At the turn of twentieth century, the field of behavioural finance is becoming increasingly important as investors are growing sceptical of the efficient market hypothesis theory. The idea that intrinsic value of a stock is based on a company’s fundamentals has been severely tested ever since the crash in 1987 and 1997. There were severe loopholes in the efficient market hypothesis which surfaced relatively quickly after people became aware of the controversy in the 1987 market crash. However, a new area of finance has started to surface and was gaining recognition quickly from investors which are termed behavioural finance.
Unlike traditional financial theory, there are lots of behavioural finance documents which provide evidences that in various events because of investor mood, stock price can be influence heavily. This area of finance emphasizes the idea that investor mood determines the fluctuations in stock prices. However, in traditional financial theory and in particular efficient market hypothesis, this change just be recognized as an abnormal phenomenon. The aim of this article is to examine to what extent the investors’ mood may impact on the fluctuation of stock price and use ‘noise trader’ to explain calendar anomalies. In the flowering section we will carefully assess each of these claims in a controlled laboratory setting.
In order to figure out the issue, it is necessary to clarify the meaning of ‘noise trader’. The so called ‘noise trader’ describes the investors who cannot receive inside information, so their behavior will be irrational when they make decisions in equity market (Kyle, 1985). These irrational investors do not make decisions by the information of stock’s fundamentals, but focus on some messages do not related stock value (De Long, Shleifer, Summers and Waldmann, 1991).
Traditional financial theory specialists claim that although there are a few irrational investors whose investment strategy is not abide by the information of stock’s fundamentals, the influence of them can be ignored. They insistent that noise traders who will buy when the price rise and sell when the price down, so the influence made by them will be undermined by rational investors.
However, the scholars who support Behavioural Finance recognized that because of the erroneous stochastic beliefs noise trader conveyed, both stock prices and expected returns will cause price distortion (De Long, 1989). For noise traders, a temporary change in mood might causes a change in their views of risk. Thaler (1987) demonstrated that investors will be more risk seeking when they has a positive mood. Instead, for the people who are in negative mood, they will be more risk averse. Compared with positive mood, negative mood has a greater effect for equity market. By the study of behavioral economics experts, if an aviation disaster happened, the bad mood will cause the stock market suffered $60 billion (Sias, 2001). Moreover, even a sports event can influence stock market returns. When the team loss the game, the mood of traders will be depressed, which cause the market returns decline. Nevertheless, if the team wins the game, it will not cause a significant positive change. Hence, these phenomenon undoubtedly give strong evidences that investor mood effects stock price. Although in DSSW model, economists recognized noise trader can gain positive expected earnings (De Long，Sh1eifer，Summers and Waldman,1990), investor should have vigilance to noise trader risk. De Long (1990) described that noise trader may not recognize their irrational behavior for a long time. Even worse, these irrational investors may become more extreme during a long period.
Irrational investors always focus on the messages which unrelated to equity market. Noise traders have many believes that the change of various natural phenomenons can also make an effect on stock returns. However, most effects have been proved that that is popular beliefs affect their decisions, not because of the change of nature. For example, economic experts found lunar cycle effects – returns around new moon dates are twice of that around full moon. However, by study major U.S. stock indexed, Dichev and Janes (2003) found that there is no significant evidence proved lunar cycle effects in stock returns. Lunar cycle effects are the result of popular believes which will impact investors’ mood. Besides that, Cao and Wei (2005) consider that temperature significantly affects mood. The research of the global stock market has provided relatively strong evidence that people will be aggression in lower temperature environment while they will be apathy and aggression when temperature rise. Thus, many effects have proved that natural environment variation can also lead stock returns.
Moreover, as one of the most perplexing of all seasonal anomalies, the holiday effect has a strong impact on stock returns. For most western stock market, like UK and US, in the month preceding holiday like Christmas or Thanksgiving Day, stock returns are significantly higher (Kim and Park, 1994). There also exists Chinese New Year effect, in market like China and Singapore, the stock returns will be higher in the month preceding Chinese new year (Wong et al (1990)). “Window dressing” is a reason of institutional investors may make seasonally related changes in their portfolio. In order to have a better financial statement, fund managers on Wall Street will make an inventory of the stock in the storehouse before financial reporting dates. Generally, the date of financial report always close to natural calendar dates. Therefore, such actions will cause the movements of the seasonal price in equity market (Baker & Wurgler, 2007).
In a nutshell, the various events that occur out of the ordinary market movement have provided ample evidence to challenge the validity of the efficient market hypothesis made famous in the 1970s. Abnormal market movements during specific times have shown that market anomaly does exist and can be best explained by various factors that I have discussed above. The availability of these researches provides a mass of evidences that investor mood not just merely being associated with stock price, it leads to significant changes in stock prices. Many effect like Chinese New Year effect and Lunar Cycle effects can prove how investor mood impact stock returns. It is also arguable that equity price movement can be affected by investor sentiment which was well documented by various noise trader theories. Furthermore, the various market anomalies which occur in the equity market lend further credibility to the influence of investor sentiment.
The behavioural Von Neumann-Morgenstern preference proposed by the two scholars Von Neumann and Morgenstern (1944) in 1950s has been an ideal analysis paradigm for rational actors’ choosing behaviour in risky circumstances. It is subsequently integrated with the Subjective Expected Utility Theory (SEUT) raised by Savage (1954), who added theoretical arguments to the theory when decision makers faced uncertainty situations. The Expected Utility Theory (EUT), comprising Von Neumann-Morgenstern Theory and Expected Utility Theory, lays the critical foundation for the magnificent theoretical mansion for modern microeconomics and thereafter macroeconomics, finance, statistics, etc.( Mongin, 1997). It is argued that when rational individuals making choices with risky and uncertain prospects, they take into account of their expected utility, which equals to the sum of all possible outcome values adjusted by their corresponding probabilities. It other words, decision makers attempt to maximize their future risk weighted expected returns. In addition, the utility function is a concave function of wealth. However, results of numerous experiments and researches have significantly and systematically violated the axiom, which shake the fundamental creditability of the theoretical framework and result in the recent trend in behavioural finance propositions and researches. After a short description of EUT’s empirical challenges, this part of article will firstly critically examine the several mainstream behavioural financial theories which attempt to capture investors’ real preferences with risk or uncertainty. Their reasoning and contribution will be subsequently discussed respectively. Finally, after the discussion of their limitation, a brief conclusion and development prospects will be presented.
According to Tversky and Kahneman (1992) there are a number of substantial findings which are empirically evidenced that challenges the rational theory. 1) Framing effects, it refers to the decision makers’ preferences depend on the framing of choices. Specifically, different composition of probability for possible outcomes result in different preferences despite of their same weighted expected utility (Arrow, 1982). 2) Nonlinear preferences, according to EUT, the total utility changes in proportion and linear to a series of probability changes. However, this was contradicted to Allais’s (1953) famous experiment which demonstrated the distance from 0.99 to 1 had more strong influence on preference than the different between 0.10 and 0.11. 3) Source dependency, which means the uncertain sources, in addition to their probabilities, play important role in determining preferences. Heath and Tversky (1991) showed that people prefer choices that are relevant to their competence. 4) Risk seeking, this is prominently observed when decision makers face large probability of a negative return. 5) Loss aversion, it is a widely known effect that losses lead to severer pains than the happiness derived from same amount of gains (Kahneman and Tversky, 1984; Tversky and Kahneman, 1991)
To explain the violations of conventional expected utility axioms, Loomes and Sugden (1982) justified the EUT function by adding one factor that measures an individual’s anticipation of regret and rejoicing feelings. This theory proposes the rational assumptions and argues that rational behaviour should be defined more restrictively. However, a large number of observations still remain unexplained.
By researching people’s behaviour in making choices with limited few outcomes, Tversky and Kahneman (1986) raised the best-known and promising non-EUT theory: prospect theory. Unlike the traditional normative theory that assumes rationality of individuals, prospect theory is descriptive. The theory’s two main propositions are 1) the positive gain utility function concaves while loss utility function is convex and at a steeper rate, which is in consistent with the risk seeking and loss aversion effect. It could also be interpreted as diminishing sensitivity. 2) Probability transformation is nonlinear, which means relatively small probability tends to be overweighed and moderate or large probabilities tend to be underweighted. Prospect theory has several significant implications as follows: 1) difficulty in reaching negotiations since both parties should balance their gains and the counterparty’s gains, which are its own losses; 2) endowment effect, which means the investors are reluctant to change settled portfolios; 3) law distinguishes between losses and foregone gains. In addition, the theory would also lead to the disposition effect that makes investors hold losers longer and sell winner faster.
Rank-dependent / cumulative theory
Rank-dependent theory was introduced by Quiggin (1982) for situation under risk and by Schmeidler (1989) for situation under unknown probability. To solve the Allais paradox, in which people abandon the choice of large gains with only small probability for a huge lose, the rank-dependent theory only overweighs the extreme outcomes with small probability rather than all events with less probability. This is reached by applying cumulative probability distribution function instead of individual probabilities. However, this theory suffers from critics as well such as insufficiently plausible and limited creditability.
Cumulative prospect theory
After the presence of the cumulative functional theory, Tversky and Kahneman (1992) further improved the prospect theory to the prospect theory by introducing a two-part cumulative function as mathematical consideration of decision weights. It contributes the theory from several aspects. The improved theory becomes applicable for any finite prospects as well as probabilities’ continuous distribution. Furthermore, the source dependence problem could be solved to a certain extent since the theory comprises both risky and uncertain prospects. Weights for positive and negative gains are also enabled to be different, while generalizing the original version. Another prominent contribution is the release of skeptical and criticized presumption that the obviously dominated prospects could be removed in editing process. Specifically, it is because the cumulative prospect theory meets the stochastic dominance. Nonetheless, the theory’s explaining ability in terms of stochastic dominance contradictions in the nontransparent context will be compromised.
Theoretical and empirical limitations
As has been indicated by Tversky and Kahneman (1981), morden theories assumes the rational or “right” preference and choice are the benchmarks for rationality, however, the experiments were dedicated to solve the problem that how people make choices without thinking about what should be the rational choice. The experimental results become more complicated when taking into account of people’s emotional influences caused by outcome forms, choice framing, etc.
The preference theories cannot be complete and perfect in application since making decisions is a mental contingent and constructive process. Formal analysis for investors is inevitably affected by their approaches to define the problem, choose the approach of elicitation and the context. Nevertheless, there is strong evidence for orderly choices of people except for occasional “irrationality”.
To sum up, all of the financial theories developed by behavioural physiologists are dedicated to better capture the real preferences or behaviour for investors in practice. On the basis of empirical observations, they address the five main phenomena that cannot be interpreted by the classical Expected Utility Theory. The different versions of developed functions do increase the accountability and comprehensiveness of their financial theories. However, all of them suffer from respective and common theoretical as well as empirical limitations. As limitations for each specific theory have been mentioned respectively earlier, general limitations comprise experimental complexity, the limited applicability due to investors’ behaviour, etc. Besides a requirement for redefining and clarifying rational behaviour, these theories’ credibility should be further proved by supportive evidence and the five phenomena, particularly for framing effect and source dependency, should be better incorporated within the theoretical structure.
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