Loss aversion is a psychological phenomenon that refers to the tendency of individuals to strongly prefer avoiding losses rather than acquiring gains of equal or even greater value. In other words, people tend to feel the pain of losses more intensely than the pleasure of equivalent gains. This bias can have a significant impact on decision-making, particularly in the fields of economics and finance.

Loss aversion was popularized by psychologists Daniel Kahneman and Amos Tversky as part of their Prospect Theory, which explains how people make decisions involving risk and uncertainty. The concept can be illustrated by the following example:

Imagine you have the choice between two scenarios:

• Scenario A: You receive $100.
• Scenario B: You have a 50% chance of receiving $200 and a 50% chance of receiving nothing.

From a rational perspective, both scenarios have an expected value of $100. However, due to loss aversion, many people tend to prefer Scenario A because they dislike the possibility of receiving nothing in Scenario B, even though the potential gain is higher.

How Can Loss Aversion Affect Our Financial Decisions?

This bias can lead to several behavioral tendencies and impact decision-making:

• Risk aversion: Loss aversion contributes to risk aversion, meaning that people are willing to accept lower potential gains to avoid losses. This can influence investment decisions, as individuals may choose safer, less profitable investments to avoid losing money.
• Sunk cost fallacy: Loss aversion can lead people to stick with decisions they have made. Even when these decisions are no longer rational, because they have already invested time, money, or effort. This is known as the sunk cost fallacy.
• Holding losing stocks: Investors may hold onto losing stocks longer than they should, hoping to avoid realizing a loss. This can cause them to miss opportunities to reinvest in more promising options.
• Negotiations: In negotiations, loss aversion can make individuals more reluctant to make concessions for fear of giving up something they already have.
• Consumer behavior: Loss aversion can affect purchasing decisions, as people may hesitate to buy if they perceive a risk of losing money on a product or service that doesn’t meet their expectations.

How to Limit the Impact of Loss Aversion on Financial Decisions?

To counter the effects of loss aversion, it is important to:

• Recognize the bias: Being aware of your tendency toward loss aversion can help you make more rational decisions by objectively considering potential gains and losses.
• Assess risks realistically: Evaluate potential risks and losses more objectively by taking into account both the potential outcomes and their probabilities.
• Diversify investments: Diversifying your investment portfolio can help mitigate the impact of potential losses from a single asset.
• Focus on long-term goals: Consider the long-term perspective and overall objectives when making decisions, rather than focusing solely on short-term gains or losses.

By understanding how loss aversion works and actively seeking to counter its effects, you can make more balanced and informed decisions in various aspects of your life, including financial choices.

L’article How Does Loss Aversion Affect Our Financial Decisions? est apparu en premier sur Neuroprofiler.

These questions have been asked in many economic experiments to MBA students and financial professionals in France and in the US.

Without surprises, most Americans prefer to bet on the Dow Jones and most French investors prefer to bet on the CAC40.

The issue is that if the question is now: “Do you prefer to bet on the increase of the Dow Jones or the CAC40 on the first of next September?

The answers of investor will be the same. Most Americans prefer to bet on the Dow Jones and most French investors prefer to bet on the CAC40.

Home bias is a cognitive bias that makes us prefer to invest in something we know, even if it is not the most optimal investment to make.

This bias has been particularly studied for its impact on financial markets and its mechanisms are still not very well understood.

It highly affects retail investors too, who prefer to invest in sectors they are comfortable with or easy to understand such as real estate or gold, even if these investments are not always optimal.

References

Prospect Theory, for risk and ambiguity, Wakker (2011)
Coeurdacier, N., & Gourinchas, P. O. (2011). When bonds matter: home bias in goods and assets (No. w17560). National Bureau of Economic Research.
Abdellaoui, M., Baillon, A., Placido, L., & Wakker, P. (2009). The rich domain of uncertainty. American Economic Review.Psychology of investing, John Nofsinger, 2004
Coval, J. D., & Moskowitz, T. J. (1999). Home bias at home: Local equity preference in domestic portfolios. The Journal of Finance, 54(6), 2045-2073.

L’article Cognitive Bias: Familiarity bias est apparu en premier sur Neuroprofiler.

Of course, all our attention is focused on the words and behaviors of your beloved one.

She or he is the only person who matters in this dining room. Yet, you suddenly hear the name of your firm from the conversation of the guy at the table behind you. This guy is not speaking louder than before, though.

After this disruption, you may be able to remember some of the details that this guy mentioned about your company while you were totally absorbed by your conversation with your partner.

How is this possible?

Our brain has two different systems for thinking and solving problems:

• System 2: conscious, deliberate and effortful. We use this system when solving analytically complex problems such as the multiplying 47*89. We are aware of the reasoning that we use to solve the problem and are thus able to explain it.
• System 1: unconscious, intuitive and effortless. We use this system when performing automatic tasks such as driving or solving 2+2. However, this does not mean that the tasks managed by our System 1 are necessarily simple.

When valuing the project of a start-up, the business angel will especially rely on his/her intuitive system 1 though the task can be extremely complex.

During the dinner, we unconsciously used our system 1 when memorizing the conversation of a person behind us. But as information was not deemed to be relevant, our conscious System 2 was not activated. Our brain is not immediately aware of this conversation. But if a very important piece of information appears (the name of your company), the System 2 will be alerted and start to analyze the situation. We become aware of this discussion behind us.

This distinction between System 1 and 2 has some neural correlates, though this is still a new and sometimes controversial field of research.

According to the global workspace theory, our neurons have three ways of sorting the tremendous quantity of perceived information in its environment:

1. Subliminal treatment: perceived information is not memorized
2. Supraliminal and unattended treatment: perceived information, like the conversation at the table behind us, is treated but we are not aware of this information. This treatment can more or less be linked to our System 1
3. Supraliminal and attended treatment: perceived information is treated and we become aware of this information. This treatment can more or less be linked to our System 2.

How does information jump from one level of consciousness to the other? It depends on how relevant our neurons judge the perceived information.

References

Baars, B. J. (2005). Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Progress in brain research, 150, 45-53.

Thinking fast and slow, D. Kahneman

Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition, 79(1), 1-37.

L’article Cognitive Bias: Thinking Fast and Slow … est apparu en premier sur Neuroprofiler.

A short introduction to this theory is given in this article.

Let’s imagine you can invest in a bet with a 50% chance of getting either $100 or $0, or get $50 directly. What do you prefer?

Based on traditional financial theories, a “rational” and risk-neutral investor should calculate the average/mean or expected value (EV) of the bet and invests only if the EV is greater than the price of the bet.

In our example, this investment strategy means that a rational investor will accept to play the gamble if the alternative is $50 or less ($100X50% + $0X50% = $50).

However, most investors are much more risk-averse than theoretical “rational” agents.

Sensitivity to gains

Based on economic experiments, a large majority of investors prefer to take the $50 rather than gambling. They prefer also to get $40, or even for some of them $30, rather than taking the risk of getting nothing.

Moreover, the higher the outcome, the less sensitive investors become to an increase in this outcome. We will appreciate to get a $10 free gift coupon rather than nothing. However, winning $11k at a casino instead of $10k does not make a real difference.

To account for this first psychological parameter (gain sensitivity), we can say that the utility we attribute to financial gains is often concave. We underestimate large gains and are much more sensitive to an increase in small gains than in large gains.

Loss aversion

Now, what if I asked you to choose between paying $50, and placing a bet where you have 50% chance of paying either $100 or nothing?

In this case, most of us prefer to gamble. There is some hope not to lose. We try our chances. Even for $25, we may be ready to gamble.

Most of us are afraid of losing. On average, economic experiments show we are twice more sensitive to losses than to gains.

Moreover, we are much more sensitive to a change in small losses than in big losses. If we expect to pay $10 for your meal and get a $20 bill, we become quite upset.

If we buy a house $1.01M instead of $1M, it may not really matter.

In a nutshell, our valuation of losses is often convex.

Loss aversion is one of the most fundamental heuristics in our financial decisions. It is also present among very young children and capuchin monkeys (Chen, 2006). We can speculate that this makes sense from an evolutionary point of views. Our hot-headed and fearless ancestor may not have survived long in the savannah…

Probability distortion

Finally, you may not care about buying a pill with a 90% or 90.5% chance of success. But you may strongly prefer a 100% safe pill over a pill with a 0.5% risk.

To sum things up, we tend to distort probabilities. Small probabilities are overweighted. Large probabilities are underweighted. 30% is most of the time the limit.

We are also more or less optimist or confident in our financial decisions. If you drive a car for the first time and are told that 1M+ people die each year on the road, you may be quite scared and over weigh the probability of crashing. If you are an experienced driver, you may on the contrary be overconfident and under weigh the risk.

Prospect Theory is a decision-making model under risk which takes into account all these psychological factors. Developed by the Nobel Prize winners Daniel Kahneman and Amos Tversky in 1979, Prospect Theory is at the heart of behavioral finance (Kahneman & Tversky, 1979; Wakker, 2011).

Prospect Theory

Let’s take a bet, L, that pays x with a probability of either p or y. x and y can be positive or negative.

How do people value this bet?

As discussed above, the mathematical average/mean o Expected Value does not take into account personal preferences and cognitive bias.

We assume that each person has a unique value function U to evaluate the bet L:
U(L) = w(p)u(x) + (1 −w(p))u(y)

With u the utility function:
u(x) = xα, x > 0,
u(x) =  − λ( − x)α, x < 0 α is the concavity/convexity or “Greed” parameter. λ is the loss aversion or “Fear” parameter.

w is the probability weighting function with two parameters: w(p) = (exp( − ( − ln p)σ))β with β>0 the elevation/pessimism parameter and σ>0 the curvature/likelihood insensitivity parameter. If 0<β<1, the probability weighting function captures optimism. If β > 1 the probability weighting function captures pessimism. If 0<σ<1, the function reflects inverse s-shape pattern where small probabilities are over weighed and large probabilities are under weighed.

Contrary to the classical framework, there is not one specific parameter for risk-taking in Prospect Theory.

Risk tolerance results from the combination of loss aversion, concavity of the utility function, and pessimism and likelihood insensitivity of the probability function.

Most empirical studies show that we tend to exhibit a fourfold pattern with risk-aversion for large probabilities and risk-seeking for small probabilities in the gain domain, risk-aversion for small probabilities and risk-seeking for large-probabilities in the loss domain.

There is however a high variability at the level of the individual.

L’article Behavioral finance, risk profiling and Prospect Theory est apparu en premier sur Neuroprofiler.

There are tons of cognitive biases but you will discover in this short article a list of biases which affect more specifically our investment decisions.

List of cognitive biaises

• Ambiguity effect

The tendency to avoid options for which missing information makes the probability of possible outcomes unknown.

• Anchoring

The tendency to rely too heavily, or “anchor,” on one specific piece of information when making decisions (also called “insufficient adjustment”).

• Belief bias

An effect where someone’s evaluation of the logical strength of an argument is biased by the believability of the conclusion.

• Confirmation bias

The tendency to search for or interpret information in a way that confirms one’s preconceptions.

• Congruence bias

The tendency to test hypotheses exclusively through direct testing, in contrast to tests of possible alternative hypotheses.

• Conjunction fallacy

The tendency to assume that specific conditions are more probable than general ones.

• Denomination effect

The habit to spend more money when it is denominated in small amounts (e.g. coins) rather than large amounts (e.g. bills).

• Endowment effect

The fact that people often demand much more to give up an object than they would be willing to pay to acquire it.

• Framing effect

Drawing different conclusions from the same information, depending on how that information is presented.

• Gambler’s fallacy

The tendency to think that future probabilities are altered by past events.

• Hindsight bias

Sometimes called the “I-knew-it-all-along” effect, the tendency to see past events as being predictable at the time those events happened.

• Loss aversion

The higher sensitivity to losses than to gains

• Mere exposure effect

The tendency to express undue liking for things merely because of familiarity with them.

• Omission bias

The tendency to judge harmful actions as worse, or less moral, than equally harmful omissions (inactions).

• Optimism bias

The tendency to be over-optimistic, overestimating favorable and pleasing outcomes.

• Planning fallacy

The tendency to underestimate task-completion times.

• Primacy effect

The greater ease of recall of initial items in a sequence compared to items in the middle of the sequence.

• Recency bias

A cognitive bias that results from disproportionate salience of recent stimuli or observations – the tendency to weigh recent events more than earlier events.

• Status quo bias

The tendency to like things to stay relatively the same.

• Defensive attribution hypothesis

Defensive attributions are made when individuals witness or learn of a mishap happening to another person. In these situations, attributions of responsibility to the victim or harm-doer for the mishap will depend upon the severity of the outcomes of the mishap and the level of personal and situational similarity between the individual and victim. More responsibility will be attributed to the harm-doer as the outcome becomes more severe, and as personal or situational similarity decreases.

• Dunning–Kruger effect

An effect in which incompetent people fail to realize they are incompetent, because they lack the skill to distinguish between competence and incompetence.

• Egocentric bias

Fundamental attribution error – the tendency for people to over-emphasize personality-based explanations for behaviors observed in others while under-emphasizing the role and power of situational influences on the same behavior.

• Halo effect

The tendency for a person’s positive or negative traits to “spill over” from one area of their personality to another in others’ perceptions of them.

• Ingroup bias

The tendency for people to give preferential treatment to others they perceive to be members of their own groups.

• Projection bias

The tendency to unconsciously assume that others (or one’s future selves) share one’s current emotional states, thoughts and values.

• Self-serving bias

The tendency to claim more responsibility for successes than failures. It may also manifest itself as a tendency for people to evaluate ambiguous information in a way beneficial to their interests.

• Worse-than-average effect

A tendency to believe ourselves to be worse than others at tasks which are difficult.

References

List from Wikipedia
“Making great decisions,” McKinsey Quarterly, Avril 2013, by Dan Lovallo and Olivier Sibony

L’article Cognitive Bias: The Cognitive Zoo est apparu en premier sur Neuroprofiler.

Let’s imagine you are given a marshmallow and asked to wait for 10 minutes to get another one under the condition that you do not eat the original marshmallow.

Look at the video below to discover this famous psychological experiement!

Adults are rarely much more mature!

Although this experience may seem trivial, it has significant depth and represents certain neural processes.

Preference dilemma in the Marshmallow effect

We tend to prefer immediate reward than future rewards, even if the future rewards are higher. Many experiments have shown that most animals have the same behavior.

Neural correlations have been found through various fMRI experiments to give a biological explanation of this biased behavior. Most of the time, participants have made a series of choices between immediate and future monetary reward while researchers were studying the activation of their brain.

They found that when making such choices, let’s say between winning 10 dollars today or 20 dollars in three months, two parts of our brain were competing.

Our parietal cortex, more sensitive to numbers, time and space magnitudes, ”wants” to be rewarded immediately while our striatum, in charge of reward evaluation, ”wants” the higher reward.

For many of us, the parietal cortex has the advantage over our reward system so that we prefer a lower immediate reward rather than waiting to get a better one.

How to model our inter-temporal choices?

Inter-temporal choice is the study of the relative value people assign to two or more payoffs at different points in time, like getting one marshmallow right now or waiting to get another one.

Most choices require decision-makers to trade-off costs and benefits at different points in time.

In one of the major contributions to behavioral economics, Loewenstein and Prelec (1992) set the foundation for this behavioral approach to decision making over time. Several models have followed (Laibson, Samuelson, Ebert and Prelec…).

They found that we do make the same inter-temporal choices in the case of gains or losses. If we have the choice between paying €300 now or €400 in one year (supposing that we do not invest this money) , we will tend to postpone this unpleasant event even if we have to pay more later.

On the contrary, as for the children in the marshmallow experiment, we will prefer to get the €300 now rather than waiting one year to get the €400. Human brains tend to procrastinate.

Moreover, we tend to overvalue very immediate rewards. We prefer to buy higher delivery fees to get our new phone the soonest as possible. This bias is called present bias.

What is the impact of cognitive time discounting on investment decisions?

Most investment decisions are made for the middle or long run. Investing in a stock, in an acquisition, or in an industrial project is betting on the future. Time preferences biases highly affect such financial decisions.

References:
Parieto-temporal activation modulates pure time preference. Wegener, J., Madsen, K., Christensen, M. S., & Jamison, J. (2008)
Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature neuroscience, 10(12), 1625-1633.

McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503-507.
Loewenstein, G., & Prelec, D. (1992). Anomalies in Intertemporal Choice: Evidence. Choice over time, 119.

L’article Cognitive bias: The Marshmallow effect est apparu en premier sur Neuroprofiler.

Who is targeted?

Every type of insurance provider, from the multinational insurance company to the car-rental employee. The measure affects both the businesses and their employees, and the types of investment products that are on the market.

What are the major measures?

1. Implement and order all types of insurance sellers to complete yearly mandatory training
2. Forbid all types of compensation based on the product sold – all sales must be made closest to the customers’ needs and bonuses and compensations based on the type or number of product sold, or any kind of financial leading to a conflict of interest is forbidden.
3. Increase transparency by making available to the customer all information needed for his/her choice
4. Command better knowledge of the customer in order to serve him/her better

What impact on pre-sale customer profiling?

The insurance processes must clearly study products according to their market (to their customers), particularly risk-profiling of the market:

• Type of client
  • Experience and knowledge
  • Financial situation and ability to bear losses
  • Risk tolerance and compatibility of the risk/reward profile of the product with the customer profile
  • Client objectives
  • Client needs

Additional requirements are demanded for insurance-based investment products, related to conflict of interests and transparency regarding remuneration and information: the insurance provider must be able to explain clearly and per customer profile the details of the contract, and must thus know its customer-profile.

Ressources

http://eur-lex.europa.eu/legal-content/FR/TXT/?uri=uriserv%3AOJ.L_.2016.026.01.0019.01.FRA

http://ec.europa.eu/finance/insurance/consumer/mediation/index_fr.htm

Impact in the UK: https://united-kingdom.taylorwessing.com/en/insurance-distribution-directive-key-themes

http://www.cms-lawnow.com/regzone/articles/2015/december/insurance-distribution-directive-adopted

https://www2.deloitte.com/content/dam/Deloitte/cz/Documents/finance/Regulatory-agenda-2016-2017.pdf

http://vff.no/assets/Presentasjoner/2016/MiFID-II_VFF_24.11.2016.pdf

L’article The 2018 European policy on insurance companies and product (IDD) est apparu en premier sur Neuroprofiler.