The number of actively managed investment funds has been growing in the past decades. The reason why their numbers are growing has become a hot topic in academic research and the media. In essence, active fund managers offer the probability of achieving above market returns in exchange for an increase in fees and risk exposure. At best this probability reflects only the perceived uncertainty about the return outcome (Holton, 2004) in contrast to the definite increase in expenses and risk exposure. To emphasize, the main stream of empirical research concludes that fund managers underperform the benchmark after adjusting for transaction and trading costs (Carhart 1997, Gruber 1996). Other evidence also shows that persistence in mutual fund performance can not be explained by superior fund manager's stock-picking skills and is not indicative of future performance (Jensen 1969, Carhart 1997). Moreover, a typical investor could increase his average annual return by 67 basis points if he invested passively (French, 2008).
The efficient market hypothesis (Fama, 1970) describes the financial markets as information efficient without any margin for successful active fund managing. Over the years EMH has been rigorously tested and confirmed to a large extent by ensuing studies yet people still tend to actively invest and manage their portfolios. The following essay addresses several behavioural biases and heuristics that altogether have a meaningful explanatory power of investors' irrational actions.
The departure paradox from EMH is difficult to explain but one explanation lies within feelings and emotions. Investor behaviour is not only affected by a rational process of thinking but also by sentiments. Three prominent emotions that affect investor psychology and subsequently investment decisions are hope, overconfidence and fear (Statman, 2002). While fear induces loss aversion, hope alternatively induces a more risk-seeking behaviour. Hope combined with overconfidence, defined as the feeling that a person is above average (Moore, Kurtzberg and Fox, 1999), leads people to take excessive risks that were they in a different situation might not have taken (Kahneman and Tvensky, 1979). Active management plays on that feeling and thus people invest in having their funds actively managed hoping that they can profit by beating the market and fulfilling their aspirations for a better life or a better social status. Of course this hope rarely translates into realized gains since, as mentioned, mutual funds underperform index linked funds when transaction costs and fees are taken into account.
The mass media is a powerful influencer that creates an impression of success of the active management industry by frequent coverage of investment legends like Warren Buffet and Jorge Soros. Similarly, fund advertising builds on excellent past performance, but in reality it should not be treated as a signal of skill and abnormal returns in post-advertisement periods (Prem, Jain and Joanna Shuang Wu, 2000). Advertising is a constituent part of fund management operating costs; however, it may not be seen as such by the first-time investor. The transaction and trading costs are uneasily estimated, as active management involves an unpredictable number of trades to reach its objectives. Making an attempt to understand the complex hidden cost structures might lead the inexperienced investor to suffer from the frame dependency problem, when a "superficial detail of formulation affects the final decision" (Kahneman, 2002). Consistent with this argument, investors were found to demonstrate higher sensitivity to load fees than to operating expenses due to their better relative visibility (Barber, Odean and Zheng, 2003).
A good performance record is one of the key factors in a fund or fund manager selection process. During the decision making the investor can experience an illusion of order in the past positive returns and predict a future performance trend while exhibiting insensitivity to the reliability and the observed sample size of the data. Subconsciously, investors would be more confident in redundant data and data confirming their initial beliefs, even though redundancy statistically reduces accuracy of prediction (Tversky and Kahneman, 1974). Hence, despite the equally likely probabilities of losses or abnormal returns, the investors choose to misinterpret statistical probabilities due to representativeness bias (Levy and Benita, 2010). Fellner (2010) also found that a higher number of prior positive returns causes excessive extrapolation and hence results in an even higher investment.
The investor's tendency to diverge from rationality can also be attributed to anchoring bias, which results in under reaction to new information (Fuller, 2000). Psychologically new information creates disagreements and investors are conservative in updating their judgements in the face of new evidence (Edwards, 1968), such as a decrease in proportion of US mutual funds with positive alphas from 14.4% to 0.6% (Barras et al, 2009). On the other hand, the insufficient objective financial knowledge significantly limits the ability of a non-professional investor to digest information and analyse the available investment options efficiently (Wang , 2009).
Another reason of the outmost importance behind investing in active management funds is regret and responsibility. The two concepts are interwoven so we have to treat and explain them jointly. Regret is defined as the psychological pain one feels after a bad outcome has come to pass (Shefrin, 2002). The intensity of regret that the individual feels along with the hindsight bias (retrospective analysis of the events that transpired in the past) brings about responsibility and that is where the active fund manager comes into the scene (Kahneman and Riepe (1998), Loomes and Sugden (1982)). For example, consider two investors A and B. Investor A makes the decision by himself to invest in a stock believing that it will outperform the market while Investor B makes the same investing decision as Investor A but after he followed the advice of his manager. After some time, the price of the stock decreased substantially. Now Investor A feels regret because it is obvious in this backward-looking analysis that he made a mistake and hence feels responsible for the losses. On the other hand investor B's level of regret is lower because he can transfer the responsibility of the bad outcome to the manager that led him to take that decision. Even Harry Markowitz, the creator of the theory of effective allocation of assets in a portfolio was quoted saying about his personal allocation preferences that "My intention was to minimize my future regret. So I split my contributions fifty-fifty between bonds and equities" (Zweig, 1998).
The next reason as to why investors tend to trust their funds in mutual funds is because of the house money effect. The house money effect is the tendency people have to take greater risks when the money they take the risks with is not their own (Thaler and Johnson, 1990). This is especially true for institutional investors since most of their investments are made using "client" money instead of company money. An example of such an occasion is the Santa Clara university endowment fund. When the university's endowment fund reached its target of $300 million, its trustees (people that did not own but just managed the fund) decided to include hedge funds, venture capital and other risky asset classes in the asset mix (Shefrin, 2002). This is indicative of a behavioural issue and certainly gives some insight as to why sponsors, even knowledgeable ones, continue to invest in active funds.
Another important behavioural concept is mental accounting and the creation of reference points. People tend to create mental accounts depending on various factors such as where the income is coming from or the level of income (Shafir and Thaler, 2006). Then a reference point is created in the mental account. If the account performs better than the assigned reference point, then this is considered a success. So even if a benchmark, such as the S&P 500, outperforms the mental account, the opportunity cost lost makes no difference to the investors. Consequently for the investors the comfort of avoiding a serious market downfall is more important than capturing the extra opportunity gains forgone by not investing in index funds (Shefrin, 2002).
Tactical asset allocation has become a fundamental component of investing, despite the fact that a lot of empirical evidence run contrary to the notion of active fund management and how effective it actually is. In this essay, examination of several investors' behavioural concepts allowed to shed light on why people continue to invest in mutual funds. One such notion is that people are continuously bombarded with information on how effective mutual funds are and how affluent individuals have consistently beat the market. The cost structure of the mutual funds is difficult to decipher so inexperienced investors might perceive a mutual fund as successful before expenses are accounted for. Extrapolation bias, lack of knowledge, anchoring bias overconfidence, hope and fear also contribute to the fallacy of profitable active funds. Then we analyzed institutional investors and how the in house-money effect and mental accounting can effect even knowledgeable investors in making bad decisions. Regardless of the overwhelming evidence against active fund management, every investor is prone to a unique combination of behavioural biases resulting in subjective perceptions of risk and irrational investing behaviour.
II: Summarise and critically discuss the empirical findings of "post earnings announcement drift" anomaly in international level
Introduction
The post earnings announcement drift (henceforth PEAD) has been the most resilient and widely researched phenomenon since it was first noted over four decades ago by Ball and Brown (1968). This delay in prices assimilating earnings information poses a challenge to the semi-strong form of market efficiency which assumes efficient incorporation of new publicly available information into stock prices (Lui et al 2003). A number of explanations have been put forward to explain the PEAD phenomenon. Bernard and Thomas (1989) explained the phenomenon by two theories; 1) failure to fully adjust abnormal returns for the risk component (inadequacy of the CAPM) [1] and 2) delay in response to earnings reports either due to failure by the investors to assimilate available information and/or that costs involved limits immediate exploitation of earnings information.
Earlier academic research, [2] on this phenomenon was based on the US market where PEAD was first observed, but eventually research was extended to other markets such as UK (Lui et al 2003), Spain( Forner et al, 2009) and New Zealand (Troung,2010). The findings across markets were varied due to methodological issues, risk pricing models, behavioural models as well as country specific factors.
This essay seeks to summarise and critically discuss the findings and explanations given by researchers on different markets about the anomaly drawing particular attention to methodological issues, risk pricings models and behavioural aspects.
Methodological Issues
The way in which data is collected matters because it may lead to methodological problems and variance in results. Table 1 summarises the methodology employed in academic papers across different market. US and New Zealand papers used event time to track stock and construct portfolios, this method is subject to look-ahead bias. Cutting off values using deciles in early research design is required in event time approach and this process will could be misleading.UK and Spain studies applied a calendar time method to fix these problems and provided an easy way of implementing investment strategies.
Table 1
Earnings measurement variables are decided after the data collection. Though SUE probably is the most commonly used measurement for earning, it can be calculated in very different ways in different circumstances. The US and UK papers standardize the unexpected earnings by its prior forecast errors, this adjustment allowed comparison of firms with different volatilities. Spanish study scaled the unexpected earning with book value of equity at beginning of the year and New Zealand paper scale earning with stock price prior the announcement to address the mispricing problem. Analyst forecast based measurement is also widely used, though this approach gives an investor a more timely comparison, biased analyst forecasts is always a serious drawback. Traditional method is to track the revision of analyst forecasts over time. In contrast, the New Zealand study tracks the gap between actual earning and forecasted earnings, presenting a new way to partially solve this problem.
Studies across four countries all tried to explain the PEAD with various risk factors. Original CAPM, Fama French three factor models and conditional versions of them are commonly used to address systematic risk's impact on earnings. US study tested the significance of the overall economic influence using Chen Roll and Ross 1986 five factor model. Market macrostructure factors such as market value, firm size and book to market ratio are widely used and implied ways to track firm's specific characteristics.
As evident from the US and New Zealand papers, factors and models which are irrelevant to risk such as investors' behaviour, momentum effect and transaction costs are continuously studied by researches. In Spain the investor conservatism bias model were applied along with the overreaction and underreaction model and the gradual-information diffusion model in order to explain post earnings drift from the perspective of behaviour finance.
Although researches conducted in these four countries have some similarities, different variables, procedures, and tests were applied to the area which is important from researchers' point of view. Due to the differences in culture, institutional characteristic and market efficiency, it is hard to standardize methodology and simply claim which one provides the most accurate measurement.
Test results for risk related models and factors
Risk related models tried to regard post earning drift as compensation for risk in the efficient market. The inadequacy of CAPM and other asset pricing models is widely tested worldwide. Differences in market depth, data limitation, errors of methodology and risk misspecification can result in inconsistent outcome across countries. Moreover, results can differ with the risk model prediction. Forner (2008) found a negative relationship between CAPM beta and PEAD profits in Spain while other countries showed a positive sign. In Forner's study, arbitrage risk is positively related to the earning profits, which is contradicting to Mendenhall's (2004) conclusion based on US sample. Though results vary worldwide, most researchers conclude that the risk's impact is insignificant on return difference, and neither the systematic risk nor other risk factors are able to explain the PEAD phenomenon separately or jointly. PEAD effectively remains after the risk adjustment and in order to fully understand and explain the PEAD phenomenon, factors other than risks must be considered.
Behavioural Factors
Bernard and Thomas (1990) found that a large proportion of the drift was due to investors' adopting a naive assumption that earnings follow seasonal random walk and failing to understand the correlation between current earnings and future quarterly earnings. The claim of naivety was refuted by Bartov et al (2000) who observed that the drift is smaller in firms that were largely held by institutional investors indicating more sophisticated investors than claimed by Bernard and Thomas. However, almost all investors suffer from psychological biases regardless of whether they are individual or institutional investors.
Barberies et al (1998) posit that investors exhibit conservative behaviour and representative bias that leads to underreaction to earnings announcement. This suggests that investors seem to be conservative and do not act immediately to earnings news on the premise that the positive earnings growth is temporary and that it will eventually revert. For institutional investor, the delayed response probably can be explained by rule which put constrains on arbitrage and insider trading.
Daniel et al (1998) departing from the underreaction based explanation hypothesizes that investors experience a combination of overconfidence and self attribution bias and tend to exhibit patterns of overreaction to privately sourced information which lead to short-term momentum. On the other hand, underreaction to publicly obtained information will generate a long-term reversion in stock return. However, when this model was tested in Spain where the momentum effect is weak, there was no significant evidence showing the relationship between overconfidence and overreaction. This is because Spanish culture is regarded as less individualistic and people give more weight on public information thus exhibiting a low level of self attribution and overconfidence (Forner, 2009).
Hong et al (1999) theory on the other hand presented a significantly different theory based on interactions between heterogeneous agents namely the "newswatchers" and "momentum traders" and observed gradual diffusion of firm specific information which lead to initial underreaction and subsequent long term overreaction. Unlike in the US, the undereaction in Spain is not significant. This is due to the code law legal system and low level of investor protection, insiders have less limits to trade on the information they obtained and the price adjust quickly which weaken the momentum effect (Forner, 2009)
Conclusion
Post earnings announcement drift phenomenon was observed across different markets since 1968 and though many academic studies were conducted and lots of trading strategies were applied, the anomaly still persists. Different methodology is adapted across countries to measure the magnitude of post earning drift. Though more and more proxies are used, each measurement has its own shortcomings. Differences in market efficiency, market depth and transparency and accounting regimes may lead to biased measurement starting from the data collection stage. Risk factors models try to explain the post earnings announcement drift under efficient market hypothesis and the effectiveness of model is largely depend on market specific features. Difference in media coverage could influence the information dispersion and delay investors' response to share price. Return is also affected by country specific macroeconomic indicators, market size, regulation on insider trading and speculative activity. Even in US and UK where the markets are more efficient compared to other countries, the risk models failed to fully explain the drift. Behavioural approaches address the persistence of drift from a different perspective based on investors' sentiments, heuristics bias and limits to arbitrage. Nevertheless, investors' behaviour varies due to cultural differences and their sophistication level. Although most investors may suffer from overconfidence bias, investors in high individualistic culture may react to earning announcement differently than those from low individualistic cultures. Limits to arbitrage put constrain on investors' early exploration of earnings announcement and increase the difficulties in correcting the anomaly.
Because of the differences in market and behavioural factors, no standardize approach can be used. Therefore, strategies aimed to exploit benefit from the post earnings announcement drift should take into account countries' specific features.
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