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Behavioural economics concepts

Conventional economics assumes that all people are both rational and selfish. In practice, this is often not the case, which leads to the failure of traditional models. Behavioural economics studies the biases, tendencies and heuristics that affect the decisions that people make to improve, tweak or overhaul traditional economic theory. It aids in determining whether people make good or bad choices and whether they could be helped to make better choices. It can be applied both before and after a decision is made.

Search Heuristics

Before a decision is made, there needs to be a minimum of two options. Behavioural economics employs search heuristics to explain how a person may evaluate their options. Search heuristics is a school of thought that suggests that when making a choice, it is costly to gain information about options and that methods exist to maximise the utility that one might get from searching for information. While each heuristic is not wholistic in its explanation of the search process alone, a combination of these heuristics may be used in the decision making process. There are three primary search heuristics.

Satisficing

Satisficing is the idea that there is some minimum requirement from the search and once that has been met, stop searching. Following the satisficing heuristic a person may not necessarily acquire the most optimal product (ie. the one that would grant them the most utility), but would find one that is "good enough". This heuristic may be problematic if the aspiration level is set at such a level that no products exist that could meet the requirements.

Directed Cognition

Directed cognition is a search heuristic in which a person treats each opportunity to research information as their last. Rather than a contingent plan that indicates what will be done based on the results of each search, directed cognition considers only if one more search should be conducted and what alternative should be researched.

Elimination by aspects

Whereas satisficing and directed cognition compare choices, elimination by aspects compares certain qualities. A person using the elimination by aspects heuristic first chooses the quality that they value most in what they are searching for and sets an aspiration level. This may be repeated to refine the search. ie. identify the second most valued quality and set an aspiration level. Using this heuristic, options will be eliminated as they fail to meet the minimum requirements of the chosen qualities.

Heuristics and Cognitive Effects

Outside of searching, behavioural economists and psychologists have identified a number of other heuristics and other cognitive effects that affect people's decision making. Some of these include:

Mental Accounting

Mental accounting refers to the propensity to allocate resources for specific purposes. Mental accounting is a behavioral bias that causes one to separate money into different categories known as mental accounts either based on the source or the intention of the money.

Anchoring

Anchoring describes when people have a mental reference point with which they compare results to. For example, a person who anticipates that the weather on a particular day would be raining, but finds that on the day it's actually clear blue skies, would gain more utility from the pleasant weather because they anticipated that it would be bad. Herd Behavior

This is a relatively simple bias that reflects the tendency of people to mimic what everyone else is doing and follow the general consensus. It represents the concept of "wisdom of the crowd".

Framing Effects

Stereotypes and anecdotes that act as mental filters are referred to in behavioural economics as Framing effects. People may be inclined to make different decisions depending on how choices are presented to them.

Biases and Fallacies

While heuristics are tactics or mental shortcuts to aid in the decision making process, people are also affected by a number of biases and fallacies. Behavioural economics identifies a number of these biases that negatively affect decision making such as:

Present Bias

Present bias reflects the human tendency to want rewards sooner. It describes people who are more likely to forego a greater payoff in the future in favour of receiving a smaller benefit sooner. An example of this is a smoker who is trying to quit. Although they know that in the future they will suffer health consequences, the immediate gain from the nicotine hit is more favourable to a person affected by present bias. Present bias is commonly split into people who are aware of their present bias (sophisticated) and those who are not (naive).

Gambler's Fallacy

Also known as the Monte Cristo fallacy, the Gambler's Fallacy is the unmerited belief that because an event occurs more frequently in the past it is less likely to occur in the future (or Vice Versa), despite the probability remaining constant. For example, if a coin had been flipped three times and turned up heads every single time, a person influenced by the gambler's fallacy would predict tails simply because of the abnormal number of heads flipped in the past, even though of course the probability of a heads is still 50%.

Narrative Fallacy

Narrative fallacy is almost the opposite of the Gambler's fallacy and is a theory states that one is more likely to predict a different event happening than what happened previously simply because it had already happened previously. For example, a person may be more likely to predict the result of a coin flip to be tails because the previous three flips were heads, even though the probability of the next flip is still 50/50.

Loss Aversion

Loss aversion refers to the tendency to place greater weight on loss than disappointment. In other words, they're far more likely to try to assign a higher priority on avoiding losses than making investment gains. As a result, some investors might want a higher payout to compensate for losses. If the high payout isn't likely, they might try to avoid losses altogether even if the investment's risk is acceptable from a rational standpoint.

Recency Bias

When a person places greater expectation on a particular outcome simply because that outcome had just occurred, that person may be affected by recency bias. To return to the coin flipping example, given that the previous one or two flips were heads, a person affected by recency bias would continue to predict that heads would be flipped.

Confirmation Bias

Also referred to as hindsight bias, Confirmation bias reflects the tendency to favour information or results that support one's own beliefs or values.

Familiarity Bias

Familiarity bias simply describes the tendency of people to return to what they know and are comfortable with. Familiarity bias discourages affected people from exploring new options and may limit their ability to find an optimal solution.

Status Quo Bias

Status quo bias describes the tendency of people to keep things the way they are. It is a particular aversion to change in favor of remaining comfortable with what is known.

Behavioral finance

Behavioral Finance is the study of the influence of psychology on the behavior of investors or financial analyst. It assumes that investors are not always rational, have limits to their self-control and are influenced by their own biases. For example, behavioral law and economics scholars studying the growth of financial firms’ technological capabilities have attributed decision science to irrational consumer decisions. It also includes the subsequent effects on the markets. Behavioral Finance attempts to explain the reasoning patterns of investors and measures the influential power of these patterns on the investor's decision making. The central issue in behavioral finance is explaining why market participants make irrational systematic errors contrary to assumption of rational market participants. Such errors affect prices and returns, creating market inefficiencies.

Traditional finance

The accepted theories of finance are referred to as traditional finance. The foundation of traditional finance is associated with the modern portfolio theory (MPT) and the efficient-market hypothesis (EMH). Modern portfolio theory is a stock or portfolio's expected return, standard deviation, and its correlation with the other stocks or mutual funds held within the portfolio. With these three concepts, an efficient portfolio can be created for any group of stocks or bonds. An efficient portfolio is a group of stocks that has the maximum (highest) expected return given the amount of risk assumed, contains the lowest possible risk for a given expected return. The efficient-market hypothesis states that all information has already been reflected in a security's price or market value, and that the current price of the stock or bond always trades at its fair value. The proponents of the traditional theories believe that 'investors should just own the entire market rather than attempting to outperform the market'. Behavioral finance has emerged as an alternative to these theories of traditional finance and the behavioral aspects of psychology and sociology are integral catalysts within this field of study.

Evolution

The foundations of behavioral finance can be traced back over 150 years. Several original books written in the 1800s and early 1900s marked the beginning of the behavioral finance school. Originally published in 1841, MacKay's 'Extraordinary Popular Delusions' and 'The Madness of Crowds' presents a chronological timeline of the various panics and schemes throughout history. This work shows how group behavior applies to the financial markets of today. Le Bon's important work, The Crowd: A Study of the Popular Mind, discusses the role of "crowds" (also known as crowd psychology) and group behavior as they apply to the fields of behavioral finance, social psychology, sociology, and history. Selden's 1912 book Psychology of The Stock Market was one of the first to apply the field of psychology directly to the stock market. This classic discusses the emotional and psychological forces at work on investors and traders in the financial markets. These three works along with several others form the foundation of applying psychology and sociology to the field of finance. The foundation of behavioral finance is an area based on an interdisciplinary approach including scholars from the social sciences and business schools. From the liberal arts perspective, this includes the fields of psychology, sociology, anthropology, economics and behavioral economics. On the business administration side, this covers areas such as management, marketing, finance, technology and accounting.

Critics contend that behavioral finance is more a collection of anomalies than a true branch of finance and that these anomalies are either quickly priced out of the market or explained by appealing to market microstructure arguments. However, individual cognitive biases are distinct from social biases; the former can be averaged out by the market, while the other can create positive feedback loops that drive the market further and further from a "fair price" equilibrium. It is observed that, the problem with the general area of behavioral finance is that it only serves as a complement to general economics. Similarly, for an anomaly to violate market efficiency, an investor must be able to trade against it and earn abnormal profits; this is not the case for many anomalies.A specific example of this criticism appears in some explanations of the equity premium puzzle. It is argued that the cause is entry barriers (both practical and psychological) and that the equity premium should reduce as electronic resources open up the stock market to more traders. In response, others contend that most personal investment funds are managed through superannuation funds, minimizing the effect of these putative entry barriers. In addition, professional investors and fund managers seem to hold more bonds than one would expect given return differentials.

Quantitative behavioral finance

Quantitative behavioral finance uses mathematical and statistical methodology to understand behavioral biases. In marketing research, a study shows little evidence that escalating biases impact marketing decisions. Leading contributors include Gunduz Caginalp (Editor of the Journal of Behavioral Finance from 2001 to 2004), and collaborators include 2002 Nobel Laureate Vernon Smith, David Porter, Don Balenovich, Vladimira Ilieva and Ahmet Duran, and Ray Sturm.

Financial models Some financial models used in money management and asset valuation incorporate behavioral finance parameters. Examples:

• Thaler's model of price reactions to information, with three phases (underreaction, adjustment, and overreaction), creating a price trend.

• One characteristic of overreaction is that average returns following announcements of good news is lower than following bad news. In other words, overreaction occurs if the market reacts too strongly or for too long to news, thus requiring an adjustment in the opposite direction. As a result, outperforming assets in one period is likely to underperform in the following period. This also applies to customers' irrational purchasing habits.
• The stock image coefficient.

Related Fields

Experimental economics

Main article: Experimental economics

Experimental economics is the application of experimental methods, including statistical, econometric, and computational, to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law (experimental law and economics).

A fundamental aspect of the subject is design of experiments. Experiments may be conducted in the field or in laboratory settings, whether of individual or group behavior.

Variants of the subject outside such formal confines include natural and quasi-natural experiments.

Neuroeconomics

Main article: Neuroeconomics

Neuroeconomics is an interdisciplinary field that seeks to explain human decision making, the ability to process multiple alternatives and to follow a course of action. It studies how economic behavior can shape our understanding of the brain, and how neuroscientific discoveries can constrain and guide models of economics.

It combines research methods from neuroscience, experimental and behavioral economics, and cognitive and social psychology. As research into decision-making behavior becomes increasingly computational, it has also incorporated new approaches from theoretical biology, computer science, and mathematics. Neuroeconomics studies decision making by using a combination of tools from these fields so as to avoid the shortcomings that arise from a single-perspective approach. In mainstream economics, expected utility (EU) and the concept of rational agents are still being used. Many economic behaviors are not fully explained by these models, such as heuristics and framing.

Behavioral economics emerged to account for these anomalies by integrating social, cognitive, and emotional factors in understanding economic decisions. Neuroeconomics adds another layer by using neuroscientific methods in understanding the interplay between economic behavior and neural mechanisms. By using tools from various fields, some scholars claim that neuroeconomics offers a more integrative way of understanding decision making.

Evolutionary psychology

Main article: Evolutionary psychology

Further information: Evolutionary economics

An evolutionary psychology perspective states that many of the perceived limitations in rational choice can be explained as being rational in the context of maximizing biological fitness in the ancestral environment, but not necessarily in the current one. Thus, when living at subsistence level where a reduction of resources may result in death, it may have been rational to place a greater value on preventing losses than on obtaining gains. It may also explain behavioral differences between groups, such as males being less risk-averse than females since males have more variable reproductive success than females. While unsuccessful risk-seeking may limit reproductive success for both sexes, males may potentially increase their reproductive success from successful risk-seeking much more than females can.