In 1931, Alfred Korzybski, an engineer, philosopher, and mathematician, presented a paper to the American Association for the Advancement of Science. In the paper, he introduced the idea that the map is not the territory. In other words, a model of a thing is not the thing itself. While models can be useful for gaining insights that help us make good decisions, they are inherently incomplete simplifications of reality.
Factors are common characteristics of stocks that help to explain differences in returns. The first documented factor was the market, as introduced by the Capital Asset Pricing Model (CAPM). Through the CAPM lens, portfolio returns were explained by beta, or exposure to the market factor, and alpha, or manager skill. If, for a given level of market risk, a portfolio had higher than expected returns, the excess was attributed to skilled active management.
The CAPM was eventually shown to be flawed – it could only explain about two-thirds of the return differences in diversified portfolios, and there seemed to be some anomalies. These anomalies resulted in the discovery of more factors. The Fama/French Three-Factor Model introduced company size and relative price as additional factors to increase the explanatory power of the model.
The Three-Factor model explains about 90% of the return difference between diversified portfolios. More recently, the expected profitability factor was introduced, further increasing the explanatory power of the model. Many other factors have been introduced in both the academic and practitioner literature.
As the evidence on factor investing has grown, many financial companies have started offering factor-based strategies, often marketed as smart beta strategies. These strategies may not be based on robust research, and the implementation may lead to outsized risks.
To help shed light on this concept, let’s start by examining an everyday example of a model: a weather forecast. Using data on current and past weather conditions, a meteorologist makes a number of assumptions and attempts to approximate what the weather will be in the future. This model may help you decide if you should bring an umbrella when you leave the house in the morning. However, as anyone who has been caught without an umbrella in an unexpected rain shower knows, reality often behaves differently than a model predicts it will.
In investment management, models are used to gain insights that can help inform investment decisions. Financial researchers frequently look for new models to help answer questions like, “What drives returns?” These models are often touted as being complex and sophisticated, and they incite debates about who has a better model. Investors who are evaluating investment strategies can benefit from understanding that the reality of markets, just like the weather, cannot be fully explained by any model. Investors should be wary of any approach that requires a high degree of trust in a model alone.
Just like with the weather forecasts, investment models rely on different inputs. Instead of things like barometric pressure or wind conditions, investment models may look at variables like the expected return or volatility of different securities. For example, using these sorts of inputs, one type of investment model may recommend an “optimal” mix of securities based on how these characteristics are expected to interact with one another over time. Users should be cautious though. A model’s output can only be as good as its input. Poor assumptions can lead to poor recommendations. In financial markets, no amount of due diligence can result in certainty; a user who places too much faith in inherently imprecise inputs can be exposed to extreme outcomes.
Given these constraints, it is important to maintain presence of mind and an acute awareness of the limitations involved with models in order to identify when and how it is appropriate to apply them. No model is a perfect representation of reality. Instead of asking, “Is this model true or false?” (to which the answer is always false), it is better to ask, “How does this model help me better understand the world?” and, “In what ways can the model be wrong?”
What is an investor to do with this knowledge? When evaluating different investment approaches, understanding a manager’s ability to effectively test and implement ideas garnered from models into real-world applications is an important first step. This step requires judgment on behalf of the manager. An investor who hires a manager to bridge this judgment gap is placing a great deal of trust in that manager. The transparency offered by some approaches, such as traditional market cap weighted index funds, requires a low level of trust on behalf of the investor; The model is simple and easy to evaluate by comparing portfolio performance to that of the index.
The trade-off with this level of mechanical transparency is that it may sacrifice the potential for higher returns, as it prioritizes matching the index over anything else. For more opaque and complex approaches, like many active or complex quantitative strategies, the requisite level of trust needed is much higher. In these cases, the manager is making substantial deviations from the market in an attempt to produce market-beating performance. The evidence in support of these strategies is very poor, making it challenging to trust the models being used.
There is a sensible middle ground where models may be used judiciously. Following the evidence on what drives returns without deviating too much from the market allows for evidence-based higher expected returns without the risk of missing out on what the market has to offer. Practically, this means slightly increasing the weights in portfolios of small cap, value, and highly profitable stocks relative to their weights in the market.
In the end, there is a difference between blindly following a model and using it to guide your decisions. As investors, cutting through the noise around the “latest and greatest” investment products and identifying an approach that employs sound judgment and thoughtful implementation may increase the probability of having a positive investment experience.
