Behavioral Finance Theory - Theoretical Framework

2. Theoretical Framework

2.4. Behavioral Finance Theory

It is evident that the initial news of the rapid spreading of COVID-19 produced dramatic economic effects in the global stock markets characterized by an extraordinary increase in stock price volatility. This volatility induced by COVID-19 can also be explained through a behavioral finance perspective. The behavioral finance theory examines the psychology behind investor’s actions and decisions and how these affect the stock market.

The traditional models in economics and finance have often been criticized for assuming that the typical investor is able to pick the best trading decision considering all relevant information. On the other hand, behavioral finance theories suggest that investors and markets are not always rational in a world with uncertainty and limited time and information. For instance, according to the Bounding Rationality concept developed by Herbert Simon (1955), investors can only be rational up to a certain point due to their cognitive restrictions (Ackert & Deaves, 2010). Due to this, it is concluded that investors are not always rational, however, they do the best they can in the circumstances. This claim is supported by the concept of Information

overload, which suggests that people can experience difficulty in processing information in certain situations.

Usually, it occurs if there is too much information available. Therefore, information overload is often characterized by a state of confusion. Typically, people tend to avoid making a decision in these situations as they feel that the decision is too much complicated. In other cases, their decisions are colored by behavioral biases driven by emotions or a lack in the processing of information (Parker, 2021). In the following sub-sections, relevant investor biases will be presented. These include Loss Aversion in the Prospect Theory, Herding Behavior, and Confirmation Bias. Subsequently, in chapter 8, the theories will be applied to explain the findings from the executed event studies and panel data regressions.

2.4.1. The Prospect Theory and Panic Selling

The Prospect theory is a positive theory developed by Kahneman & Tversky (1979). The theory aims to describe the actual behavior of people, which is contrary to normative theory, such as the expected utility theory, which states that a reasonable person should act in a certain way. The prospect theory criticizes the expected utility theory as it claims that people in practice do not behave as stated in the expected utility theory.

The theory is based on studies by psychology researchers, which showed that responses to decision problems did not align with the expected utility theory (Kahneman & Tversky, 1979). In general, they observed three

key aspects, where the first aspect states that “People sometimes exhibit risk aversion and sometimes exhibit risk seeking, depending on the nature of the prospect.” The researchers observed that in some cases, the majority of people would choose a decision that is inconsistent with risk aversion. In contrast to the expected utility theory, the prospect theory allow a change in risk attitude depending on the nature of the prospect.

The second key aspect states that “Peoples’ valuations of prospects depend on gains and losses relative to a reference point. This reference point is usually the status quo.” Essentially, the second key aspect claims that the risk attitude varies across gains and losses. More specifically, the decision is based on a reference point, which usually is the current wealth. The third key aspect suggests that “People are averse to losses because losses loom larger than gains” (Ackert & Deaves, 2010). The researchers found that people reacted differently to potential losses and the equivalent absolute value in potential gains. If people were faced with a risky decision, which could lead to gains, they were more likely to exhibit a risk-averse behavior as they would prefer the solution that led to a higher certainty and lower expected utility. On the other hand, if people were faced with a risky decision, which could lead to losses, they were more inclined to exhibit a risk-seeking behavior suggesting that they preferred the solution where losses could be avoided even though this would result in a lower expected utility. This actively demonstrated that the decision-making behavior was inconsistent with the expected utility theory, where the choice with the highest expected utility should have been preferred. The behavior describes in the third key aspect is also known as the concept of loss aversion.

Essentially, it suggests that psychologically the pain of losing surpasses the pleasure of gaining. Consequently, people will be willing to take more risks to prevent a loss than generate a gain.

The loss aversion behavior has especially been observed in a bear market (Szyszka, 2011). Observing the abnormal declines in stock prices awakens fear among investors, which dominates emotions like greed. Thus, as the risk of experiencing losses increases, investors will tend to act irrationally and be willing to take more risk. At some point, the fear will turn into panic, which is followed by sellouts. Due to the fear of making further losses, investors will sort to panic selling in order to cut their losses. Therefore, panic selling is usually observed in the stock market during times where the market is falling (Boyer, 2020). A study by Professor Rui Yao from the University of Missouri in 2016 showed that investors who were economically vulnerable and investors who were economically well-off both assorted to panic selling in times of economic downturns. The reason for this was behavior was linked to loss aversion, which is considered one of the key predictors for investment mistakes (Hurst, 2016). Most researchers agree that the impact of panic selling can be self-destructive as the decisions are based on emotions and not reliable data. Essentially, panic selling results in locking in losses instead of avoiding them (Szramiakje, 2017).

2.4.2. Herding Behavior

Heuristics are necessary in order to make decisions in environments where there is limited information, attention, and processing capacity. A heuristic is a shortcut where only a subset of accessible information is utilized. Thereby, the complexity of decision-making can be reduced. The herding behavior is one among many heuristics and describes how people are influenced by the behavior of a larger group (Ackert & Deaves, 2010). The term stems from the tendency observed in the nature of animals. For instance, once a herd of animals begins to move towards a specific direction, the rest of the animals will start following the herd in the same direction. Hence, the behavior is defined by the individual’s tendency to follow the majority's sentiment (Pettinger, 2018). According to Banerjee (1992), herding can be defined as “everybody doing what everyone else is doing even when their private information suggests doing something else.” This behavior can also be observed in economics. In some cases, investors tend to mimic other investor’s actions and trading behavior.

This can oppose a challenge to the efficient market hypothesis as the investors' trading behavior can become collective irrational. Essentially, when people do not make their own independent decisions, the informativeness of the market is reduced (Banerjee, 1992).

The herding behavior has often been observed in the financial market during turbulent periods of uncertainty and market distress (Ackert & Deaves, 2010). There are several reasons for the occurrence of herding behavior.

According to Keynes (1936), people sometimes do not process new information efficiently and therefore tend to follow the majority in order to avoid being the outcast. More specifically, he concludes that “it is better for reputation to fail conventionally than to succeed unconventionally” (Keynes, 1936). Basically, in some cases, it is easier to “go with the flow” than to take an independent decision (Devenow & Welch, 1996). Even market analysts are prone to herding as they dislike standing out in the crowd. Recent research has shown that some analysts’ forecasts are biased as it does not fully reflect their private information and instead mimic the behavior of others to gain acceptance by investors and other analysts (Ackert & Deaves, 2010). In addition to this, Banerjee (1992) highlights that during times of uncertainty in a specific area of the financial market, investors who lack expertise in that particular field will resort to the advice provided by the majority of experts.

In these cases, individual investors can be influenced by the majority's opinion since they assume that the majority have access to some prevalent information that they do not (Banerjee, 1992). Since information acquisition can be costly, it is not always possible to determine which alternative is the best. In these cases, the best and most cost-efficient alternative can be determined through social learning, where the behavior of others is observed and followed (Bandura, 1977). It should also be noted that sometimes what seems like herding could actually be people acting on similar information (Ackert & Deaves, 2010). For example, if it is evident that one alternative is better, then it should not be a surprise that the majority is choosing that particular alternative.

However, whatever the reason for herding may be, most researchers would agree that herd behavior is one of the most prevalent reasons for irrational behavior, causing abnormal positive and negative returns in the stock market (Dhall & Singh, 2020). This irrational behavior will introduce volatility to the stock prices, resulting in the prices deviating from the fundamental value.

The herding behavior was, for instance, observed during the Dot-com bubble in the early 2000s, where the excessive speculation about the future prospects of internet technology led to a drastic increase in the valuations of US technology stock equity. Subsequently, the stock return of several internet-based stocks increased rapidly up until between 2001 and 2002, where the bubble burst and resulted in the shutdown of numerous online shopping companies (Hayes, 2019).

2.4.3. Overconfidence bias and the underestimation of risks

The overconfidence bias can occur in decision-making when people tend to overestimate their knowledge, skills, and the precision of their information. Additionally, the tendency is observed when people are excessively optimistic about the future and think they can control it more than what is considered logical by objective analysts (Ackert & Deaves, 2010). Various research has shown that most people believe that their skills are above average, which statistically does not make sense. This is also known as the better-than-average effect and is very common among investors and people in general. One study, for instance, showed that 93%

of the people living in the US claim they are better than the average driver (Svenson, 1981). However, this illusion of knowledge and illusion of control is not optimal since it can make one prone to making mistakes in investment decisions. Usually, investment decisions will seem less risky when an investor is overconfident.

Thus, this mindset can interfere with the investor’s ability to perform good risk management (Ackert & Deaves, 2010). Unrealistically optimistic investors will less likely detect warning signals which could indicate an upcoming market decline.

According to the study in 2016 by Professor Rui Yao from the University of Missouri, the overconfidence bias is also a key factor in causing investors to make common investment mistakes during market downturns. The study concludes that overconfident investors are more likely to sell their stocks and subsequently place the amount in the bank until the stock market returns to the bull market. However, this behavior is illogical since this usually means that these investors are selling too low (Hurst, 2016).

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