Efficacy of Behavioral Finance in Japan - Literature review Example

Efficacy of Behavioral Finance in Japan INTRODUCTION The efficient market hypothesis indicates that since market prices reflect all available information, containing information about the future, the only difference between the stock prices at time t and time t + 1 are phenomenon that can not possibly be predicted. Hence, in an efficient market, prices can be statistically tested and investigated for the random-walk hypothesis. Fama (1991) describes market efficiency as: A market in which firms can make production-investment decisions, and investors can choose among the securities that represents ownership of firms’ activities under the assumption that security prices at any time “fully reflect” all available information (Fama, 1991, p. 383) Fama (1991) divides efficiency in 3 levels: -The weak form hypothesis asserts that stock prices already reflect all information that can be derived by examining market trading data such as the history of past prices, trading volume, or short interest. -The semi strong form hypothesis states that all publicly available information regarding the prospects of a firm must be reflected already in the stock price. -The strong form hypothesis states that stock prices reflect all information relevant to the firm, even including information available only to company insiders. Numerous papers have demonstrated that early identification of new information can provide substantial profits. Insiders who trade on the basis of privileged information can therefore make excess returns, violating the strong form of the efficient market hypothesis. Even the earliest studies by Cowles (1933,1944), however, make it clear that investment professionals do not beat the market. It has already been stated that an efficient market is one where the prices of securities fully reflect all available information, but then what are the sufficient conditions for capital market efficiency? In an idealized world, such conditions would be No transaction costs in trading securities. All available information is available without cost to all market participants. All agree on the implications of current information for the current price and distributions of future prices of each security. The debate about market efficiency has resulted in thousands of empirical studies and literature attempting to determine whether particular markets are in fact ‘efficient’, and if so to what degree. In fact, the majority of studies and researches of technical theories have gone to the result that it is difficult to predict prices. Moreover, the random walk theory indicates that price movements will not follow any trends and so by knowing the past price movements it’s not possible to predict the future price movements. All these state that markets are in fact efficient. However, researchers have also exposed many stock market anomalies that seem to be inconsistent with the efficient market hypothesis. Trading strategies seem to be widespread among fund managers and there is little evidence that they would generate excess returns in practice (Malkiel, 2003). Evidence proof that the use of trading strategies might be closely related to behavioural anomalies. It is impossible to consistently make abnormal returns using a trading strategy based on a given set of information when the markets are efficient. This postulate, of course, is based on the premise that (1) all investors have cost-less access to currently available information about the future; (2) they are good analysts; and (3) they pay close attention to the market process and adjust their holdings appropriately. Till the late seventies, empirical studies supported the view that the capital markets are informationally efficient. Many models related to security valuation have been based on this concept of ‘informational efficiency of capital markets.’ However, the late seventies and the eighties brought in evidences questioning the validity and highlighting various anomalies related to the capital market efficiency. There are many focused studies that demonstrated the possible trading strategies yielding abnormal rates of return using the historical data and publicly available information ruling out the efficacy of markets. The efficient market hypothesis has been challenged by numerous studies on the grounds that there are often underrreactions or overreaction of stock markets to information. (Baberies et al, 1998; Daniel et al, 1998; Hong and Stein, 1999). Accordingly, in a variety of markets, sophisticated investors can earn superior or riskless profits by taking advantage of market imperfections (underreaction or overreaction). Fama (1998) is however against the empirical findings generated from these studies. He outlines a number of studies and identifies a series of flaws in these studies, which help them to achieve their desired results. Fama (1998) proposes two reasons why the efficient market hypothesis can never be rejected. Firstly Fama (1998) states that an efficient market generates a category of events that individually suggests that prices overreact to information. He suggests that in an efficient market apparent underreaction will be about as frequent as overreaction. Consequently if anomalies split randomly between overreaction and underreaction, they are consistent with the efficient market hypothesis. (Fama, 1998: pp 284). Secondly if unusual behaviour of long-term returns are so large that they cannot be attributed to chance, then an even split between over- and underreaction is a pyrrhic victory for market. (Fama, 1998). Fama asserts that the long-term return anomalies are sensitive to methodology. He concludes that they turn to become marginal or disappear when exposed to different models for expected (normal) returns or when different statistical approaches are used to measure them. Fama (1998) further states that even if these models are viewed one-by-one, most long term return anomalies can be attributed to chance. Following criticisms by Fama (1998), studies Baberies et al (1998), Harrison et al (1998) and Harrison and Stein (1998) have opened up new ground for testing momentum and reversal. They have all begun to develop behavioural models that aim to unify a range of previously documented anomalies in asset returns. (Hong et al, 2000). Baberies et al (1998) adopted a parsimonious model of investor sentiment, or how investors form beliefs, consistent with empirical findings of underreaction and overreaction to stock markets. Harrison et al (1998) tested the Hong-Stein version of the underreaction hypothesis. Their aim was to provide evidence that momentum reflects the gradual diffusion of firm-specific information. Harrison et al (1998) began their work by sorting stocks into different classes, for which information is a priori more or less likely to spread gradually. Their hypothesis was based on the assumption that stocks with slower information diffusion should exhibit more pronounced momentum. Harrison et al (1998) considered firm size as a basis for theor first set of tests. This is because information about small firms gets out more slowly. (Harrison and Stein, 1998). Given the fact that traditional asset pricing models such as the capital asset pricing model (CAPM), the Arbitrage pricing theory (APT), or the intertemporal capital asset pricing model (ICAPM) have a hard time explaining the growing set of stylised facts, most morden models today are turning to behavioural theories, where behavioural can be thought of as involving some departures from the classical assumptions of strict rationality and unlimited computational capacity on the part of investors. (Harrison and Stein, 1998). Harrison and Stein (1998) had the same goal like Baberis et al (1998) and Daniel et al (1998). However, their emphasis was based on the interaction of heterogeneous agents. Having identified a new ground of research based on behavioural studies, this study is aimed at testing the efficacy of behavioural finance in Japan using the model developed by Baberies et al (1998) to see if there is underreaction (reversal) or overreaction (momentum). This will be done by applying the BSV model to the Nikkei forward 255 and Nikkei Average. This will enable the researcher conclude whether there is momentum in any of the markets and compare the two markets to see if there are any similarities or differences between markets in the same country. The Nikkei Forward 255 is made up of mostly institutional investors while the Nikkei average is made up of public investors. Comparing the two markets can lead to interesting results and conclusions concerning market efficiency. The model will also be applied to the Dow Jones Ind, the FTSE100 and the Hang Seng in order to test for cross country similarities and differences in overreaction (momentum) and underreaction (reversal). Having said this, we will now describe the BSV model in detail and provide some strengths and weaknesses in the model. The BSV Model Only the formal model of will be presented here. The informal description of the model can be seen in Baberies et al (1998: pp 318-321). The model is based on the assumption that there is only one security and one investor. It is also assumed that the security pays out 100% of earnings as dividends. Lest assume that the earnings at time t are where is the shock to earnings at time t, which can take on only two values, +y or –y. It is believed by the investor that the value of is determined by one of two models, Model 1 and Model 2, depending on the “state” of the “regime” of the economy. (Barberies et al, 1998). The two models have the same structure and are both markov processes because the value taken by depends only on the value taken by . The difference lies in the transition probabilities. The transition matrices for the two models are: The key assumption in the above models is that is small falling between 0 and 0.5, while is large and falls between 0.5 and 1. According to model 1 a positive shock is likely to lead to underreaction (reversal) while under model 2 a positive shock will lead to overreaction (momentum). (Barberies et al, 1998). Since the investor is convinced that he knows the parameters and , and that he is right about the underlying process controlling the switching from one regime to another, or equivalently from models 1 to 2, which is also Markov, this implies that the state of the world today depends only on the state of the world in the previous period. (Barberies et al, 1998). The transition matrix is: The state of the world at time t is written as . If =1, we are in the first regime and the earnings shock in period t, is generated by model 1. Also, if = 2, we are in the second regime and the earnings shock is generated by model 2. The parameters and determine the probabilities of the transition from one state to another. Barberies et al (1998) explain that particular focus should be on small and , meaning that transitions from one state to another rarely occur. Consequently, it is assumed that +< 1. Also, is assumed to be smaller than . To make it possible for the investor the security, he/she needs to forecasts earnings into the future. (Barberies et al, 1998). Considereing the fact that earnings are generated by two different regimes as presumed by the model, the investor’s task is to try to understand which of the two regimes is currently governing earnings. After observing earnings at a particular time, the investor can use the information to make a good guess as possible about which regime he is in. (Barberies et al, 1998). Let us assume that at a particular time t, the investor has observed an earnings shock, , he calculates a probability that was generated by Model 1, using the new data to update his estimate from the previous period . Formally, . If we assume that the updating follows Bayes Rule, then in effect, if the shock to the earnings in period t+1, y­­­­­­­­t+1, is the same as the shock in earnings during the period t, yt­­­, the investor updates qt+1 from qt, using (Barberies et al, 1998). This implies that the investor puts more pressure on model 2 if he sees two consecutive shocks of the same sign. Also, if the shock in period t+1 is opposite to that in period t, then (Barberies et al, 1998). This implies that the investor puts more pressure on model 1 if he observes opposite shocks during period t and t+1. (Barberies et al, 1998). We intend to apply this model to the Nikkei Forward 255, the Nikkei average so as to enable me carry out a comparative analysis on whether there is Momentum or reversal. The model will also be applied to the Dow Jones Ind, the FTSE100 and the Hang Seng in order to test for cross country similarities and differences in overreaction (momentum) and underreaction (reversal). This will be done by analysing the implications to the model for prices in the aforementioned markets. According to Fama (1998), the BSV model is motivated by evidence from cognitive psychology of two judgment biases namely the representativeness bias of Kahneman and Tversky (1982): people give too much weight to recent patterns in the data and too little to the properties of the population that generates the data and conservatism attributed to Edwards (1968): the slow updating of models in the face of new evidence. Fama (1998) concludes that although the BSV performs well in explaining anomalies it is meant to explain, its prediction of long-term return revesal does not capture the range of long-term results observed in the literature. In a nutshell, the long-term return literature seems more consistent with the market efficiency prediction that long-term return continuation and long-term return reversal are equally likely chance results. (Fama, 1998). Although I have some skills to carry out some of the analysis in this study, most of the analysis a good understanding of the method and other required skills will be acquired in the course of the study. The time required for this study is two years and I envisage a cost of about 5,000 pounds a year. I intend to structure this dissertation as follows: Section 1 is going to discuss the problem Background, Section 2 will talk about the Objectives, Section 3 will cover the research method, data collection and data mining. Section 4 will carry out the empirical analysis and findings, while section 5 will carry out the conclusion and recommendations. BIBLIOGRAPHY Hong H., Lim T., Stein J. C (2000). Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. Journal of Finance. Vol. 1 pp 265-295. Hong H., Stein J.C. (1999). A Unified Theory of Undereaction, Momentum Trading, and Overreaction in Asset Markets. Journal of Finance. Vol. 6, pp 2143-2184 Barberies N., Shleifer A., Vishny R. (1998). A model of investor sentiment. Journal of Financial Economics Vol 49, pp 307-343. Fama, E. F. (1991) Efficient Capital Markets: II. Journal of Finance, Vol. 46 Issue 5, p1575-1617. Fama, E. F. (1998) Market Efficiency, Long-term returns, and behavioural finance. Journal of Financial Economics. Vol. 49 pp 283-306. Read More