Robo-advising, why your brain can’t trust them, and why you can’t trust it

Most people think they are in control of their thoughts, I mean how can you let anyone else know what you’re thinking without telling them? This is perhaps one of the biggest lies advertisement companies would like you to think. In 2002 Daniel Kahneman became the first psychologist to have obtained the Nobel prize in economics for his ground laying work on behavioural economics. People seem to be unpredictable, yet we are all actors of our logic and emotions which form our choices. Truth is that the black box of our brain is always feeding from outside world. This feeding process results in a vulnerability that can be manipulated to make you jump to specific thoughts. The most trivial of example is the classic phrase “Do NOT think of a pink elephant”. Most people can’t help to unintentionally think of a pink elephant when asked this question.  Our minds have developed certain patterns of thought etched in our evolution.

Humans are satisfiers rather than maximisers. Generally speaking, we seek the best satisfactory solution rather the most optimal one. This creates less work for our brains making its thinking power more efficient. However, efficient brains don’t always make the best optimal logical decision. This play between critical and quick thinking is described in Daniel Kahneman’s book Thinking Fast and Slow. Professor Daniel Kahneman introduces two systems of thought; System 1 is the default cruise mode, making quick automatic decisions, and System 2 is the cognitive dense mode used to formulate more complex thoughts. Since System 2 requires more mental power so your brain constantly defaults to the more efficient thought System 1. When System 1 is in control, the brain is subject to falls into mental patterns known as heuristics. Heuristics is a mental shortcut that allows people to solve problems and make judgments quickly and efficiently.

If you were asked which city has a larger population Hamburg or Berlin? Most people without knowing the exact population of each city would guess Berlin as it is the capital of Germany. This is an efficient way to arrive to a correct answer, however the assumption that the capital is the most populous city is not always right. For example, if the question was switched to guessing which two cities (Zurich or Bern) is the capital of Switzerland, most people would reply Zurich. This is an example of how this quick thought patterns can benefit us but occasionally misguide us to false conclusions.

Furthermore, these heuristics are also present in the way we think about money. Among many the most common ones are prospect theory, and loss aversion. These mental shortcuts can trap us into making wrong assessment by relying on our quick judgement (System 1). By understanding them you can limit their effect on you and how they could impact your financial investments.

Prospect Theory

Prospect Theory was arguably what awarded Daniel Kahneman the Nobel prize in 2002.  This theory is a behavioural model that shows how people decide between alternatives that involve risk and uncertainty (e.g. % likelihood of gains or losses). It demonstrates that people think in terms of expected utility relative to a reference point (often your current wealth). This can be best exemplified through a value vs gain graph shown below.

The first observation we see it that our mind does not follow a linear curve. If we double our gains, we would expect to double our perceived value. However, prospect theory highlights how we don’t asses value linearly. Most people would score 100 million dollars in gains at a very high value. If this amount was then double to 200 million, we see that high value score is not double as it plateaus as the gain increase. This hints how human’s minds think in relatives rather than absolutes. The relative happiness difference from 1 million to 2 million is more logarithmic.

A second interesting observation is how the curve is not symmetrical between gains and losses. The slope is steeper in the loss section relative to the gain section. This dissymmetry points to our second heuristics known as loss aversion. 

Loss Aversion – Understanding fear

Loss Aversion refers to people’s tendency to prefer avoiding losses to acquiring equivalent gains: it is better to not lose $5 than to find $5. To better understand how loss aversion works take the example shown below. This example illustrates people reactions to risk varies under different frames.

Trading Psychology

These effects play a critical role in behavioural finance. A drop in the market could create different reactions depending on how the public perceives the drop. The media also plays a critical role in choosing which frame to contextualise this market shift. Saying the market lost 10% or saying the market is at a 10% discount generates different emotions and actions to how you perceive the value of a stock. In the end the valued is 10% less but the emotion people attach to that number can give different perceptions of its true value. Since humans are loss averse, we are also loss centered. You have probably heard stories before of a heavy market drop. Investors get loss averse (scared), and close their trading account effectively cashing in their loses. Then one or two years later the market bounces back up to equal or higher values. The wise investor would have seen the drop as an opportunity to buy. This is why setting a strategy plan and playing accordingly when you stand to worsen your lose your assets is extremely hard, or conversely win in the stock market and have greed keep you in until suddenly the price pops. This is why emotions and strategy do not mix well in valuing stocks. 

Setting up the rules 

Since humans do not instinctively separate their emotions and biases from their actions a solution could be to automate their actions through a computer

A robot-advisory can create the split between of human biases and cold thought strategy. The problem is that human minds already have biases against robot-advisory. The name in itself is in some ways deceiving. The name suggests an independent entity able to create decisions of its own. We already have trouble letting close relatives handle our finances or even our banks, how will anyone feel comfortable leaving it to a mysterious robot-advisory program. The answer lies that a robot is unbiased to emotions but can stick to a programmed strategy of risk. Many people see the robot as its own entity absent from responsibility since its actions can’t be relied to a person. This however is completely the wrong approach to look at robo-advising. A better analogy is the ability to put an airplane flight on autopilot. The user is still at the cockpit in control of the functions, but the autopilot computer on the plane constantly monitors the plane’s trajectory to the users set conditions. For the pilot that could be the cruising height and passage, but a robo-advisor that could be the level or risk the user is willing to stomach and the sectors he wants to invest. Robo-advisory should be seen as a tool and not a different entity in itself. 

Conclusion

Most people falsely think they are completely in control of their action, and fail to see the hidden cues that shape and influence our actions. The human brain process is split into two systems of thought, System 1 and System 2. One that is automatic and reactive thinking while the other is wilful and reflective thinking. The mind is an amazing computer, but we don’t hold the control panels to it. Directing our tasks over to a computer to which we do control is perhaps our best bet to make unbiased financial decisions.

References

https://www.investopedia.com/articles/trading/02/110502.asp

https://en.wikipedia.org/wiki/Prospect_theory#/media/File:Valuefun.jpg

https://en.wikipedia.org/wiki/Prospect_theory

https://www.verywellmind.com/what-is-a-heuristic-2795235

https://en.wikipedia.org/wiki/Expected_utility_hypothesis

https://www.thebalance.com/what-is-the-behavior-gap-2388311

https://www.thebalance.com/why-people-lose-money-in-the-market-4144737

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