors are at their vulnerable most. Fear and anxiety take over leading to sub-optimal decisions like selling off prematurely and exiting the market. Such circumstances breed an environment of high-risk aversion and “flight to safety”. A typical case of buy high and sell low.
3. Forecasting is hard, doing it without biases is even harder. “It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.” - Daniel Kahneman.
Every investment decision requires us to form a view of the future. While it is theoretically correct that the value of a business is equal to the present value of discounted future cash flows, its practical execution is challenging, let alone the actualization of every analyst’s holy grail—terminal value. As investors, not only do we have to make deliberate forecasts in a highly dynamic and uncertain world, but we also must do so with the least amount of prejudice. Easier said than done.
Projections are often deemed to fit narratives in our heads, which are driven by incentives, biases, and past experiences. We look for information that supports our beliefs and ignore that which does not (confirmation bias). We overvalue the stocks we own while undervaluing the ones we do not (endowment bias). We extrapolate recent performance without considering past performance over a long period (recency bias). This list goes on.
The apparent precision of such an exercise on an excel sheet gives certainty-seeking agents (i.e., investors) an illusion of control. Just for context, Nifty 50 analyst estimates have been revised downward every year since 2013-2021; downgrades were much larger in cyclicals over the same period.
Investing is not Physics, in which laws can be quantified to the smallest fraction; it is a soft science. Overemphasis on biasedly quantified outcomes can lead to flawed decisions in a complex world.
4. There will always be regrets.
In the complex system of stock markets, things are the most obvious in hindsight and the least when one is experiencing them live. There is no “no brainer”. This is equivalent to a detective summarizing a case after it has been solved. The investigation is confusing and convoluted in real-time, despite the narration’s methodical and logical structure. Everyone is a genius in hindsight.
As a result, there will be regrets. It’s the nature of the game. Investors can’t escape this reality despite making futile attempts to time the market. This is a sobering thought, as humans have a propensity for regret/loss aversion which leaves us handicapped in cases where we should act but don’t, and vice versa. For instance, investors often continue to hold or not reduce exposure to a high-flying stock with a strong narrative but absurd valuation when the proper process dictates selling or moderating exposure to such a position, only to avoid regret. Nobody wants to see the stock increase in value after they sell it.
Changing the lenses through which we view the markets allows us to identify potential pitfalls. It becomes clear that the capital markets puzzle encompasses both behavior and logic, among other things. Redefining this puzzle makes it obvious that the tools we’re accustomed to using – i.e., mathematical models and heuristics, are crutches that won’t take us too far.
We believe that we have sharper tools. More importantly, the ones that work better for us. These tools don’t rely on reductionism. Instead, they rely on understanding the wiring of our brains and the biases that lead to suboptimal decisions.
How do we deal with these implications?
“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.” – Darwin
In Africa's Gorongosa National Park, poachers hunted elephants throughout a 15-year civil war that started in 1977. They raised money for weapons by selling ivory tusks, which caused a 90% fall in the population of elephants during that time. Faced with adversity, elephants in the park developed the ability to function without tusks, and this ability was handed down to their progeny. Three times as many elephants were born without tusks, and occasionally male elephants were born with considerably reduced tusks to deter poachers.
Being adaptable is essential to evolution - it differentiates, selects, and amplifies.
Markets, like nature, are Darwinian. Investors need to be adaptable to survive and thrive over extended periods. We often lack adaptability when our biases unconsciously cloud our judgment, and therefore, we are our own worst enemies. Adaptability in the context of investors is a state of mind; it requires an open mind towards ideas, data, and opinions; curiosity to learn more across various fields; honesty to accept things that one doesn’t know; and humility to learn from our mistakes. In no way are we advocating changing time-tested principles of long-term value creation; we are merely advocating a mindset to execute them better.
At Xponent Tribe, we embrace uncertainty. Below is how we have adapted to deal with it.
When formulating an opinion about the future, we concentrate on sectors and businesses where we can make broad predictions rather than precise or low probability narrow predictions. For instance, two-wheeler EVs are currently at a very nascent stage of industry evolution, with multiple players in the running to grab a pie of the market. The space is extremely competitive due to low entry barriers. Having studied the evolution of industries from nascency[SA7] to maturity, it is tough to pick the winner in this race, at least currently. Therefore, Electric vehicles will gain share from ICE players over the next decade is a high probability broad prediction, while player X will emerge as the winner in the EV space is a narrow prediction. It’s easier to figure out the losers vs. winners in this situation.[4] Knowing what to avoid is equally if not more important than knowing what to own.
We have high respect for base rates and do not let market narratives based on recency bias disregard them. We do not get carried away by the recent performance of an otherwise average industry or business that has not created long-term wealth historically. We spend time trying to understand if there is a structural change that requires us to update our views. As John Templeton said, “this time it’s different” are the four most dangerous words in investing.
We do not make futile attempts to time the market in our pursuit of regret aversion. We understand that we cannot extract maximum returns from a stock by accurately catching the bottom and selling it at its peak. Our buying and selling decisions are solely based on fundamentals and our perceived margin of safety at the time of decision-making.
While we do not completely undermine the use of spreadsheets to make projections, we are aware of its shortcomings. We do not seek definite answers from it and it’s not our gospel truth. For us, a historical track record of execution (reflected both in numbers and management intent) is more important and therefore holds more value in our decision-making. It acts as a compass
We form a balanced view, knowing that the opposite of a good idea can also be a good idea. For example, we can make a case for or against concentration vs. diversification, low P/E vs. high P/E, trading vs. investing, bottom-up vs top-down, etc. The results depend on the individuals executing the strategy. We know what works for us and stick to it.
We believe that it’s important to have strong opinions that are loosely held. While having strong opinions based on independent research helps us be decisive when facing uncertainty, holding them loosely keeps us flexible in updating those decisions or opinions in light of new information. We also maintain a culture of zero complacencies to ensure that we question and monitor our decisions periodically instead of just sitting on them.
We understand the sociological aspect of investing and do not encourage seeking social proof on investment discussions within the team. We maintain a culture of open communication. Opposing views, if well thought through, are encouraged and appreciated. Aggregating views from multiple perspectives enables us to make more informed decisions.
We are not ashamed of admitting that there are things we do not know. We do not know when or why the market would peak or trough, how it would react based on what the Fed or RBI would do, or whether our beloved FIIs look at us favorably on that day. We focus more on what we can know, like a company's competitive positioning, , management integrity, key drivers of longevity, industry structure and its implication on profit pools etc.
We will not be a part of every game in town. We will not have a view on all companies gaining attention due to new 52-week high. Despite our best efforts, there will be companies we miss out on. They may be outside our circle of competence, or we may miss them entirely.
In a world where something is always happening, doing nothing is also something. Our FOMO level when it comes to rumors and speculations is low. We try to be objective about when to act and do not have the urge to do something often, especially not on monthly export data or retail volumes. Based on our investment philosophy, we are more comfortable taking a long-term view.
We understand that complex systems create patterns such as nonlinearity, power law, increasing returns to scale among many[5]. These patterns function as guiding frameworks for our decision-making.
The human brain is wired to take energy-efficient shortcuts by creating heuristics that work against good decision-making. Incorporating these tools in our investment process and using them in a disciplined manner inoculates us from mistakes we could make due to our common cognitive biases and makes us adaptable. It lets us focus on things that matter over the long-term.
Xponent Tribe’s endeavor is to make better long-term decisions for all our stakeholders, and we strongly believe that our approach will enable us to achieve that objective.
[1] Memory cells (T cells) are created every time a body faces a new pathogen. This is like a record of pathogens in the body which triggers an immune response from the same cells whenever the body faces the pathogen again.
[2] Super forecasting by Philip Tetlock
[3] https://www.bloomberg.com/news/articles/2022-09-28/research-monkey-shortage-boosts-china-s-vaccine-development
[4]Such predictions won’t be narrow for every company in other industries. Context matters – in this case its industry evolution.
[5] We will talk about these patterns in our future posts.