Mind over model

Checking the weather? Guess what – you’re using a model. While models can help us gain insights that we can use to make good decisions, they are inherently incomplete simplifications of reality.

That’s worth remembering because in the investment world, factor models have been a frequent topic of discussion. Often marketed as smart beta strategies, these products are based on underlying models with limitations that many investors may not be aware of.

Before focusing on the use of models in investment, consider again the everyday example of a model that we identified above - 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 your house in the morning or whether you need to set up some emergency sprinklers before you go on holiday next week. However, as anyone who has been caught without an umbrella in an unexpected rain shower knows, reality often behaves very differently from how 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 incite debates about who has the superior 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. Hence, investors should be wary of any approach that requires a high degree of trust in a model alone.

Remember the garbage factor

Just like with weather forecasts, investment models rely on different inputs. Instead of things like barometric pressure or wind conditions, investment models may look at variables such as 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.

However, users should be cautious. Remember the saying, ‘garbage in, garbage out’? A model’s output can only be as good as its inputs. Poor assumptions can lead to poor recommendations. However, even with sound underlying assumptions, a user who places too much faith in inherently imprecise inputs can still be exposed to extreme outcomes.

Given these constraints, we believe bringing financial research to life requires users to apply common sense and remain aware of the limitations involved in order to identify when and how it is appropriate to apply a model. No model is a perfect representation of reality. So 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?’

Mind the judgment gap

As an investor, when evaluating different investment approaches, it’s important to assess a manager’s ability to test theories and implement ideas garnered from models into real-world applications. This requires judgment on behalf of the manager, and an investor who hires a manager to take decisions on their behalf is placing a great deal of trust in that manager.

That said, different management approaches can be associated with differing levels of trust. For example, traditional index funds often use quite a simple model and offer relatively high transparency: it’s easy to evaluate whether a fund has matched the return of an index. The trade-off with this level of mechanical transparency is that it may sacrifice the potential for higher returns, as it prioritises matching the index over anything else. In contrast, with more opaque and complex approaches, like many active or complex quantitative strategies, the requisite level of trust needed is much higher. Investors should look to understand how these managers use models, and also question how to evaluate the effectiveness of their implementation.

Rigorous attention must be paid to how any such strategy is implemented. To quote Nobel laureate Robert Merton, successful use of a model is “10% inspiration and 90% perspiration”. In other words, having a good idea is just the beginning. Most of the effort required to make an idea successful is in effectively implementing that idea and making it work.

In the end, there is a difference between blindly following a model and using it judiciously 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.

At Moore Stephens, as part of our Intelligent Investment strategy, we use a structured, long term and low cost approach to investment advice, which makes use of only well-established and proven models backed up by Nobel prize winning academic studies and over 50 years of academic research.

For more information, please contact us.

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