I often get asked why markets behave the way they do. If I had an exact answer to this question, I’d be a billionaire. Hundreds of books are written on this topic, each with its own sound reasoning. It’s an industry that employs hundreds of thousands of smart analytical people, obsessed over every little detail there is to a company and its products. If there was a one answer, like a true economist would say – someone most likely would have found it by now. So here is my attempt at explaining why markets behave the way they do, my value proposition is that it’s intended for non-finance schooled people and it’s grounded in non-finance day-to-day examples. For this to be successful, I will use a lot of oversimplifications of real-life economic forces. My focus will be purely on the human factor, ignoring the financial and economic ones. People familiar with economics and finance, might be quick to point out the omissions, but this post is not intended for them. There is much better literature on the topic from more reputable sources for finance professionals. I hope this post enriches your view on the topic, even if you don’t agree with mine. The post is intended to be read top to bottom, for the argument to flow cohesively read it in its entirety.

It is all rooted in two words – context and expectations.

Context

Let’s begin with a short 2-minute video of the Nobel laureate Robert Shiller who is an authority on this matter.

This clip succinctly captures the main spirit driving the markets. Let me give you a simple example that will make things clearer. I work in the City of London, where a coffee from Starbucks costs roughly £3.60 nowadays (May 2019). The price isn’t much different in near-by coffee chains. This has anchored my perception of value of a cup of coffee to that price tag. Recently, I was in Switzerland, where I bought the same coffee and it costed me roughly £7 after converting from Swiss Francs to British Pounds. Needless to say, I was shocked by the price difference and found it very expensive. Now, imagine a Swiss person coming to London and buying the same coffee. They will find it cheap, because for them the £7 price is what they would consider normal. The product and the quality are the same. Allow me to introduce the first oversimplification – we are ignoring inflation, the concept of purchasing power parity, interest rate expectations, transportation costs, etc. Only later did I found an article by Voucherbox that the most expensive coffee cup globally was actually in Switzerland.

The prices are as of 3 years ago, but you get the idea.

Knowing this, how are the two people in this example to agree on what is the actual price of a cup of coffee. Now, include the entire world with their perception of value of coffee in their city. How are we all to agree on one fair price? Who will have the largest say in the decision? What would be our criteria? We can easily get all global prices and classify them based on the average price into cheap and expensive. However, none of us would end up with the actual fair price of the cup of coffee. Obviously, it’s more complicated than that. The price of the raw materials and labour costs will differ by country and so on. The pricing complexity compounds as we go deeper. This is where satisficing kicks in – you can’t spend your entire life deriving one true price for one thing, you get enough information that will allow you to get as close, but not to the exact true price and run with it. Mostly because you can never have perfect information, which means you have the complete information set. In real life it’s impossible, with a limited set of puzzle pieces you need to get an overall idea of how the image looks like. Secondly, information is not cheap, the moment you go beyond free World Bank or United Nations’ data, specialized data is very difficult to collect and expensive. Not everyone will have access to the same tools, either because of availability or price, so you have information asymmetry. Now imagine the entire world with their perception of value based on their environment and goals, having to agree on one share price for a company.

Each one will see a different value for a multitude of reasons. Let’s take a mergers & acquisitions example – A car manufacturer will see a different value in a tire maker, because of the vertical synergy and cost saving, compared to an ice cream manufacturer, just looking to diversify their holdings.

It took me years of studying and working in finance to understand that there is no one price at any one point. It’s always a range. To simplify, imagine the share price as just a weighted average of the value perception, where the parties with the largest investment have the largest weight. The perceived worth changes as well as the weights all the time. It’s the view of the one with the largest cash inflow that gains temporarily a higher weight to their view, until next one comes along. That’s why markets appear irrational. Also, don’t forget, based on the coffee example above, what looks expensive for someone, might not be the case for others. With the rise of algorithmic trading, the update frequency of both variables has increased dramatically. However, it’s humans that are still in charge of the decision making, the market price doesn’t reflect just the company fundamentals, but the human aspect as well.

This is where usually the debate between Technical and Fundamental Analysis comes into play and the discussion of the efficient markets. I’ve already written on this, you can read more on it in this post.

This is not to say, there isn’t any randomness at any one point. Sometimes, it’s very difficult to differentiate between irrationality and if there’s some economic meaning behind it. A recent example, was companies adding “Blockchain” to their name and their share price rising rapidly without any company focus or structural changes. Some think it was a clever marketing stint – sure, but their share price shouldn’t have risen at all, as no other changes had been announced at the time. Maybe some of these companies really did pivot to blockchain, but I doubt the majority of them did.

Biases everywhere

The amount of biases we are susceptible to is large. Here are a few, in no particular order:

Anchoring bias – as explained earlier with the coffee example. It’s the pegging the value of something to a number and always using it as a reference value, even if it’s no longer valid.
Confirmation bias – seeing only the things that fit our theory and ignore the contradictory evidence.
Availability bias – overestimating probabilities of an event, only because it has happened to us.
Overconfidence bias – if you get a few lucky guesses right, your confidence for the next guess increases, even without any new information available to justify the increase.
Loss aversion – people would hold a losing position for longer, to avoid admitting to themselves they’ve made a mistake, sometime justifying it by “it will turn around”.

The full list is quite extensive. If you go through it, you will definitely find a few that describe you. We are constantly plagued by a plethora of biases. It’s a fascinating topic dating back to classical philosophy, but I’m not going to go there now. The book I’ve discovered with the largest collection of day-to-day psychological biases is “The Art of Thinking Clearly” by Rolf Dobelli. The CFA Level III book on Behavioural Investing is also a good succinct read on the topic. A complimentary book, focusing more broadly on the topic of is “Predictably Irrational” by Dan Ariely.

Madness of the Crowds

Having the knowledge of biases at this point, you can imagine when a large group of people exhibiting the same bias how this would affect the price. Don’t forget it’s not permanent. I think the best quote that captures this is by Warren Buffet – “Be fearful when the rest are greedy and be greedy when the rest are fearful”.

Risk vs Uncertainty

This is my favourite debate. Interestingly enough, it’s often overlooked even by finance professionals. These two illustrate the border between calculated odds and pure gambling. So what are the two exactly? We can look at Ellsberg’s Paradox – in short, people would take on a bet, where they know the probability of winning, even if it’s extremely low compared to an unknown winning probability no matter how high it could be. When you are taking a risk, you can calculate the probability of something happening, with uncertainty you can’t predict the outcomes. That in a nutshell is risk vs uncertainty. Risk can be managed, uncertainty not. People often present uncertainty as risk with very low or even unknown odds and they look the same, but are not.

Expectations

In finance everything revolves around expectations. The reason is – everyone is focused on predicting the future, so today’s price already reflects the weighted expectations of future event occurrences. To get the price moved there needs to be a surprise announcement, that hasn’t been priced yet or its weight misestimated. You can read more on the different levels of market efficiency here. To get the direction right, you need to be aware of everyone else’s expectations and how they weighted their event outcomes. Announcing news, everyone else already expected shouldn’t move the price in any way.

I don’t know another industry that has a statement like “Earnings missed estimates”, meaning analysts’ expectations are not incorrect, it’s the companies that get it wrong. In a Motley Fool article, there’s a hilarious list of contradictive daily finance jargon. It’s a funny read!

Scale

Another factor is that humans are naturally terrible at perceiving scale, especially exponential changes. Our predecessors worked mostly in linear terms and rarely had to deal with exponential growth. Take this statement as an example – “1 million seconds is roughly 11 days and 14 hours, 1 billion seconds is 31.5 years”. The law of large numbers is unavoidable. People struggle, myself included, to perceive the scale of operations sometimes, we either undershoot or overshoot. When valuations are done, people approach them with their perception of scale and expected growth.

Is this it?

Not even the half of it. The real-world is much more complicated and interesting. I’ve just previewed only the human element, completely ignoring financial and economic factors such as dividend yield, time horizon and thousands of other details finance professionals would look into. If you’ve enjoyed the post. Share away!


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