Introduction The rise and fall of the stock market bubbles is used to explain the inefficiency of market hypothesis (Dufwenberg et al., 2005). The emerging literature is not able to comprehensively capture and explain the dramatic movements in the share prices that were witnessed during the rise and the crash of bubbles. There are two significant events that have laid the foundation to the understanding of this concept. These are the 1990s NASDAQ craze and the mortgage financing that explained the changes in the housing prices (Nofsinger, 2011). The ineffectiveness of the market hypothesis is to explain the course of stock market bubbles and crashes has necessitated the application of the behavioral finance, which is the essence of this paper. Defining Bubble Fuster et al (2010) define asset bubble as a significant difference between the current price of an asset and its intrinsic value, which is characterized by instant and accelerating prices followed by precipitous declines. Given that fundamental value cannot be observed, this definition may not be applied easily. In addition, intrinsic value is only estimated, but there is a wide disagreement on this measurement. According to Zweig (2007), it is not easy to distinguish bubbles from ordinary fluctuations in prices that are associated with economic of market factors. For instance, developing a clear understanding of the fluctuations in the stock prices between 1992 and 1999 have been problematic among scholars because of the ineffectiveness of the market hypothesis that has dominated the scholarly analysis of market trends. The same challenge has also been witnessed in the attempts to understand the effect of the changes in the housing prices in the US during 2007 (Shiv et al., 2005). In the stock market, some sectors experience rise and subsequent fall in prices, such as those witnessed in the growth stock since 1950s to 1970s (Dufwenberg et al., 2005). This sectoral fluctuation has also been a challenge among the theorist of market hypothesis. Though the impact of fluctuations in the 1950s stock played a role in the 1970s market bubbles, it was associated with macroeconomic factors (Thaler, 2005). The larger section of the decline in market was, however, linked to a sudden increase in the global prices of oil in relation to a slowdown in growth of global economy, but not solely bubble bursting. In addition, the price fluctuations in 1987 were linked to the rising rates of interest and the persistent slowdown in the global economy. This means that there have been periodic rectifications in the global economy to correct the fluctuations (Sornette, 2003). In this case, whereas other corrections may be termed as collapse in the bubbles, others are related to the market conditions and economic changes. Therefore, the definition a bubble depends on the nature of extreme valuations in the price changes, especially in a retrospective manner. Application of Behavioral Fiancé Model Behavioral model Stage 1: The Initial Forecast In a financial system, the decision-making process consists of forecast about the future, which occurs in each household and institution. In this case, each forecast includes an expected development of future events that relate to home mortgages, stock prices and other capital goods. Lux & Marchesi (2000) note that each decision, despite being financial, is based on the ability to forecast the future value. These decisions may also be consequential such as pursuing relationship, choosing a career or making investment decisions. What determines the choice of decision is the satisfaction to be achieved as illustrated by prospect theory (Boswijk et al., 2007). Therefore, what people perceive about the future is reflected in institutional decisions. In this case, the financial decisions in the financial markets may be intuitive or ad hoc in the minds of the agents of decisions making. For instance, an investor may forecast high returns on investments to inform his choice to increase his investments. To forecast the future, representative heuristics theorist notes that people use the past trends, which creates biases in the analysis of future events (Thaler, 2005). Therefore, this bias relates to unpredictable fluctuations caused by the deterministic methods of market hypothesis. Stage 2: Overconfidence Representative error in the case of heuristic approach is caused by the inability to think rationally about the statistical relationship among various market trends (Malkiel, 2003). For instance, the past and present events may lead to a pervasive bias, which creates overconfidence that makes market participants to extrapolate positive events into the future. In this case, overconfidence is intertwined with excessive extrapolation that makes financial market to be too rosy. Overconfidence is a significant factor in the study of behavioral finance, which occurs in most decisions made by men by rating themselves above average. According to Hofstede’s (1984), men tend to be more confident than women and thus, they are the majority in the trade of brokerage accounts even if they produce inferior returns. Overconfidence is related to miscalibration or overestimation of the ability and skills of the decision-maker. Therefore, the subsequent outcome of overconfidence is a bias
presented in decision-making, excessive extrapolation (Kindleberger and Aliber, 2009). Excessive extrapolation means the behavior of overweighting the recent positive outcomes and underweighting the long-term information (Nofsinger, 2005). This explains why most people tend
to increase their investments in a company when its stock is performing well regardless of the future possibilities of collapsing. This phenomenon is linked to the market fluctuations such as bubbles and crashes, which is calibrated in relation to the actual risk of the stock. For instances, when the economic condition improves and the prices increase, the investors concern themselves with the current positive experience. Therefore, they extrapolate the future that is skewed on present positive experiences. In this cases, the memory of poor returns reduces and loses its salience, hence the forecast of future outcomes become romantically positive. The psychological response to the consequences of return may increase the fluctuations of prices in the market. Stage 3: Group Amplification or Transmission According to the cognitive theory, any group must exhibit divergent perceptions about the future as witnessed in financial system (Thaler, 2005). This represents a spectrum of perceptions of the future, which has two extreme end al competing to achieve the same goal. The differences among these competing perceptions are reconciled in the price negotiations that occur daily in the financial markets. Therefore, unrealistic and extreme perceptions such as overconfidence are factored out. Experienced investors capitalize on the mistakes of the investors with miscalculated views to profit (Shiv et al., 2005). Therefore, rational investors will use the present overconfidence of other investors to increase his or her stock in the future. The question is how the entire financial market develops this overconfident attitude. A significant condition for bubbles is an increase in enthusiasm for a certain asset among a big number of participants (Shiv et al., 2005). Though those who are opposed to prevailing view may insist on the opposite side of the trade by forecast the likelihood of a fall in price of an asset, they tend to be few not to garner adequate forces to countervail the majority views. Therefore, the price of an item shoots up. At this stage, there are two types of group psychology, group polarization and group think, which come into action. It is through these psychological dimensions that biases of individual decision-making are passed on and enhanced to group levels. This group may be a financial market or the financial system as a whole. Groupthink is a poor decision-making process that is related with fiascos such as a feeling of invulnerability by rationalizing its behavior, and systematically ignoring external factors and alternatives (Chen et al, 2001). Group polarization on the other hand illustrates the likelihood of a group to decide riskier options that goes against individual decisions. Though the terms groupthink and group polarization mean small groups, the current development in communication technology allows the inclusion of financial systems or entire market (Hirshleifer and Hong, 2003). Bubbles do not occur from individual overconfidence or representativeness error, but arises when communication technology contributes to both collective shift to risky behaviors and exchange of information among people (Shiv et al., 2005). Through social interaction in the markets, the attitude of the whole financial system changes to a higher level of risk, which most market actors agrees to be too risky. Little study has been done in an attempt to link the social-psychological elements with bubble information. For instance, Malkiel, B. G. (2003) documented a study that captured the group psychology dynamics in the assessment of US stock market collapse of 1929.by analogy, the financial system consist of human decision-makers who shift collectively to riskier positions. However, given that each individual member preserves his relative distance from others, the internal view of the role of the market actors and market are unchanged. In other words, groupthink and group polarization are also called herding (Dufwenberg et al., 2005). The significant aspect, in terms of its impacts on bubbles, is that it causes a transformation of personal biases into social or group biases. It makes the social or financial system to take riskier decisions because of the dynamics of communication by acting as a group which is facilitated by technology. Rather than moderating the excess optimistic forecasts through the integration of competing ideas, technology magnifies the errors of decision-making in the system and its actors, which represents overconfidence or representativeness error. Stage 4: Recalibration In stage four of market bubbles there is recalibration of the expectations in relation to reality and the coexisting collapse (Barberis and Thaler, 2003). At this point, the actual data from the field makes market actors to start questioning their extremely rosy extrapolation and the consensus made through group opinion. This involves the recalibration of the excessively optimistic group extrapolations in relation to real observed data within the financial markets and the economy. In some contexts, the real data may be overwhelming to question the excessively optimistic extrapolation of the market actors. In the context of bubble in the prices of stock, it happens when the expected earnings and revenue growth rate declines below the optimistic expectations (Nofsinger, 2005). For instance, in the case of mortgage bubbles, it happens when the default rates starts to shoot up unexpectedly, contrary to the initial assumptions of the market participants. According to Chen et al (2001), recalibration stage is the restoration of a rational expectation of the future. At this stage, market actors recognize that their extrapolation of the future were unrealistically positive and revises them based on the reality of the data. The nature of changes depends on the extent of the expectation. Zweig (2007) argues that this rational reexamination of the expectation may also be enhanced by group polarization which caused bubble inflations by working in a reverse manner. The group dynamic in this case is referred to as conservative shift, which is the opposite of risky shift (Hirshleifer and Hong, 2003). The collective group dynamics becomes risk-averse compared to individual perception. The adjustment means gradual deflation of the price of an asset that had bubbled. Conclusion A lot of study on the course of stock market bubbles and crashes is based on analysis of market hypothesis. However, this popular approach has been criticized for providing partial information on market issues, especially the impact of psychological factors on stock market bubbles and crashes. Therefore, a new shift in the analysis centers around the contribution of behavioral finance as discussed in this essay based on four-stage model. The first stage illustrates how market participants make forecasting errors based on their past and current information to cause future bubbles. The second stage argues that participants exaggerate their expected forecasts because of overconfidence. The impact of groupthink and group polarization is captured in the third stage. The fourth stage illustrates how the exaggerations are adjusted based in the real data. Therefore, behavioral finance provides an effective explanation of stock market bubbles and crashes. References Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, 1(2), 1053-1128. Boswijk, H. P., Hommes, C. H., & Manzan, S. (2007). Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control, 31(6), 1938-1970. Chen, J., Hong, H., & Stein, J. C. (2001). Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics, 61(3), 345-381. Dufwenberg, M., Tobias, L. and Evan M. (2005). ‘Bubbles and Experience: An Experiment’. American Economic Review. 95(5). 1731-1737. Fuster, A., David, L. and Brock, M. (2010). ‘Natural Expectations and Macroeconomic Fluctuations’. The Journal of Economic Perspectives. 24(4). 67-84. Hirshleifer, D., & Hong Teoh, S. (2003). Herd behaviour and cascading in capital markets: A review and synthesis. European Financial Management, 9(1), 25-66. Hofstede, Geert (1984). Culture’s Consequences: International Differences in Work-Related Values (2nd ed.). Beverly Hills CA: SAGE Publications. Kindleberger, C. P., & Aliber, R. Z. (2009). Manias, panics and crashes: A history of financial crises. Hoboken, NJ: Wiley. Lux, T., & Marchesi, M. (2000). Volatility clustering in financial markets: A microsimulation of interacting agents. International Journal of Theoretical and Applied Finance, 3(04), 675-702. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 12(7), 59-82. Nofsinger, J. R. (2005). Social mood and financial economics. The Journal of Behavioral Finance, 6(3), 144-160. Nofsinger, J. R. (2011). The Psychology of Investing. Pearson Education, Boston, MA. Shiv, B., George, L., Antoine B., Hanna D. and Antonio R. D. (2005). ‘Investment Behaviour and the Negative Side of Emotion’. Psychological Science. 16:435-439. Sornette, D. (2003). Why stock markets crash: Critical events in complex financial systems. Princeton, N.J: Princeton University Press. Thaler, R. H. (2005). Advances in Behavioral Finance, 2. Princeton: Princeton University Press. Zweig, J. (2007). Your Money and Your Brain. Simon and Schuster, New York, NY.
