As we all probably know from our own experience, there are many human behaviour patterns that can strongly influence our investment decisions, but of which we are often not even aware. These behavioural patterns lead us to evaluate investments much less rationally than they should be or than the theory of efficient markets assumes. Our emotions, inclinations and also external influences play a major role here. The fairly new scientific field of behavioural finance deals precisely with these behavioural patterns.
In the following article, I would like to explain the essentials of our inclinations, their causes and the influences on our investment behaviour.
What you will learn in this article
- What the market efficiency hypothesis is and why markets are often inefficient
- How behavioural finance tries to better explain these inefficiencies
- What key behavioural patterns influence your investment decisions
- What impact these behavioural patterns have on your decisions
- How you can learn to recognise the behavioural patterns and take them into account when investing
Efficient markets theory versus behavioural finance
Market efficiency hypothesis
The market efficiency hypothesis first assumes that all market participants, i.e. sellers and buyers, act completely rationally. Moreover, they all have access to the same market data, company information, economic data, etc. at the same time.
Since all buyers and sellers act completely rationally, they all draw the same conclusions from the available information, make the same predictions and act accordingly by buying or selling shares or other securities. This then leads to the fact that all available information is already reflected in the share prices at all times. The market is therefore "efficient".
It is therefore not possible for individual market participants to achieve better returns than the overall market on a sustained basis ("the market cannot be beaten in the long term").
Eugene Fama, who also received the Nobel Prize in Economics for it in 2013, developed the model of the market efficiency hypothesis in 1970.
Since criticism of the validity of the market efficiency hypothesis arose at that time, it was subdivided into three levels:
- Weak efficiency
- Medium efficiency
- Strong efficiency
Weak efficiency: Information from the past is already priced into prices. We cannot draw conclusions about the future from past price movements. Prices move randomly.
Medium efficiency: All market-relevant publicly accessible information (not insider information) is already included in the price in addition to past price trends. Fundamental analysis therefore no longer makes sense, but trading on the basis of insider information does.
Strong efficiency: The market efficiency hypothesis applies in full. All market-relevant information (including insider information) is already reflected in current share prices. It is neither worthwhile for us as investors to conduct a detailed fundamental analysis nor to trade on the basis of insider information. The best option in this case would be a passive index fund that completely tracks the index (because there is no way for us to do better).
Developments, especially in the field of behavioural finance, indicate that the market efficiency hypothesis is valid at best in its weak form (empirical studies, among others by Robert J. Shiller, well before the research in the field of behavioural finance, have already shown this, however).
Of course, as investors we act rationally in very few cases. Our environment and our emotions have a very strong influence on our decisions. The field of behavioural finance deals with the influences of psychology on the (investment) behaviour of investors and the resulting influence on the markets. In this way, behavioural finance helps to explain the inefficiencies in the financial markets.
Behavioural patterns resulting from our learning processes (heuristics)
We humans learn best by simply doing things. This has been described in many books and I can only confirm it from my own experience with my real estate investments. We can acquire a lot of knowledge by reading, but it is in the implementation or the application and activation of our knowledge that we really learn.
In terms of investing in shares or other traded securities, however, learning through trial and error or experimentation and through personal experience comes with a few disadvantages. For example, instead of looking at a company's financial statements, business model and quality of management, we use familiar investment rules. For example, we only buy shares on the basis of a very low price/book ratio because we have already heard or read about it. If we have had a bad experience with this, we quickly adjust our strategy, perhaps on the basis of an analysis we have read in SeekingAlpha that promises above-average returns.
Anyway, we have a tendency to be guided by the news and stories that are either most easily available or most prominently placed. For example, when Wall St Journal writes about tech companies as the coming up-and-comers in the stock market sky, we find it hard to ignore this information, even though it may be wrong (which should be the case quite often with popular stock market and business publications).
Hersh Shefrin, university professor, author and one of the pioneers in the field of behavioural finance identifies 4 steps on how we develop such an inclination via our learning process:
- We first develop general principles when we figure things out for ourselves
- We then develop rules of thumb (heuristics) based on this to draw conclusions from the information that is easily available to us
- We are therefore prone to make certain errors, simply because our heuristics are also error-prone
- We end up actually making mistakes in certain situations because we rely on our flawed rules of thumb
Use of stereotypes and if-then relationships
We tend to use stereotypes to classify companies as good companies and good investments at the same time. Often we use experience or information that we believe to be correct. This essentially means that we judge individual characteristics of companies as good and therefore believe that they are generally good companies.
For example, it could be that we feel India will be the next China and therefore the economy there will grow strongly for the near future. We now have a tendency to take a blanket view that every Indian company is a good investment. On the other hand, we have a blanket view that any company with a focus on sustainability and environmental issues is a good company, simply because we feel that sustainability will be one of the important issues of the future.
However, the use of if-then relationships can also take completely different forms. Even if we conclude on the basis of good quarterly figures that future results will be correspondingly good, we fall into this trap.
By the way, a similar behavioural pattern is the so-called halo effect. The halo effect describes a cognitive distortion in which we infer the unknown characteristics of a person from the known characteristics. A typical example is the voice of a person on the telephone. Even if we have never seen the person, we have an exact idea of what the person must look like. In reality, of course, the person often looks quite different.
Overconfidence means that we tend to have too much confidence in our own abilities. Among other things, this also refers to our ability to predict things correctly. Overconfidence is often expressed in the fact that we systematically underestimate the risk of our investments. In reality, this can lead to surprises because we systematically underestimate the range of possible scenarios. The real development of a share price will therefore often lie outside what we expect.
Conservatism (Anchoring and Adjustment)
From the perspective of behavioural finance, the term conservatism refers to our inability to correctly or sufficiently take into account the influence of new information on our estimates. For example, we may have just completed our valuation of a company when new information (e.g. in the form of the quarterly report) is published. Even though this information could have a significant impact on our valuation, we have a tendency to rather stick to our original estimate or assumptions (conservative). That is, we are psychologically anchored to our old estimate.
For example, if the information in the quarterly report, when viewed independently, would indicate a 30% decline in the share price, then we have a tendency to assume a smaller decline, e.g. only 15%, based on our original analysis. In many cases, this leads to negative surprises when the share price actually reflects the new information.
Another example is, for example, forecasts of economic development: Depending on the current economic environment, we will tend to be too optimistic or too pessimistic, namely depending on whether we are currently experiencing or have experienced an economic boom or crisis.
Fear of the Unknown (Aversion to Ambiguity)
The concept of fear of the unknown can be well explained in terms of probabilities. For example, if someone tosses a coin, many of us will bet on one side despite the low 50% chance of winning. If, however, the chances of winning are unknown, we tend to hold back.
Closely related to this is the so-called gambler's fallacy. If, for example, we have already tossed a coin 10 times and all 10 times the coin has shown "heads" (and not "tails"), then we have the feeling that the probability of a "tails" following the next toss is extremely high. However, as we know, the probability is still 50%, because it does not change, regardless of what the previous tosses have shown.
So what does this concept mean for investing or from a behavioural finance perspective? It could explain, for example, why we tend to invest in markets that have a clear direction (mostly upwards). If share prices are generally going up and the mood on the stock markets is good, we estimate the probability of further growth to be higher and also dare to bet on it by jumping on the bandwagon as well. In a market that does not show a clear direction, however, we tend to hold back because we cannot assign probabilities.
Dependency on framework conditions
Framework dependency implies the assumption that our decisions and our actions are dependent on the framework in which we receive information (e.g. via the media) or the circumstances in which we personally find ourselves (e.g. emotional). If we were to make decisions independently of our framework, then these would be solely of an economic nature and our state of mind etc. would have no influence on them.
Here are the 4 essential behavioural patterns that can be attributed to our dependence on the framework conditions.
Loss aversion is one of the key principles of behavioural finance. Loss aversion refers to our reluctance to accept a loss. A share may be trading significantly below our purchase price, we will tend to hold on to it anyway in the hope that the price will soon go up again and we can then still get out at plus minus zero.
This behaviour also has to do with changes in the status quo. We are reluctant to change things because it means we have an opinion and make a decision based on it. And making a decision involves a very real chance that the decision will be wrong. Doing nothing, on the contrary, feels much better to us, even if we know it is wrong.
Fear of loss can also lead us to take higher than average risks in the hope of still breaking even.
Interestingly, there is a big difference between the feeling of winning and the feeling of losing. We hate losing much more than we enjoy winning. Many studies have shown this. For example, an interesting experiment was published in Science in which 2 groups of subjects were each handed USD 50 and then independently offered 2 options:
- Group 1 could either keep USD 30 directly or engage in a game in which there was a 50% probability of either winning or losing the entire USD 50
- Group 2 could either lose USD 20 outright or enter into a game in which there was a 50% chance of either winning or losing the entire USD 50.
Both groups were therefore presented with de facto the same option. However, only 43% of the first group chose to gamble, while 61% of the second group chose to gamble.
Nobel Prize winner Daniel Kahneman (who has also been working on behavioural finance since the 1970s) also tells of a question he regularly asks his students:
In my classes, I say: 'I'm going to toss a coin, and if it's tails, you lose USD 10. How much would you have to gain on winning in order for this gamble to be acceptable to you?
People want more than USD 20 before it is acceptable. And now I have been doing the same thing with executives or very rich people, asking about tossing a coin and losing USD 10,000 if it's tails. And they want USD 20,000 before they'll take the gamble.
This means that our chance of winning must be more than 2 times as high as our maximum possible loss. Otherwise, we tend not to get involved in the coin toss game.
This means, firstly, that our decisions depend very much on how the options are presented to us, secondly, that a loss is much more difficult for us to accept emotionally than a win, and thirdly, that we would take unreasonably high risks to make up for losses.
Minimisation of Regrets
In the context of investing, regret refers to the feeling of having made a bad decision. "If only I had..." typically runs through our minds. For example, we might sell a well-performing stock too early and then watch it go up another 100%. "I wish I hadn't sold that stock" we then say to ourselves. We have a tendency to avoid this feeling of regret or to exclude it as far as possible.