Sloan (1996), in a determinative paper, added the accrual anomaly in the list of the market imperfections. Since then, academics have focused on the empirical investigation of the anomaly and the connection it has with other misspricing phenomena. The accrual anomaly is still at an embryonic stage and further research is needed to confirm the profitability of an accruals based strategy net of transaction costs. The current study investigates the accrual anomaly while taking into consideration a UK sample from 1991 to 2008. In addition, the predictive power of the Fama and French (1996) factors HML and SMB is being tested along with the industrial production growth, the dividend yield and the term structure of the interest rates.

Chapter 1

Introduction

Since the introduction of the random walk theory which formed the basis for the evolvement of the Efficient Market Hypothesis (EMH hereafter) proposed by Fama (1965), the financial literature has made many advances but a piece of the puzzle that is still missing is whether the EMH holds. Undoubtedly, the aforementioned debate can be considered as one of the most fruitful and fast progressing financial debates of the last two decades.

The Efficient Market Hypothesis has met many challenges regardless of which of its three forms are being investigated. However, the weak form and semi strong hypothesis have been the most controversial. A literally vast collection of academic papers discuss, explore and argue for phenomena that seem to reject that the financial markets are efficient.

The famous label of “anomaly” has taken several forms. Many well-known anomalies such as the contrarian investment, the post announcement drift, the accruals anomaly and many others are just the beginning of an endless trip. There is absolutely no doubt that many more are going to be introduced and evidence for the ability for the investors to earn abnormal returns will be documented.

Recently, academics try to expand their investigation on whether these well-documented anomalies are actually profitable due to several limitations (transaction costs etc) and whether the anomalies are connected. Many papers are exploring the connection of the anomalies with each other proposing the existence of a “principal” misspricing that is documented into several forms.

The current study tries to look into the anomaly that was initially documented by Sloan (1996) and has been labelled as the “accrual anomaly”. The accrual anomaly can be characterised as being at an embryonic stage if the basis for comparison is the amount of publications and the dimensions of the anomaly that light has been shed on.

The facts for the accrual anomaly suggest the existence of the opportunity for investors to earn abnormal returns by taking advantage of simple publicly available information. On the other hand, accruals comprising an accounting figure have been approached from different points of view with consequences visible in the results of the academic papers. Furthermore, Stark et al (2009) challenge the actual profitability of the accrual anomaly by simply taking transaction costs into consideration.

The present paper employs an accrual strategy for a sample comprising of UK firms during 1991-2008. The aim is to empirically investigate the profitability of such strategies during the whole data sample. The methodology for the calculation of accruals is largely based on the paper of Hardouvelis et al (2009). Stark et al (2009) propose that the positive excess returns of the accruals’ strategy are based on the profitability of small stock. In order to investigate the aforementioned claim, the current study employs an additional strategy by constructing intersecting portfolios based on accruals and size.

Finally, five variables are being investigating at the second part of the study for their predictive power on the excess returns of the constructed portfolios. The monumental paper of Fama and French (1996) documented an impressive performance of two constructed variables (the returns of portfolios named HML and SMB). In addition, the dividend yield of the FTSE all share index, the industrial production growth and the term structure of the interest rates will be investigated as they are considered as potential candidates for the prediction of stock returns.

Chapter 2

Literature review

2.1. Introduction

During the last century the financial world has offered many substantial advances. From the Portfolio Theory of Markowitz (1952) to the development of the Capital Asset Pricing Model of Sharpe (1964) and Lintner (1965), and from the market Efficient Market Hypothesis (hereafter EMH), developed by Fama (1965), to the recent literature that challenges both the CAPM and the EMH, they all seem to be a chain reaction. 

The financial academic world aims to give difficult but important answers on whether markets are efficient and on how investors should allocate their funds. During the last two decades, many researchers have documented that there exist strategies that challenge the claim of the supporters of the efficient and complete markets. In this chapter, the effort will be focused on reviewing the financial literature from the birth of the idea of the EMH until the recent publications that confirm, reject or challenge it.

In a determinative paper, Fama (1970) defined efficient markets and categorised them according to the type of information used by investors. Since then, the finance literature has offered a plethora of studies that aim to test or prove whether markets are indeed efficient or not. Well known anomalies such as the post announcement drift, the value-growth anomaly or the accruals anomaly have been the theme of many articles ever since.

2.2. Review of the value-growth anomaly

We consider as helpful to review the literature for the value growth-anomaly since it was one of the first anomalies to be investigated in such an extent. In addition, the research for the value-growth anomaly has yielded a largely productive debate on whether the documented returns are due to higher risk or other source of mispricing.

Basu (1970) concluded that stocks with high Earnings to Price ratio tend to outperform stocks with low E/P. Lakonishok, Shleifer and Vishny (1994) documented that stocks that appear to have low price to a fundamental (book value, earnings, dividends etc) can outperform stocks with high price to a fundamental measure of value. Lakonishok, Shleifer and Vishny (1994) initiated a productive period that aimed to settle the dispute on the EMH and investigate the causes of such “anomalies”.

Thus, the aforementioned researchers sparked the debate not only on the market efficiency hypothesis but also on what are the sources for these phenomena. Fama and French (1992) supported the idea that certain stocks outperform their counterparts due to the larger risk that the investors bear. Lakonishok, Shleifer and Vishny (1994) supported the idea that investors fail to correctly react to information that is available to them. The same idea was supported by many researchers such as Piotroski (2001). The latter also constructed a score in order to categorise stocks with high B/M that can yield positive abnormal returns (namely, the F Score). Additionally, the “market efficiency debate “drove behavioural finance to rise in popularity.

The value-growth phenomenon has yielded many articles that aim to find evidence that a profitable strategy is feasible or trace the sources of these profits but, at the same time, the main approach adopted in each study varies significantly. Asness (1997) and Daniel and Titman (1999) examine the price momentum, while Lakonishok, Sougiannis and Chan (2001) examine the impact of the value of intangible assets on security returns.

In addition, researchers have found evidence that the value-growth strategies tend to be successful worldwide, as their results suggest. To name a few, Chan, Hamao and Lakonishok (1991) focused on the Japanese market, Put and Veld (1995) based their research on France, Germany and the Netherlands and Gregory, Harris and Michou (2001) examined the UK stock market.

It is worth mentioning that solely the evidence of such profitable strategies could be sufficient to draw the attention of practitioners, but academics are additionally interested in exploring the main cause of these arising opportunities as well as the relationship between the aforementioned phenomena (namely, the value growth, post announcement drift and the accrual anomaly). In general, two schools of thought have been developed: the one that supports the risk based explanation or, in other words, that stocks yield higher returns simply because they are riskier, and the one that supports that investors fail to recognise the correct signs included in the available information.

2.3. The accruals anomaly

2.3.1. Introduction of the accrual anomaly.

Sloan (1996) documented that firms with high (low) accruals tend to earn negative (positive) returns in the following year. Based on this strategy, a profitable portfolio that has a long position on stocks with low accruals and short position on stocks with high accruals yields approximately 10% abnormal returns. According to Sloan (1996) investors tend to overreact to information on current earnings. Sloan’s (1996) seminar paper has been characterised as a productive work that initiated an interesting to follow debate during the last decade. It is worth noting that even the very recent literature on the accrual anomaly has not reached reconciling conclusion about the main causes of this particular phenomenon and about whether a trading strategy (net of transaction costs) based solely on the mispricing of accruals can be systematically profitable.

At this point it is worth mentioning that the accruals have been found to be statistically significant and negative to predict future stock returns. On the other hand, there are papers that examine the accruals and its relations with the aggregate market. A simple example is the paper published by Hirshleifer, Hou and Teoh (2007), who aim to identify the relation of the accruals, if any, with the aggregate stock market. Their findings support that while the operating accruals have been found to be a statistical significant and a negative predictor of the stock returns, the relation with the market portfolio is strong and positive. They support that the sign of the accruals coefficient varies from industry to industry reaching a peek when the High Tech industry is taken into account (1.15), and taking a negative value for the Communication and Beer/Liquor industry.

2.3.2 Evidence for the international presence of the phenomenon.

Researchers that investigated the accruals anomaly followed different approaches. At this point, it is worth noting that the evidence shows the accrual anomaly (although it was first found to be present in the US market) to exist worldwide. Leippold and Lohre (2008) examine the accrual anomaly within an international framework. The researchers document that the accrual anomaly is a fact for a plethora of markets.

The contribution of the paper though, is the large and “complete” number of tests used, so that the possibility of pure randomness would be eliminated. Although, similar tests showed that momentum strategies can be profitable, recent methodologies used by the researchers and proposed by Romano and Wolf (2005) and Romano, Shaikh and Wolf (2008), suggest that the accruals anomaly can be partially “random”.

It is noteworthy that the additional tests make the “anomaly” to fade out for almost all the samples apart from the markets of US, Australia and Denmark. Kaserer and Klingler (2008) examine how the over-reaction of the accrual information is connected with the accounting standards applied. The researchers constructed their sample by solely German firms and their findings document that the anomaly is present in Germany too. We should mention at this point that, interestingly, prior to 2000, that is prior to the adoption of the international accounting standards by Germany, the evidence did not support the existence of the accrual anomaly. However, during 2000-2002, Kaserer and Klingler (2008) found that the market overreacted to accrual information. Hence, the authors support the idea that an additional cause of the anomaly is the lack of legal mechanisms to enforce the preparation of the financial statements according to the international accounting standards which might gave the opportunity to the firms to “manipulate” their earnings.

Another paper that focuses on the worldwide presence of the accruals mispricing is that of Rajgopal and Venkatachalam (2007). Rajgopal and Venkatachalam examined a total of 19 markets and found that the particular market anomaly exists in Australia, UK, Canada and the US. The authors’ primal goal was to identify the key drivers that can distinguish the markets where the anomaly was documented. Their evidence supports the idea that an accrual strategy is favoured in countries where there is a common law tradition, an extensive accrual accounting and a low concentration of firms’ ownership combined with weak shareholders’ rights.

LaFond (2005) also considers the existence of the phenomenon within a global framework. The author’s findings support the notion that the accrual anomaly is present worldwide. In addition, LaFond argues that there is not a unique driving factor responsible for the phenomenon across the markets. It is worth noting that LaFond (2005) documented that this particular market imperfection is present in markets with diverse methodology of accrual accounting. Findings are against the idea that the accrual anomaly has any relation with the level of the shareholders protection or a common law tradition, as suggested by Rajgopal and Venkatachalam (2007). Finally, the author suggests that, if any, it is not the different method of accrual accounting (measurement issues) that favours or eliminates the accrual anomaly, but the accrual accounting itself.

2.3.3.Further Evidence for the roots of the accruals anomaly.

Additionally, papers such as those of Thomas and Zang (2002) or Hribar (2000) decompose accruals into changes in different items (such as inventory, accounts payable etc). The findings catholically suggest that extreme changes in inventory affect returns the most. On the other hand, many articles connect the accruals with information used by investors, such as the behaviour of insiders or analysts, as the latter can be considered a major signal to the investors for a potential manipulation of the firms’ figures.

In particular, Beneish and Vargus (2002) documented that firms with high accruals and significant insider selling have substantial negative returns. Bradshaw (2001) and Barth and Hutton (2001) examine the analysts’ reports and their relation with the accruals anomaly. Their findings support that the analysts’ forecasting error tends to be larger for firms with high accruals, while analysts do not revise their forecasts when new information for accruals is available.

Gu and Jain (2006) decompose accruals into changes in inventory, changes in accounts receivable and payable and depreciation expenses and try to identify the impact of the individual components to the anomaly. Consistent with Sloan (1996), Gu and Jain (2006) document that the accrual anomaly exists at the components level. The findings are important since Desai et al (2004) supported the connection of the accrual anomaly with a single variable (cash flows from operations). The researchers suggest that the results yielded by Desai et al (2004) were highly dependent on the methodology used and thus, suggested that the accruals anomaly is “alive and well”.

Moreover, other articles try to confirm whether the anomaly is mainly caused by the wrong interpretation of the information contained in accruals. Ali et al. (2000), investigate whether the naïve investors’ hypothesis holds. Following the methodology introduced by Hand (1990) and Walther (1997), they found that for smaller firms, which are more likely to be followed by sophisticated investors, the relation between accruals and negative future returns is weaker compared to larger firms, which are followed by many analysts. Therefore, the researchers suggest that, if anything, the naïve investors’ hypothesis does not hold. In contrast to other market anomalies where findings suggest that the naïve investors hypothesis holds, the accruals anomaly is suggested as unique.

Shi and Zhang (2007) investigate the earnings fixation hypothesis suggesting that the accruals anomaly is based on the investors “fixation” or “obsession” on earnings. Their primal hypothesis is that if investors are highly based on the reports about earnings and misprice the value-relevant earnings, then the returns should be dependent not only on the accruals but also on how the stock’s price changes according to reported earnings. The researchers’ hypothesis is confirmed and finding support that an accrual strategy for firms whose stocks’ price highly fluctuates according to earnings yields a 37% annual return. Sawicki and Shrestha (2009) aim to examine two possible explanations for the accruals anomaly. Sloan (1996) proposed the fixation theory under which investors fixate on earnings and thus overvalue or undervalue information for accruals.

Kothari et al. (2006) proposed the “agency theory of overvalued equity” according to which managers of overvalued firms try to prolong the period of this overvaluation which causes accruals to increase. The paper uses the insider trading and other firm characteristics and tries to compare and contrast the two major explanations. Evidence produces bd Sawicki and Shrestha (2009) support the Kothari et al. (2006) explanation for the accrual anomaly. In a relatively different in motif paper, Wu and Zhang (2008) examine the role that the discount rates play in the accrual anomaly.

They argue that if anything, the anomaly is not caused by irrationality from the investors’side but by the rationality of firms as it is proposed by the q-theory of investment. They argue that since the discount rates fall and more projects become profitable (which makes accruals to increase) future stock returns should decline. In other words, if the capital investment correctly adjusts to the current discount rates, the accruals should be negatively correlated with the future returns and positively correlated with the current returns. The evidence of Wu and Zhang (2008) support that the accruals are negatively correlated with the future stock returns but the contribution of the paper is in that they document that current stock returns are positively correlated with the accruals.

2.3.4. The relation of the accrual anomaly with other market imperfections.

Many papers examine the relation between the accruals anomaly and other well-known anomalies such as the post announcement drift or the value-growth phenomenon. Desai et al. (2002), suggest that the “value-growth” anomaly and the accruals anomaly basically interact and conclude that the ¨accruals strategy and the C/P reflect the same underlying phenomena”. Collins and Hribar (2000) suggest that there in no link between the accruals anomaly and the “PAD”, while Fairfield et al. (2001) support that the accruals anomaly is a sub-category of an anomaly caused by the mistaken interpretation of the information about growth by the investors.

Cheng and Thomas (2006) examine the claim that the accrual anomaly is a part of a broader anomaly (and more specifically, the value-glamour anomaly). Prior literature suggested that the operating cash flows to price ratio subordinates accruals in explaining future stock returns (Deshai et al (2004)). Their evidence suggests that the Operating CF to price ratio does not subsume neither abnormal nor total accruals in future announcement returns. This particular result does not confirm the claim that the accrual anomaly is a part of a broad value-glamour anomaly.

Atwood and Xie (2005) focus on the relation of the accrual anomaly and the mispricing of the special items first documented by Burgstahler, Jiambalvo and Shevlin (2002). Their hypothesis that the two phenomena are highly related is confirmed since the researchers found that special items and accruals are positively correlated. Additionally, further tests yielded results that suggest that the two imperfections are not distinct, while the special items have an impact on how the market misprices the accruals.

Louis and Sun (2008) aim to assess the relation between the abnormal accrual anomaly and the post earnings announcement drift anomaly. The authors hypothesize that both anomalies are related to the management of the earnings and thus, they aim to find whether the two are closely connected. The findings are consistent with the primal hypothesis, as they found that “firms with large positive change of earnings that were least likely to have manipulated earning downwards” did not suffer from PEAD, while the same result was yielded for firms that had large negative change of earnings that were least likely to have managed their earnings upwards.

As supported by many researchers the value-growth anomaly and accruals anomaly might be closely related or they might even be caused by the similar or even identical roots. Fama and French (1996) support that the book to market factor captures the risk of default, while Khan (2008) suggests that in a similar pattern firms with low accruals have a larger possibility to bankrupt. Therefore, many researchers try to connect the two phenomena or to answer whether a strategy based on the accruals can offer more than what the value growth strategy offers.

Hardouvelis, Papanastopoulos, Thomakos and Wang (2009) connect the two anomalies by assessing the profitability of interacting portfolios based on the accruals and value-growth measures. Their findings support that positive returns are obtainable and magnified when a long position is held for a portfolio with low accruals while combined with stocks that are characterised as high market to book. The difference of a risked-based explanation or an imperfection of the markets is considered to be a major debate, as it can challenge the market efficiency hypothesis.

Many researchers, such as Fama and French (1996) noted that any potential profitable strategy is simply due to the higher risk that the investors have to bear by holding such portfolios. In a similar way, the profitable accruals strategies are considered as a compensation for a higher risk. Stocks that yield larger returns are compared or labelled as stocks of firms that are close to a financial distress. Khan (2000) aims to confirm or reject the risk-based explanation of the accruals anomaly.

The researcher uses the ICAPM in order to test if the risk captured by the model can explain the anomaly first documented by Sloan (1996). It is worth noting that the descriptive statistics results for the sample used showed that firms that had low accruals also had high bankruptcy risk. The contribution of the paper is that, by proposing a four factor model enhanced by recent asset pricing advances, it showed that a great portion of the mispricing that results in the accrual anomaly can be explained within a risk-based framework. Furthermore, Jeffrey Ng (2005) examines the risk based explanation for the accrual anomaly which proposes that accruals include information for financial distress.

As proposed by many papers, the accrual anomaly is simply based on the fact that investors bare more risk and thus low accrual firms have positive abnormal returns. The researcher tries to examine how and if the abnormal returns of a portfolio which is short on low accruals stocks and long on high accrual firms changes when controlling for distress risk. Evidence supports that at least a part of the abnormal returns are a compensation for bearing additional risk. Finally, the results support that the big portion of the high abnormal returns of the accrual strategy used in the particular paper is due to stocks that have high distress risk.

2.3.5. The accruals anomaly and its relation with firms’ characteristics.

A noteworthy part of the academic literature examines the existence of some key characteristics or drivers that are highly correlated with the accruals anomaly. Many researchers have published papers that aim to identify the impact of firm characteristics such as the size of the firm, characteristics that belong to the broader environment of the firms such as the accounting standards or the power of the minority shareholders. Zhang (2007) investigates whether the accrual anomaly varies cross-sectionally while being related with firms’ specific characteristics. The researcher primarily aims to explain which the main reason for the accrual anomaly is.

As Zhang (2007) mentions, Sloan (1996) attributes the accrual anomaly to the overestimation of the persistence of accruals by investors, while Fairfield (2003) argues that the accrual anomaly is a “special case of a wider anomaly based on growth”. The evidence supports the researcher’s hypothesis that characteristics such as the covariance of the employee growth with the accruals have an impact on the future stock returns. Finally, Zhang (2007) documents that that accruals co-vary with investment in fixed assets and external financing.

Louis, Robinson and Sbaraglia (2006) examine whether the non-disclosure of accruals information can have an impact on the accruals anomaly. The researchers, dividing their sample into firms that disclose accruals information on the earnings announcement and firms that do not, investigate whether there exists accruals’ mispricing. The evidence supports that for firms that disclose accruals information, the market manages to correctly understand the discretionary part of the change of the earnings.

On the contrary, firms that do not disclose accruals information are found to experience “a correction” on their stock price. Chambers and Payne’s (2008) primal aim is to examine the relation of the accrual anomaly and the auditing quality. The researchers’ hypothesis is that the accruals mispricing is related with the quality of auditing. Additionally, their findings support that the stock prices do not reflect the accruals persistence characterising the lower-quality audit firms. Finally, their empirical work finds that the returns are greater for the lower-quality audit portfolio of firms.

Palmon, Sudit and Yezegel (2008) examine the relation of the accruals mispricing and the company size. Evidence shows that company size affects the returns and, as the researchers documented, the negative abnormal returns are mostly due to larger firms while the positive abnormal returns come from the relatively small firms. Particularly, as the strategy with the highest profits they found the one that had a short position in the largest-top-accrual decile and a long position in the smallest-low-accrual decile.

Bjojraj, Sengupta and Zhang (2009) examine the introduction of the Sarbanes-Oxley Act and the FAS 146 and how these two changes affected the accrual anomaly. FAS 146 (liabilities are recognized only when they are incurred) reduced the company’s ability to “manipulate” earnings while the SOX aims to enhance the credibility of the financial statements. The evidence recognises a change on how the market conceives information about restructurings charges. The authors propose that a possible explanation is that before the introduction of SOX and the FAS 146, the market was reluctant due to the ability of the firms to manage earnings. Finally, Bjojraj, Sengupta and Zhang (2009) document that post to the FAS 146 and the SOX act, low accrual portfolios do not generate positive abnormal returns.

2.4. The applications of the accruals phenomenon and reasons why it is not arbitraged away.

The importance of the analysis of the anomalies is substantial for two reasons. Firstly, the profitability of a costless strategy challenges the EMH, especially if the strategy is based on bearing no additional risk. Secondly, managers’ incentives to manipulate the financial statements and consequently the accruals would be obvious if a profitable strategy based on such widely available information existed. Chen and Cheng (2002) find that the managers’ incentive to record abnormal accruals is highly correlated with the accrual anomaly. The hypothesis of the researchers, which their findings support, was that the investors fail to detect when the managers aim to record abnormal accruals and that may contribute to the accruals anomaly.

Richardson’s (2000) main objective is to examine whether the information contained in the accruals is utilized by short sellers. As the researcher mentions, previous articles such as that of Teoh and Wong (1999) found that sell side analysts were unable to correctly “exploit” the information contained in accruals for future returns. Richardson suggests that short sellers are considered as sophisticated enough to utilize the accruals information. Findings confirm previous work, such as that of Sloan (2000), who suggests that even short sellers do not correctly utilize the information contained into accruals.

Ali, Chen, Yao and Yu (2007) examine whether and how equity funds benefit from the accrual anomaly by taking long position into low accruals firms. The researchers aim to identify how exposed are the equity firms to such a well known anomaly and what characteristics these funds share. By constructing a measure called “accruals investing measure” (AIM), they try to document the portion of the low accruals firms into the actively managed funds. The evidence shows that generally funds are not widely exposed to low accruals firms but when they do so, they have an average of 2.83% annual return. It is worth noting that the annual return is net of transaction costs.

Finally, the side-effects of high volatility in returns and in fund flows of the equity funds that are partially based on the accrual anomaly might be the reason behind the reluctance of the managers. Soares and Stark (2009) used UK firms to test whether a profitable accrual strategy is feasible net of transactions costs. Their findings support that indeed the accrual anomaly is present in the UK market. The authors suggest that for such a strategy to be profitable, someone is required to trade on firms with small market capitalization. They also suggest that although the accruals’ mispricing seems to exist also in the UK, the transaction costs limit the profits to such an extent that the accrual anomaly could be difficult characterised as a challenge to the semi strong form of the efficient market hypothesis.

Finally, we should not neglect to mention two papers that discourse on why the markets do not simply correct the accruals anomaly. According to the classical theory, markets are so imperfect that can produce the incentive to the market to correct the “anomalies” at any point of time. Mashruwala, Rajgopal and Shevlin (2006) examined the transactions costs and the idiosyncratic risk as possible reasons of why the accrual anomaly is not arbitraged away. The researchers aimed to investigate why the market does not correct the anomaly, but also to identify whether the low accruals firms are riskier. The paper poses the question of what stops the informed investors from taking long positions into profitable stocks according to the accrual anomaly so that they can arbitrage it away. The paper examines the practical difficulty of finding substitutes so that the risk can be minimized and its relation with the accrual anomaly. Additionally, the paper investigates the transaction costs and findings support that according to the accrual anomaly, the profitable stocks tend to be the ones with low stock prices and low trading volume.

Lev and Nissim (2004) focus on the persistence of the accrual anomaly. They argue that the fact that the anomaly is present is maybe due to the characteristics that the profitable stocks (according to the accrual anomaly) share. Extreme accrual firms tend to have low stock prices (larger transaction costs) and low book to market which makes them unattractive to investors. They argue that the former drives the individual investors to avoid the low accrual firms, while the latter explains why institutional investors do not pick up low accrual firms into their portfolios. Lev and Nissim (2004) suggest that these two characteristics are main cause of why the market does not correct the anomaly.

2.5. Conclusions

Since Fama(1965) introduced the efficient market hypothesis, the financial literature has offered a rich inheritance which discourses potential market imperfections. The last decades, many phenomena such as the value-growth anomaly, the post earnings announcement drift or the accruals anomaly have played a key role in the financial literature. Many papers suggest that the aforementioned “paradoxes” can be rationally explained within a risk-based framework. On the other hand, many researchers have documented the irrationality of the investors and the determinant role that it plays on challenging the EMH.

Sloan (1996) was the first to document the accruals anomaly. According to Sloan (1996) and a growing literature since then, low accrual firms tend to outperform companies that have high accruals. The academics have not yet concurred on the roots of the accruals anomaly bit it worth noting that as the case with the value-growth anomaly, researchers propose the risk-based explanation and the underweight or overweight of the information for the accruals by investors. It is worth noting that a lot of papers try to identify common origins between the imperfections mentioned.

Hardouvelis et al(2008) ,which is the paper at which the current dissertation is based on, provide evidence for the close relation of the value-growth and the accruals anomaly. In the UK, the evidence suggest that a profitable strategy based on information for accruals can be achieved although some researchers object on how challenging these strategies can be for the EMH due to the transactions costs. In any case, further research needs to be conducted so that the financial academic world can reach a decision on how important this phenomenon is what are its main causes.

Chapter 3

Research design

3.1 Introduction

Sloan (1996) showed that the market does not correctly price the accrual components of earnings. Since then, researchers have been empirically testing variations of the profitability of the strategies that are based on accruals. As mentioned in the literature review, academics focus on answering whether the accrual anomaly is a part of wider misspricing or whether it consists of an additional challenge for the efficiency of the market (EMH).

Hardouvelis et al (2009) attempt to connect the accruals anomaly with the value-growth phenomenon and thus the formation of the portfolios is based initially on accruals. Next, Hardouvelis et al proceed on a two ways classification according to accruals and the Book to Market ratio. They hypothesize that if the misspricing captured by accruals is different from the Value Growth anomaly, then a two ways classification should increase the profitability of the “hedge portfolio”.

This chapter focuses on presenting the data sample that is used in the present paper and the research methodology employed to empirically test the profitability of an accruals based strategy. Moreover, the chapter is divided into two sub-parts. The first sub-part will presents the data used, while the second sub-part describes and explains the research approach that the present paper is based on.

The present study employs the accrual calculation method used in Hardouvelis et al (2009), while implementing an entirely different approach to evaluate the profitability of the “accrual strategy”. The research approach focuses on creating hedge portfolios based on annual figures and the market capitalisation of the firms. Firstly, accruals are computed and the stocks are assigned to one of the ten equally sized portfolios while the construction of intersecting portfolios based on accruals and market capitalisation follows.

The intersecting portfolios aim to answer whether the profitability of the strategy is largely due to small stocks. The current study aims to answer whether abnormal returns are feasible and whether the consideration of the size in the formation of the portfolios improves the profitability. Finally, the time series of cumulative abnormal returns are calculated and the excess return of the portfolios are regressed on independent variables such as the Fama and French factors (i.e HML and SMB), the industrial production, the dividend yield and the term structure in order to assess the predictive power of the variables.

The selection of the aforementioned variables is based on the following two criteria: firstly, there is a plethora of recent papers that support the predictive power of the industrial production and the dividend yield. Secondly, the construction of the HML and SMB mimicking portfolios is based on US data. The results of the regressions will allow the evaluation of the Fama and French factors on UK data and the illustration of the predictive power of the rest of the chosen variables on the accruals’ constructed portfolios and the hedge portfolios.

3.2 Data

The present sub-part presents the data range used. The sources from which the data were extracted, the data range and the overall set of data will be analysed. The tests that were conducted for the present paper used a data sample that initially consisted of 6323 UK firms whose both financial data and monthly returns were available on the “Datastream” database for the 1989-2008 (inclusive) period. Due to the “June approach” that was employed for the formation of the portfolios, each year’s financial data are used for the “hedge” portfolios of the next fiscal year. The approach for the construction of the final sample can be summarised as follows:

Each year all the firms whose financial data were not available (one or more items) were excluded from the portfolio formation procedure. The annual availability of the accounting data and the rebalancing of the portfolios each July made the sample dynamic, both in terms of the number of stocks that were included each year and in terms of the type of stocks that were included.

In addition, all the firms that can be characterised as belonging to the financial sector were excluded as well. Fama and French (2002) suggested that the financial services firms suffer from high leverage, which can bias the information for distress that high leverage for non-financial firms convey. In addition, financial firms are proposed by researchers to be excluded as it is from hard to impossible to distinguish the operating from the investing activities. It is worth noting that the sample includes delisted and dead companies to avoid possible survivorship biases.

3.2.1 Accounting Data

The annual financial data extracted from Datastream that the present paper used for each firm included: 

Total Assets defined as the “the sum of total current assets, long term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets”[1].

Total Liabilities “represent all short and long term obligations expected to be satisfied by the company and including but not restricted to current liabilities, long term debt, deferred taxes, deferred income and other liabilities”[2]

Cash and Equivalents defined as “ cash and short term investment”[3]

Short Term Debt defined as the portion of debt payable within one year and including but not restricted to current portion of long term debt, notes payable, and bank overdrafts.[4]

Long Term Debt representing “all interest bearing financial obligations, excluding amounts due within one financial year”[5]. Long term Debt includes but is not restricted to mortgages, bonds, long term notes, long term bills Medium Term Loans.

Market Value of the firms

The above criteria employed to our sample yielded the data presented in the following Graph (Graph 3.1).

Graph 3.1. Number of Firms included each year.

At this point, it is worth illustrating the industry breakdown of the sample. The industry breakdown for the accrual-based portfolios is presented in Table 3.1. The industry breakdown was calculated as the average percentage of the firms of each industry in the seventeen years of the data range that the current study considers.

It is worth noting, that the results for the two ways classification do not differ. The table depicts the low frequency of each industry in the sample and the large variety of industries that the sample covers in order to exclude the possibility of certain industry level characteristics that would bias the results.

Table 3.1.Industry Breakdown for portfolios formed based on accruals.

The calculation of the average presence of each industry in the sample was based on accruals sorted portfolios for the period 1991-2008. The result is the simple average of the industry breakdown of the seventeen years.

3.2.2 Financial data and Macroeconomic variables

In addition, the data presented below were also extracted from the “Datastream” database

Monthly returns for the FTSE all share index

Monthly returns for the three month UK treasury Bill which is used as a proxy for the risk free rate

Data for the UK industrial production

Data for the returns of the two risk factors, SMB and HML.

The dividend yield of the FTSE All Share for the UK

Monthly data for the UK long term bond

Term Structure calculated as the difference of the Long Term UK bond minus the three month UK T-Bill

3.3 Research Methodology

The second sub-chapter will analyse the research methodology implemented to empirically test the accrual anomaly as initially documented by Sloan (1996). The present paper employs a two dimensions approach. Firstly, an investigation of the profitability of the strategy that is based on the formation of portfolios according to accruals and size is conducted. Secondly, we examine the explanatory power of variables such as HML, SMB, the Term structure, the dividend yield of the FTSE All Share Index and industrial production have on the specific portfolios.

3.3.1 Construction of Portfolios based on accruals

The first part focuses on the investigation of the returns of the portfolios that are constructed based on accruals and tests whether a profitable strategy is indeed feasible when holding a long position on low accruals firms and short selling high accruals stocks. As already mentioned, the sample covers the period from 1991 to 2008. As time series analysis calls, continuously compound (logarithmic) monthly returns has been adopted throughout the paper and the returns were calculated as:

(1).

Additionally, the construction of the portfolios was based on two separate methodologies:

Formation of portfolios based on accruals

Formation of intersecting portfolios based on size and accruals

Initially, Healy (1985) defined accruals as “the change in net working capital less depreciation expense”[6] a definition which Sloan (1996) and Desai et al (2004) followed. Recently, Hirshleifer et al (2004) suggested that the formation of portfolios based on the growth of Net Operating Assets reflects greater mispricing as it captures “all cumulative past changes between accounting profitability and cash profitability”. Richardson et al (2005) was the first article to include non current operating assets and the empirical results yielded larger abnormal returns for the constructed hedge portfolio. In this paper, the method proposed by Richardson et (2005) followed by Hardouvelis et al (2009) is employed. The Net operating assets are calculated as :

(2)

and accruals are computed as:

(3)

Where :

NOA= Net Operating Assets

TA=Total Assets

C=Cash and Equivalent

TL=Total Liabilities

STD=Short Term Debt

LTD=Long Term Debt

As already mentioned, the portfolios are based on solely the accruals measures and on the two classification based on accruals and size.

Sloan (1996) constructed portfolios according to the accruals each year starting at the end of each June, holding the portfolios for one year and then rebalancing the portfolios according to the accruals announced at the next financial year. It is widely known, that UK firms have by law a six month period to publicly announce their financial statements beginning the end of the financial year.

Therefore, a feasible strategy should consider the availability of the data that the formation of the portfolios should be based on. Additionally, this particular approach provides the market with the opportunity to adjust its expectations for firms’ performance according to current data. For this reason, in this paper the “June approach” is employed.

Each June, the accruals for the firms are calculated and each firm is assigned to one of the ten value weighted portfolios from July to June of the next year. For the next financial year, the portfolios are rebalanced meaning that accruals are again calculated and stocks are assigned to new portfolios based on their accruals. The data for the monthly stock returns (extracted by Datastream) are used to calculate the compound return. The returns of the portfolios are weighted with the value of the firms in order to account for the size effect. At this point, we should note that the firms’ accruals are scaled each year by the average of total assets of the beginning and the end year.

As in many preceding papers, the deciles are from low accruals to high which means that the first decile contains the firms with the lowest accruals, while the decile ten contains the firms with the highest accruals. The following table (Table 3.2) illustrates the summary statistics for the accruals of the portfolios across the entire data sample.

Table 3.2. Illustration of the descriptive statistics of accruals for each portfolio for the period 1991-2008.Descriptive statistics for the accruals for the period 1991-2008.Std stands for standard deviation while SumSqDev stands for the sum of squared deviations

The above table illustrates that firms that appeared to have negative accruals were also included in the sample. In addition, Table 2 depicts that the two extreme deciles “suffer” from substantially higher standard deviation than the rest of the deciles.

3.3.2 Intersecting portfolios based on accruals and size

The two way classification of the portfolios was based on the calculated accruals of the firms that had available financial data and returns for the specific year and also on the size of the firm. As already mentioned in the literature review, the positive abnormal returns of deciles comprised by stocks of firms with lower accruals is largely a characteristic of the small stocks. In order to confirm or reject the latter observation, the present paper empirically tests whether intersecting portfolios based on size and accruals outperform a strategy which is based solely on accruals.

For the intersecting portfolios, stocks were first sorted according to accruals from lower to higher. Three deciles were created assigning the firms with the lowest accruals at the lowest decile and the firms with the highest accruals at the highest decile.

Following, each of the three deciles was sorted according to size and each decile was divided in three additional deciles from smaller size to higher size. Particularly, decile one consists of the small firms that have low accruals while the ninth decile contains the largest firms of the highest accruals decile. As in the one way portfolio classification, continuously compounded monthly returns were calculated for each stock, while the return of the portfolio was weighted by the value of the firms.

The table that follows (Table 3.3) summarises the descriptive statistics for the accruals of the nine deciles based on the two way classification.

Table 3.3. Illustration of the descriptive statistics of accruals for each portfolio based on the two ways classification for the period 1991-2008.Std stands for standard deviation, while SumSqDev stands for the sum of squared deviations and Sample Var stands for Sample Variance 

In addition, each year the portfolios were rebalanced according to the aforementioned two ways classification and the hedge portfolio was based on the idea of holding a long position on the decile one while short-selling decile nine. The value weighted returns of the portfolios were calculated as:

(4) where:

stands for the value weighted return of the portfolio at year

denotes the return of the firm at year

stands for the Market value of the stock at year, and

stands for the Market value of the portfolio at year

In order to decide on the profitability of the strategies that are based on the formation of the portfolios according to accruals and according to accruals and size, the cumulative abnormal returns were calculated. The calculation of the cumulative abnormal return was :

Where:

stands for the cumulative abnormal return of the portfolio at the year

stands for the value weighted return of the portfolio at the year as described at equation (4)

Then, a time series of the monthly for each decile is produced. The profitability of the strategy is defined through the annualisation of the and the construction of two hedge portfolios. Firstly, for the two extreme portfolios and secondly, for the two lowest deciles minus the two highest deciles .Finally, the average annual abnormal return is expressed. The aforementioned procedure is repeated for both the one-way and the two-ways portfolio classification in order to assess the improvement of the profitability of the accrual-size strategy over the accrual strategy.

The time series of the cumulative abnormal returns (in excess of the risk free rate) for each portfolio and for the hedge portfolios will be regressed on the state variables mentioned further above (i.e the IP growth, the dividend yield and the term structure) and the Fama and French factors.

The HML and SMB are constructed as follows.[7] For the methodology that is used, each end of June stocks are divided into two parts according to their size forming the small (S) and the “big” (B) portfolio. In addition, the stocks form three portfolios based on their book-to-market based on a 30%-40%-30% allocation for the high (H), medium(M) and low portfolio(L). The intersecting portfolios of the aforementioned allocations form six portfolios. Each year the HML factor is defined as the difference of the average of the two high book-to-market portfolios and the average of the two low book-to-market portfolios. Similarly, the SMB factor is constructed for each month as the difference of the average of the three small portfolios and the average of the three portfolios consisting of “big” stocks.

3.3.3 Regressions of the portfolios’ returns on the selection of variables.

As already stated above, six specific variables were chosen in order to be tested in terms of their predictive power on the portfolios formed according to accruals and according to accruals and size. The variables used were:

Monthly Returns of the “high minus low” portfolio (HML)

Monthly Returns of the “small minus big” portfolio (SMB)

Monthly data for the Dividend Yield of the “FTSE All Share”

Term structure defined as the difference between the Long Term UK government bond and the three month UK government Bill

The monthly excess return of the “FTSE all “ over the risk free rate (the three month UK treasury bill is used to proxy for the latter), and

Monthly data for the growth of the Industrial Production

More specifically, the monthly industrial production growth was calculated as:

where :

is the industrial production growth at time

is the level of industrial production at year .

To begin with, it is worth noting that the predictive power of the variables was tested for both the accruals’ sorted portfolios and for the portfolios sorted based on both accruals and size.

For the one way classification, the independent variables were the time series of the cumulative abnormal returns of the ten deciles plus the two hedge portfolios over the risk free rate. The two hedge portfolios were defined as the :

The Lowest minus the Highest, and

The First two deciles minus the two lowest deciles

For the two way classification, as described above, the independent variables were the time series of the cumulative abnormal returns on the nine deciles plus the two hedge deciles over the risk free rate. Again the two hedge portfolios were calculated as the:

The first minus the ninth decile, and

The first two deciles minus the two lowest

Each decile plus the two hedge portfolio for each formation method yielded a time series of seventeen years. These times series were regressed on a variable or a set of variables each time. First, the Simple Capital Asset Pricing Model (CAPM) was tested in order to assess the predictive power of the market premium on the sample. It is worth noting that the sample included periods of severe market conditions, namely the millennium dot.com bubble and the recent (and current) major economic recession. Thus, the first model to be tested for its forecasting ability was the CAPM . The formula used was:

, where :

the left part of the equation is the excess return of the portfolios at year over the risk free rate

is the intercept in order to test whether the linearity of the model holds

is the beta of the portfolio or a measure of the sensitivity of the portfolio to the fluctuations of the market

is the market premium at time

Secondly, the regression of the portfolios, including the two hedge portfolios, were ran on the Fama and French (1996) three factor model as described by the equation below:

The independent variable of the above equation is the cumulative abnormal return of the portfolios over the risk free rate, while:

HML is the return of portfolio ”High minus Low” based on the book to market

SMB is the return of the portfolio “Small minus Big” based on the market value

is the market premium at time

is the intercept in order to test whether the linearity of the model holds

is the slope of the portfolio

The next step was to test the predictive power of the model that consists solely of the growth of the industrial production growth, the dividend yield and the term structure. Thus, regressions for each decile for both accruals’ sorted and accruals-size sorted portfolios were ran as described by:

, where :

stands for the logarithmic growth of the industrial production

stands for the dividend yield of the FTSE All share index.

is the difference of the long term UK bond minus the three month UK Bill

Next, the study focused on whether the explanatory power of the models can be improved if we use the variables combined. To be more precise, regressions of the Capital Asset Pricing model as well as the three factor Fama and French model plus the three state variables (dividend yield, industrial production growth and term structure) were estimated and the results are presented in the next chapter. In detail the two final models used were as follows:

3.4 Synopsis of the Data and Methodology and Contribution of the study

Initially, it was Sloan (1996) who documented the accruals anomaly but, as Hardouvelis et al (2009) suggested, Sloan did not account for non current operating assets. Recent papers assert that the latter proposed method for the calculations of the accrual anomaly is more precise and able to capture an even greater misspricing. In the present study, the method employed by Hardouvelis et al (2009) is employed to test the profitability of the strategies that are based on publicly available information for accruals.

The accounting data as well as the returns on the market and the risk free rate described above were extracted from Datastream. After excluding firms that did not have one or more items available, around 1200 to 1500 firms were used to empirically answer the study questions.

The research approach can be summarised into the formation of portfolios according to accruals and according to size and accruals and into the testing of the predictive power of a set of variables on the deciles formed. Certain criteria were applied to ensure that the sample would be not be biased. Therefore, dead and delisted firms were included. In addition, all financial firms were excluded for reasons that are described in the main body of the current chapter.

Regarding the preparation of the construction of the portfolios, the June approach was employed and thus the portfolios were constructed based on annual accounting data each july. Under this method, the strategy is made feasible since the UK laws allow firms to publicly announce their statements up to six months after the end of each financial year.

Recent papers propose that the accruals anomaly is largely due to small stocks. Under this assertion, the construction of the portfolios is based both on solely accruals figures and (intersecting portfolios) on size and accrual. The basic hypothesis is that the hedge portfolio constructed by the two extreme deciles for the second method (intersecting) should outperform the one way classified hedge portfolio.

Fama and French (1996), in a very determinative paper, documented high forecasting ability of the factors named HML and SMB. In addition, the recent financial literature documents that dividend yield, industrial production and the term structure of the interest rates are among the best performing candidates for forecasting stock returns.

Therefore, after constructing the portfolios and calculating the cumulative abnormal returns for each decile, these time series are regressed on the aforementioned variables as well as the simple CAPM. The next chapter includes the results from the around one hundred regressions regarding the explanatory power and the statistical significance of the variables.

Chapter 4

Empirical Results

4.1 Introduction

The fourth chapter of the present study consists of a presentation of the empirical results, their economic interpretation, the limitations of the current study and thoughts for future work. In the first part of the study the results for cumulative excess returns for the period will be analysed for both the accruals sorted and the intersecting portfolios. The second part consists of the regressions results for the state variables described in the previous chapter. Finally, the last part discusses the limitations of the study and what we consider as helpful and fruitful future research.

4.2 Empirical Results for the accrual anomaly.

The accrual anomaly, as illustrated in the previous chapters was examined and the results generated seem to confirm previous studies. Hardouvelis et al (2009) documents excess returns for a strategy taking a long position on the low accruals decile and short selling the high accruals decile. In addition, Stark et al (2009) documented similar results for UK firms.

As illustrated in the research and methodology chapter, the current study approached the accrual anomaly under two different scopes. Initially, deciles based on accruals were sorted and a strategy was formed for seventeen years (1991-2008). We preferred not to document only the results for the two extreme deciles but to also investigate the returns for a similar to Value-minus-Growth strategy, which is long on the first two deciles and short on the two last. The latter strategy is arbitrarily named “DMG” for practical reasons.

The following table (Table 4.1) depicts the descriptive statistics for the ten deciles along with the two strategies for the sorting based on accruals (Panel A), and the nine deciles and the two strategies according to accruals and size (Panel B). The Dec1-Dec10 strategy yields a return that is around 2.6% over the risk free rate and the “DMG” yields an approximate 8.3% annual excess return.

We could cite that the standard deviation and the range from the minimum to the maximum observation for the all the deciles, and especially for the “DMG” strategy, appears to be substantially high. Especially for the strategy that shows the most profits, the maximum annual observation was around 0.5 while the minimum observation was approximately equal to -0.32. Comparing the standard deviation of the two different strategies, it is clear that the “DMG” strategy appears as more uncertain. It would be a speculation though to say whether the larger returns for the “DMG” are due to the larger uncertainty, given the statistical results and the limitations of the current study.

In addition, previous papers document an almost monotonically descending order for the excess returns from the first to the last decile. On the other hand, our results can not confirm this trend (especially for the seventh decile). In more details, according to our data, a strategy based on the seventh portfolio, instead of the last two extremes, would be more profitable. The aforementioned result can be due to the sample used or even due to pure luck.

Panel B of Table 1 contains the descriptive statistics for the nine deciles (intersecting portfolios) and the two strategies. The further sorting of the portfolios was based on the assertion and some evidence that the profits of the accrual anomaly are largely due to the “contribution” of small stacks.

The empirical results confirm that the ideal profitable strategy is the one that is long on low accruals’ small firms and short on high accruals’ large firms. The annual excess return for the Dec1-Dec9 strategy appears to be 3.5%, while “the two first -minus-the two last” strategy yielded an impressive 9.3% annual return over the rate that we used as a proxy for the riskless asset. In general, the intersecting portfolios seem to yield larger returns than the simple accrual sorted deciles. In addition, the descending order trend seems to be more confirmed for the interacting portfolios. In particular, the excess returns for the lower deciles tend to be larger than the excess returns of the highest deciles, which in most cases are negative.

On the other hand, the results for the standard deviations appear to be similar to the results for the strategy based solely on accruals. The standard deviation for the most profitable strategy is approximately 0.26, while all the deciles show large fluctuations in their returns. In general, the empirical results confirmed that investors could exploit opportunities for a profitable strategy based on publicly available information. On the other hand, the seventeen years’ period that we used yielded results that could be purely random. It is worth mentioning, that the 1991-2008 date range included two major harsh economic periods for the UK market. Firstly, the 2000 dot.com bubble and secondly the current economic recession.

Table 4.1. Descriptive statistics for the excess returns of the portfolios.

Panel A.The first table includes the descriptive statistics of the excess returns of the ten deciles and the two strategies when the formation of the portfolios was based on accruals. Std.Dev stands for the standard deviation, Probability stands for the p-values and DMG stands for the first two deciles minus the two last.

D1-Rf

D2-Rf

D3-Rf

D4-Rf

D5-Rf

D6-RF

D7-RF

D8-RF

D9-RF

D10-RF

(D1-D10)-Rf

DMG

Mean

0.0357

0.0353

0.0201

-0.0041

0.0204

0.0175

-0.0327

-0.0416

-0.0219

-0.0647

0.0264

0.0836

Median

0.0149

0.0249

0.0123

-0.0249

-0.0243

0.0012

-0.0546

-0.0343

-0.0094

-0.0230

0.0088

0.0739

Maximum

0.3001

0.3753

0.3339

0.4110

0.3468

0.3503

0.2932

0.2824

0.2931

0.1059

0.2841

0.5698

Minimum

-0.1522

-0.1935

-0.1620

-0.3095

-0.1956

-0.1798

-0.3487

-0.2398

-0.2594

-0.4159

-0.1827

-0.3267

Std. Dev.

0.1177

0.1543

0.1117

0.1810

0.1657

0.1396

0.1468

0.1295

0.1463

0.1628

0.1443

0.2089

Probability

0.5648

0.5694

0.0607

0.3613

0.3151

0.6192

0.8451

0.5490

0.6461

0.2183

0.6931

0.7837

Panel B.The second table includes the descriptive statistics of the excess returns of the ten deciles and the two strategies when the formation of the portfolios was based on accruals and size (intersecting). Std.Dev stands for the standard deviation, Probability stands for the p-values and DMG stands for the first two deciles minus the two last.

D1-Rf

D2-Rf

D3-Rf

D4-Rf

D5-Rf

D6-RF

D7-RF

D8-RF

D9-RF

(D1-D9)-RF

DMG-RF

Mean

0.0526

0.0304

-0.0090

-0.0422

0.0018

-0.0641

-0.0344

-0.027

-0.0368

0.0352

0.0937

Median

0.0118

0.0029

0.0017

-0.0091

0.0182

-0.0279

-0.0459

-0.0371

-0.0094

0.0446

0.0493

Maximum

0.4191

0.3481

0.1650

0.3562

0.3484

0.1556

0.4310

0.1882

0.1350

0.3503

0.6706

Minimum

-0.2108

-0.1806

-0.2147

-0.4810

-0.2822

-0.276

-0.3768

-0.1779

-0.2851

-0.2912

-0.3694

Std. Dev.

0.1631

0.1509

0.0917

0.1841

0.1486

0.1148

0.2149

0.1152

0.1092

0.1728

0.2666

Probability

0.5613

0.6742

0.8560

0.7062

0.8690

0.7401

0.7422

0.5863

0.6839

0.8728

0.5701

4.3 Results for the regressions of the excess returns.

The current part of the empirical results includes the findings of the regressions of the deciles based on both the accrual sorting and the intersecting portfolios on the state variables described in the previous chapter. The results are divided into two parts that consist of three subparts each. First we will be presenting the results for sorting based on accruals.

As described in the data and methodology chapter, we ran five sets of regressions. Firstly, we present the results for the simple CAPM and the three factor model introduced by Fama and French (1996), while afterwards we document the coefficients, the t-stats and the Adjusted R-squares for the regressions on the macro variables and the combination of the CAPM with the macroeconomic variables. Finally, we illustrate the evidence on the investigation of the combination of the F&F model with the macroeconomic variables.

In a similar way, the next part (4.3.2) includes the regressions results for the intersecting portfolios and the strategies formed by these portfolios.

Table 4.2

Correlation Matrix of the regressions

Correlation Matrix for the variables used in the regressions. IPGROWTH stands for the logarithmic growth of the industrial production, Term stands for the term structure of the interest rates, HML for the returns of the portfolios "high minus low", SMB for the returns of the portfolio "small minus big",DY for the dividend yield and RM-Rf for the excess return of the market over the risk free rate.

RM-RF

DY

HML

SMB

TERM

DY

-0.0326072

HML

-0.0524271

0.0721244

SMB

-0.0001153

0.0722646

0.3392765

TERM

-0.0712327

-0.2558232

-0.0196066

-0.0371131

IPGROWTH

0.1189447

0.0430802

0.0075368

-0.0244029

-0.10404

Table 4.2 depicts the correlation matrix for the variables used in the regressions. The largest and positive correlation was observed for the “HML” with “SMB” and for the market premium with the growth of the industrial production. On the other hand, the largest and negative correlations were observed for the “DY” with the “TERM” factor and for the “IPGROWTH” with the “TERM”. It is worth noting that in general the correlations for the variables used are relatively small. 

4.3.1. Regressions for the portfolios sorted based on accruals.

The next table (Table 4.3) includes the results for the regressions (CAPM) of the ten deciles and the two strategies. The results for the Capital Asset Pricing model yielded statistically significant coefficients for the excess returns of the deciles two, three, six and, most importantly, for the strategy that is based on taking a long position on the first two deciles and a short position on the deciles with the firms with the highest accruals. The coefficients of the third and sixth deciles are small and positive, but the coefficients for the second decile and the “DMG” strategy appear to be negative (0.16 and -0.28 respectively). The results for the constant show that the linearity holds for all the deciles apart from the excess returns of the eighth decile, which is statistically significant but substantially close to zero.

The Fama and French (1996) three factor model regression shows that the HML and SMB factors do not have explanatory power for predicting the returns on the portfolios when these are sorted based on accruals. Both HML and SMB appear to be catholically statistically insignificant. The three factor model results show similar results for the predictive power of the market premium. The second, third, sixth and the “DMG” portfolios have significant coefficients for the market premium, while the values follow a similar trend as those yielded by the CAPM. It is worth noting that the results for the overall explanatory power of the models (Adjusted R-squares) indicate that there are additional variables that influence and predict the returns of the portfolios.

Table 4.3 

Monthly Regressions of the Excess Decile Returns on the Fama and French Factors

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals.R1 stands for the CAPM regression and R2 for the three factor model by Fama&French. Rf stands for the riskfree rate. Di- Rf is the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return. HML is the High Minus Low Portfolio and SMB is the Small Minus Big Portfolio. Figures with the asterisk indicate statistical significance.

Rm-Rf

HML

SMB

CONSTANT

R2

D1-Rf

R1

0.000534

-0.0005

-0.0050

R2

0.0031

0.0048

-0.0003

-0.0003

-0.0128

D2-Rf

R1

-0.1678*

0.0010

0.0173

R2

-0.1670*

0.0014

0.0060

0.0012

0.0113

D3-Rf

R1

0.1067*

-0.0009

0.0114

R2

0.1081*

0.0026

0.4396

-0.0009

0.0025

D4-Rf

R1

0.0152

-0.0031

-0.0048

R2

0.0164

0.0021

0.0020

-0.0030

-0.0139

D5-Rf

R1

-0.0160

-0.0016

-0.0048

R2

-0.0132

0.0051

-0.0010

-0.0014

-0.0132

D6-Rf

R1

0.0377*

-0.0013

-0.0028

R2

0.0377*

0.0000

0.0025

-0.0013

-0.0118

D7-Rf

R1

0.0071

-0.0059*

-0.0049

R2

0.0056

-0.0027

0.0013

-0.0060*

-0.0143

D8-Rf

R1

0.0223

-0.0066*

-0.0043

R2

0.0243

0.0035

-0.0011

-0.0065*

-0.0128

D9-Rf

R1

0.0522

-0.0008

-0.0024

R2

0.0518

-0.0007

0.0037

-0.0007

-0.0111

D10-Rf

R1

-0.0029

-0.7421

-0.0048

R2

0.0130

-0.0040

0.0060

-0.0029

-0.0128

(D1-D10)-Rf

R1

-0.0150

-0.0022

-0.0048

R2

-0.0102

0.0089

-0.0063

-0.0020

-0.0092

DMG-Rf

R1

-0.2894*

0.0000

0.0227

R2

-0.2886*

0.0015

0.0053

0.0001

0.0142

Table 4.4 presents the results for the regressions of the macro-factors and the dividend yield (R1) and the combination of the three factors and the CAPM (R2). The first regressions show that the dividend yield and the logarithmic growth of the industrial production are statistically significant at the 5% confidence level for approximately half the deciles, revealing their explanatory power. The values of the significant coefficients are catholically negative, while the results show that the decile that is affected the most from the fluctuations of the dividend yield is the ninth.

The “Term” variable does not appear to affect the returns of the deciles apart from the seventh decile, where a t-stat of 2.3 indicates that changes in the term structure of the interest rates will negatively affect the returns of the decile by 0.4%. At this point, we should note that the t-stats of the variables for the two strategies yielded statistically insignificant results. The incorporation of the market value in the regression gave similar results for the macro factors as described above. In addition, the significance of the market premium follows similar trends as in the previous table. The results for the constant show an almost overall significant presence for the deciles but not for the two strategies.

The next table (Table 4.5) illustrates the results for the combination of the market premium, the Fama and French risk factors, the dividend yield, the industrial production and the term structure of the interest rates. The market premium appears to negatively affect the “DMG” portfolio, while the t-stat shows that the negative sign and the value of 0.29 is statistically significant. In addition, the HML and SMB seem to have no predictive power based on our sample. The two macroeconomic factors, namely the dividend yield and the industrial production, appear to negatively affect the accrual-based portfolios. On the other hand, the values of the coefficients are substantially small. The variable “Term” yielded results that are identical to the ones in the table below (number). In general, the regression of the combination of the variables used produced a negative and significant relation between the “DMG” returns and the market premium. Moreover, the dividend yield and the industrial production growth display the largest predictive power. It is worth noting that the constant appears to be large and significant for the first two deciles (0.37 and 0.28 respectively).

Table 4.4

Monthly Regressions of the Excess Decile Returns on the macro-variables.

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals. Rf stands for the riskfree rate.. Di- Rf is the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return. DY stands for the dividend yield, IPGROWTH stands for the logarithmic growth of the industrial production and Term for the term structure of the interest rates. R1 stands for the regression of the returns on the DY, IPGRWOTH and TERM while in the R2 the market premium is incorporated. Figures with the asterisk indicate statistical significance.

Rm-Rf

DY

IPGROWTH

TERM

CONSTANT

R2

D1-Rf

R1

-0.0180*

-0.0031*

-0.0018

0.3640*

-0.0089

R2

-0.0356

-0.0185*

-0.0032*

-0.0018

0.3758*

-0.0140

D2-Rf

R1

-0.0104

-0.0018

-0.0006

0.2147

0.0125

R2

-0.1934*

-0.0136

-0.0024

-0.0009

0.2783

0.0247

D3-Rf

R1

-0.0166*

-0.0030*

-0.0007

0.3462 *

-0.0096

R2

0.0782

-0.0154*

-0.0027*

-0.0006

0.3206*

0.0076

D4-Rf

R1

-0.0054

-0.0015

-0.0037

0.1603

0.0047

R2

-0.0076

-0.0055

-0.0015

-0.0037

0.1628

-0.0002

D5-Rf

R1

-0.0059

-0.0008

-0.0008

0.0939

-0.0127

R2

-0.0270

-0.0063

-0.0008

-0.0009

0.1027

-0.0178

D6-Rf

R1

-0.0073

-0.0021*

-0.0014

0.2312*

0.0019

R2

0.0153*

-0.0071

-0.0021*

-0.0014

0.2261*

-0.0001

D7-Rf

R1

-0.0066

-0.0011

-0.0049*

0.1253

0.0226

R2

-0.0164

-0.0068

-0.0012

-0.0049*

0.1307

0.0177

D8-Rf

R1

-0.0157*

-0.0029*

-0.0020

0.3312*

0.0124

R2

-0.0112

-0.0159*

-0.0029*

-0.0020

0.3349*

0.0093

D9-Rf

R1

-0.0231*

-0.0031*

-0.0003

0.3801*

0.0006

R2

0.0195

-0.0228

-0.0030*

-0.0003

0.3737*

-0.0019

D10-Rf

R1

-0.0047

-0.0012

-0.0022

0.1294

-0.0050

R2

-0.0012

-0.0047

-0.0012

-0.0022

0.1298

-0.0094

(D1-D10)-Rf

R1

0.0132

0.0021

-0.0010

-0.2559

-0.0057

R2

-0.0346

-0.0139

-0.0018

-0.0001

0.2247

-0.0097

(DMG)-RF

R1

-0.0134

-0.0017

0.0000

0.2133

-0.0051

R2

-0.2999*

-0.0113

-0.0011

0.0010

0.1495

0.0182

Table 4.5

Monthly Regressions of the Excess Decile Returns on the overall set of variables.

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals. Rf stands for the riskfree rate. Di- Rf is the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return. HML is the High Minus Low Portfolio and SMB is the Small Minus Big Portfolio.DY stands for dividend yield,IPGROWTH for the logarithmic growth of the industrial production and TERM for the term structure of the interest rates. Figures with the asterisk indicate statistical significance.

Rm-Rf

HML

SMB

DY

IPGROWTH

TERM

CONSTANT

R2

D1-Rf

-0.0330

0.0052

-0.0002

-0.0188*

-0.0032*

-0.0018

0.3791*

-0.0228

D2-Rf

-0.1926*

0.0017

0.0061

-0.0139

-0.0023

-0.0008

0.2821

0.0183

D3-Rf

0.0797*

0.0030

-0.0014

-0.0154*

-0.0027*

-0.0005

0.3219*

-0.0011

D4-Rf

-0.0066

0.0019

0.0013

-0.0057

-0.0015

-0.0036

0.1646

-0.0099

D5-Rf

-0.0243

0.0053

-0.0008

-0.0065

-0.0008

-0.0008

0.1059

-0.0262

D6-Rf

0.0152*

-0.0001

0.0019

-0.0071

-0.0020*

-0.0013

0.2269*

-0.0097

D7-Rf

-0.0177

-0.0027

0.0008

-0.0066

-0.0011

-0.0049*

0.1292

0.0082

D8-Rf

-0.0092

0.0038

-0.0011

-0.0160*

-0.0029*

-0.0019

0.3369*

0.0010

D9-Rf

0.0195

0.0003

0.0044

-0.0230*

-0.0030*

-0.0002

0.3758*

-0.0101

D10-Rf

-0.0034

-0.0041

0.0056

-0.0047

-0.0011

-0.0022

0.1294

-0.0180

(D1-D10)-Rf

0.0294

-0.0092

0.0059

0.0140

0.0022

-0.0009

-0.2708

-0.0142

DMG-Rf

-0.2988*

0.0022

0.0060

-0.0117

-0.0011

0.0010

0.1535

0.0099

4.3.2. Results of the regressions for the intersecting portfolios.

This sub-chapter includes the results for the regressions, where the dependent variables were the excess returns on the portfolios and the strategies formed that were constructed based on accruals and size. The first table (Table 4.6) incorporates the results for the regressions for the CAPM and the Fama and French three factor model.

The results generated show that the market premium has significant explanatory power only for the second decile and the “DMG” strategy. Both values appear to be negative (-0.1 and -0.2 respectively). The Fama and French loadings are catholically insignificant except for the SMB factor for the excess returns on the fifth decile where the coefficient was estimated to be close to 0.1. The CAPM hypothesis for linearity seems to hold for all portfolios except for the sixth decile where the constant was significant but approximately zero.

Further, we regressed the excess returns of the interacting portfolios on the dividend yield, the logarithmic growth of the industrial production and the term structure of interest rates. In addition, the market premium was added in the regression in order to detect the changes in the explanatory power and the t-stats of the results. The findings reveal (Table 4.7) the statistical significance of the dividend yield and the industrial production growth across the sample. Across the deciles, the findings support that both the dividend yield and the industrial production have a negative impact on the returns of the deciles from one to nine. On the other hand, the regression of the returns of the two strategies yielded no significant results. The variable “Term” appears as insignificant, apart from the seventh decile. Similar to our previous findings, the market premium seems to “explain” fluctuations of the returns of the strategy which is arbitrarily defined as “DMG”. We should point out that, although the dividend yield and the industrial production were found to be statistically significant when the portfolios were sorted according to accruals, the intersecting portfolios increased even more the predictability of the excess returns for these variables. Finally, the constant was found positive and significant for the vast majority of the portfolios, while the adjusted R-squares remained at substantially low levels.

Table 4.6

Monthly Regressions of the Excess Decile Returns on the Fama and French Factors

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals and size. Rf stands for the riskfree rate. Di- Rf are the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return. HML is the High Minus Low Portfolio and SMB is the Small Minus Big Portfolio. R1 stands for the CAPM regression and R2 for the three factor model by Fama&French. Figures with the asterisk indicate statistical significance.

Rm-Rf

HML

SMB

CONSTANT

R2

D1-Rf

R1

-0.0631

0.0005

-0.0019

R2

-0.0656

-0.0044

0.0148

0.0007

0.0046

D2-Rf

R1

-0.1001*

0.0000

0.0015

R2

-0.1016*

-0.0027

0.0141

0.0002

0.0045

D3-Rf

R1

0.0111

-0.0012

-0.0047

R2

0.0121

0.0018

0.0019

-0.0011

-0.0129

D4-Rf

R1

0.1000

-0.0056

-0.0004

R2

0.0990

-0.0020

0.0105

-0.0054

-0.0053

D5-Rf

R1

-0.0452

-0.0003

-0.0029

R2

-0.0453

-0.0002

0.0142

0.0001

0.0089

D6-Rf

R1

0.0875

-0.0065*

0.0031

R2

0.0884

0.001569)

-0.0004

-0.0065*

-0.0066

D7-Rf

R1

-0.0547

-0.0052

-0.0030

R2

-0.0561

-0.0027

0.0151

-0.0050

0.0022

D8-Rf

R1

0.0079

-0.0030

-0.0049

R2

0.0086

0.0012

0.0041

-0.0029

-0.0130

D9-Rf

R1

0.0526

-0.0020

-0.0021

R2

0.0536

0.0020

0.0007

-0.0019

-0.0115

(D1-D9)-Rf

R1

-0.1157

-0.0057

0.0056

R2

-0.1192

-0.0064

0.0141

-0.0056

0.0111

DMG-Rf

R1

-0.2237*

-0.0027

0.0145

R2

-0.2293*

-0.0103

0.0241

-0.0025

0.0269

Table 4.7

Monthly Regressions of the Excess Decile Returns on the Macroeconomic variables.

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals and size. Rf stands for the riskfree rate. Di- Rf is the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return.DY stands for the divend yield, IPGROWTH for the logarithmic growth of the industrial production and Term for the term structure of the interest rates. R1 stands for the regression of the returns on the DY, IPGRWOTH and TERM while in the R2 the market premium is incorporated. Figures with the asterisk indicate statistical significance.

Rm-Rf

DY

IPGROWTH

TERM

CONSTANT

R2

D1-Rf

R1

-0.0179*

-0.0031*

-0.0018

0.3640*

0.0198

R2

-0.0356

-0.0185*

-0.0032*

-0.0018

0.3758*

0.0160

D2-Rf

R1

-0.0104

-0.0018

-0.0006

0.2147

-0.0051

R2

-0.1934*

-0.0136

-0.0024

-0.0009

0.3758*

0.0189

D3-Rf

R1

-0.0166*

-0.0030*

-0.0007

0.3463*

0.0311

R2

0.0782

-0.0154*

-0.0027*

-0.0006

0.2783

0.0348

D4-Rf

R1

-0.0054

-0.0015

-0.0037

0.1603

0.0092

R2

-0.0076

-0.0055

-0.0015

-0.0037

0.3206

0.0042

D5-Rf

R1

-0.0059

-0.0008

-0.0008

0.0939

-0.0126

R2

-0.0270

-0.0063

-0.0008

-0.0009

0.1628

-0.0172

D6-Rf

R1

-0.0073

-0.0021

-0.0014

0.2312

0.0239

R2

0.0153

-0.0071

-0.0021

-0.0014

0.1027

0.0194

D7-Rf

R1

-0.0066

-0.0011

-0.0049*

0.1253

0.0174

R2

-0.0164

-0.0068

-0.0012

-0.0049*

0.2261

0.0127

D8-Rf

R1

-0.0157*

-0.0029*

-0.0020

0.3312

0.0307

R2

-0.0112

-0.0159*

-0.0029*

-0.0020

0.1307

0.0260

D9-Rf

R1

-0.0231*

-0.0031*

-0.0003

0.3801

0.0329

R2

0.0195

-0.0228*

-0.0030*

-0.0003

0.3349

0.0284

(D1-D9)-Rf

R1

-0.0134

-0.0017

0.0000

0.2133

-0.0042

R2

-0.0346

-0.0139

-0.0018

-0.0001

0.2247

-0.0085

DMG-RF

R1

-0.0065

-0.0003

0.0015

0.0508

-0.0101

R2

-0.2999*

-0.0113

-0.0011

0.0010

0.1495

0.0140

The last table (Table 4.8) depicts the results for the regressions of the intersecting portfolios on the overall set of the variables analysed in the present study. It is clear that the results are similar to the ones generated by the models analysed above. The HML and SMB factors demonstrate no significant performance, while the dividend yield and the industrial production appear to be the best candidates to explain the excess returns in the set of variables used. The adjusted R-squares are catholically at low levels regardless of the methods used for the portfolio sorting and irrespectively of the model tested. It has become clear that the excess returns of the portfolios that are sorted based on accruals (and size) are affected by additional factor(s). Further research is considered essential to test more variables especially for the firm-level environment due to the nature of the accounting figure that accruals are. As described in earlier parts of the current study, managers are believed to have incentives to adjust the firms’ earnings and thus affect the accruals. Future research on the impact of the insider trading and on how analysts and the large owners of the firms react to fluctuations of the accruals might generate variables that are able to show a larger predictive ability.

The current study has met several limitations mostly due to its nature. Firstly, the dataset used incorporates two major periods that are characterised as two of the largest downturns of the last decades. The 2000 dot.com bubble and the current economic recession had undoubtedly a huge impact on the financial markets. Still, it was interesting to investigate the results generated by a strategy that has been documented by many researchers. Unfortunately, due to the nature of the current study and thus the limited data range and data set, there is a possibility that the two aforementioned periods had a severe impact on the results. A more in depth research that will investigate the implications of the accruals strategy by using a larger sample might produce more precise results.

In addition, researchers are still investigating the impact of the methodology used to calculate accruals and the impact that each methodology has in capturing the misspricing.

Table 4.8

Monthly Regressions of the Excess Decile Returns on the overall set of variables

This Table presents the constants (C), slope coefficients and R2 of the Monthly Regressions for the Excess Returns of the deciles sorted according to accruals and size. Rf stands for the riskfree rate. Di- Rf are the excess portfolio returns. DMG stands for the two first deciles minus the two last deciles. Rm-Rf stands for the excess market return. HML is the High Minus Low Portfolio and SMB is the Small Minus Big Portfolio.DY stands for dividend yield,IPGROWTH for the logarithmic growth of the industrial production and TERM for the term structure of the interest rates. Figures with the asterisk indicate statistical significance.

Rm-Rf

HML

SMB

DY

IPGROWTH

TERM

CONSTANT

R2

D1-Rf

-0.0330

0.0053

-0.0002

-0.0189*

-0.0032*

-0.0018

0.3792*

0.0087

D2-Rf

-0.1927*

0.0018

0.0062

-0.0140

-0.0024

-0.0009

0.2821

0.0132

D3-Rf

0.0797

0.0030

-0.0015

-0.0155*

-0.0028*

-0.0006

0.3219*

0.0263

D4-Rf

-0.0066

0.0019

0.0014

-0.0057

-0.0015

-0.0037

0.1647

-0.0053

D5-Rf

-0.0243

0.0054

-0.0009

-0.0066

-0.0009

-0.0008

0.1060

-0.0257

D6-Rf

0.0152

-0.0001

0.0020

-0.0072

-0.0021

-0.0014

0.2269*

0.0101

D7-Rf

-0.0178

-0.0027

0.0008

-0.0067

-0.0012

-0.0049*

0.1292

0.0033

D8-Rf

-0.0093

0.0039

-0.0012

-0.0161*

-0.0029*

-0.0020

0.3370*

0.0180

D9-Rf

0.0196

0.0003

0.0045

-0.0230*

-0.0030*

-0.0003

0.3758*

0.0207

(D1-D9)-Rf

-0.0297

0.0095

-0.0059

-0.0142

-0.0019

-0.0001

0.2286

-0.0126

DMG-Rf

-0.2989*

0.0022

0.0060

-0.0117

-0.0012

0.0010

0.1535

0.0058

We decided to use a straightforward and relatively new definition of the accruals in order to have a base for comparison (as used in Hardouvelis et al 2009). Finally, an additional limitation of the study is the variables used. Due to the definition of the accruals that incorporates changes of the net operating assets over two years (as used in the present study) it might be useful and enlightening to further investigate the effect of the changes of the variables used between consecutive years.

4.4 Implications of the current study.

Evidence for the existence of profitable strategies is of primal interest for investors and fund managers. Investors are allocating their funds with the major goal of the profit maximization according to their preferences (i.e. the risk they are willing to undertake). The present study confirms other studies for the profitability of a zero cost (transaction costs are not included) strategy that is based on publicly available information. The confirmation of the profitability of the accrual strategy is beyond any doubt a major challenge for the EMH hypothesis and a precious finding for investors and fund managers. Investors and fund managers are always seeking for the correct messages in the information that is available to them which will enhance their profitability while keeping the risk undertaken at a certain level. The key question is whether profits generated by the accrual strategy are due to the underlying risk or due to mispricing. The simple methodology that was employed in the current study cannot answer or approach this puzzle but the results reveal abnormal returns while the levels of the standard deviation are high.

In addition, the second part of the empirical chapter reveals that dividend yield and industrial production growth have a superior (compared to the other variables) ability to forecast the returns of the portfolios constructed based on accruals and accruals and size. A never ending challenge for the financial literature is the “discovery” of the variables that will be proved as the key drivers of the stock returns. The challenge is not primarily attached to the idea of the “non limit” profits as it is to the optimization problem for each individual investor given his or her preferences. A potentially “correct” model that will have the ability to accurately forecast the stock returns can be considered as the most important tool for investors and fund managers. It is yet to discover which the most efficient predictors for the stock returns are. The findings of the current study show that the industrial production growth and dividend yield are among the strong candidates for the prediction of the stock returns for accruals’ and accruals’-size portfolios.

Finally, it is a commonly held belief that managers have the incentives to manipulate the firms’ earnings so that a stable and mostly a progressing image of the firms’ figures is created. The current study shows that stocks with higher accruals tend to underperform stocks with lower accruals. Although, it would be a simplified conclusion to say whether investors beliefs for the high changes in the net operating assets are the drivers for this anomaly the results of the current study are surely a warning message for managers.

4.5 Synopsis of the empirical results

Chapter 4 presented the results of the empirical analysis of the accrual anomaly as documented by Sloan (1996). The methodology to calculate accruals was based on a recent paper by Hardouvelis et al (2009). The investigation of the excess returns of strategies was approached by two different scopes regarding the formation of the portfolios.

The data sample we used generated 2.6% annual return over the risk-free rate for the accrual sorting portfolios, while the intersecting portfolios generated an additional 0.9%. In addition, we formed deciles similar to the Value-Growth strategy, taking a long position on the first two extreme portfolios and a short position on the last two. This strategy yielded 8.3% average annual excess return for the portfolios constructed according to accruals and 9.4% for the intersecting portfolios. The descriptive statistics for the annual excess returns revealed large fluctuations of the returns for the portfolios, captured by a substantially high standard deviation. Furthermore, the results generated by a simple historical investigation were proved as statistically insignificant.

The next part of the chapter presented the results for the state variables that were used in order to regress the portfolios on. The findings support that the dividend yield and the industrial production growth have the largest predictive power among the set of the variables used. The Fama and French (1996) risk factors (HML and SMB) appeared as overall unable to capture the changes of the excess returns of the portfolios in our sample. Several methods were used to investigate whether the explanatory power of the models would improve as captured by the Adjusted R-square. The findings yielded considerably low figures regardless of the model used. As already mentioned, further research is needed to test the assertions of the plethora of papers that consider the relation of the accrual anomaly to other already known anomalies.

Chapter 5

conclusions

5.1 Synopsis of the current study.

The last two decades, academics have been largely arguing on the efficiency of the markets. Since the definition of the Efficient Market Hypothesis by Fama in the early 1960s, the financial literature has become richer and richer with evidence in favour of the rejection of the EMH.

The plethora of publications discourses on a wide range of imperfections such as the contrarian investment, the post announcement drift and recently the accrual anomaly. Sloan (1996) documented the opportunity of investors to earn positive abnormal returns by simply taking long position on low accrual stocks and short position on high accrual firms. Since then, academics are investigating the accrual anomaly in order to detect and specify the connection it has with other anomalies and examine its international presence.

The current study discourses on the accrual anomaly by employing the methodology suggested by Hardouvelis et al (2009) to calculate accruals. Briefly, stocks were assigned into ten portfolios (from low to high) according to their accrual figure. We investigated the profitability of such strategy for the 1991 to 2008 period. The results reveal that a 2.6% excess return is gained if an investor had allocated his/her funds into the portfolio Low minus High. In addition, a strategy that is similar to the VMG (value minus growth), which is arbitrarily named as “DMG” in the current study, earned 8.3% average annual excess return.

The next step was to investigate the evidence of Stark et al (2009) that small stocks attribute to the profitability of the accrual strategy. Stark et al (2009) suggested that since the small stocks are the most profitable, the strategy itself has restricted profits due to the large transaction costs. To test this hypothesis, we sorted portfolios according to size and accruals and investigated the change of the profitability compared to the one-way classification. The results yielded show annual excess returns around 3.6% for the Decile 1 minus Decile 10 strategy and 9.3% for the “DMG” strategy. It is worth noting that the results for both the one-way classification and the intersecting portfolios appeared to have substantially high standard deviation.

The next part of the study investigates the explanatory power of five variables. We chose to test the returns of the portfolios HML and SMB proposed by Fama and French (1996) as well as the dividend yield for the FTSE all share index, the logarithmic industrial production growth and the term structure of the interest rates.

The results revealed no connection between the HML and SMB factors and the returns of the portfolios sorted neither with accruals nor with the intersecting portfolios. The growth of the industrial production and the dividend yield appeared to be the most effective to explain the returns for the portfolios formed.

Undoubtedly, the accrual anomaly has not been fully investigated neither for its actual profitability nor for the relation it has with other market imperfections. Further research is considered as necessary in order to see what drives the returns of the accruals’ based portfolios.

Due to the nature of the accruals and the obvious incentive of the managers to manipulate earnings, it is considered as essential to test the connection of the accrual anomaly to factors such as the selling of stocks by managers, or large owners of the firms. In addition, analysts’ forecasts revisions are considered as potentially effective candidates to predict stock returns for such strategies.

5.2. Limitations of the current study and ideas for future research.

Due to the nature of the present study and the extent is has, many limitations have been occurred. Firstly, the data sample was limited to around 1200 stocks each year while the time horizon was only seventeen years. The larger limitation for the sampling horizon was the fact that it incorporates two major “economic crisis” with clear effects on the stock market (dot.com bubble and the 2008 credit crunch).

In addition, the methodology that was employed in order to test the profitability of the accruals anomaly was limited in the construction of portfolios based on size and accruals. Further construction of portfolios based on book to market, sales and other figures would surely enhance the precision of the results and would provide us with additional evidence to answer whether accruals anomaly is connected with other anomalies.

Furthermore, the inclusion of additional variables would surely add predictive power of the models created in the current study and would hopefully create a clearer image for the drivers of the returns of the portfolios constructed based on accruals.

Finally, as already mentioned, we chose to employ the methodology used in Hardouvelis et al (2009) to calculate the accruals. It is suggested that the specific calculation method captures even larger mispricing. A comparison of the methodologies for the calculation of accrual would reveal whether the method employed plays a major role in the profitability of the accruals strategy.

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[1] Directly quoting datastream item’s WC02999 definition

[2] Directly quoting datastream item’s WC03351 definition

[3] Datastream item WC020005

[4] Datastream item WC03051

[5] Directly quoting datastream item’s WC18232 definition

[6] Hardouvelis et al (2009)

[7] Gregory,Harris and Michou (2001).