As discussed before, different financial market participants or pundits have different perceptions on which factors or variables are affecting stock returns in the financial market. For example, it can be found that market pundits tend to assert that if something happen, then stock returns will be affected positively (or negatively). However, most of the time, with the help of hindsight, it is found that the claims from these experts are not true, no matter how their original arguments may be as persuading as possible. As such, it is the responsibilities for investors to verify the claims of these market pundits. To do so, it is critical for any serious investors to investigate the rationale behind each of the claims of the market pundits. The factors postulated to will be affecting stock returns should be investigated, if these factors will truly have at least, historically statistically significant relationships with stock returns. If the factors are found to exhibit no statistically significant relationships to stock returns in the past, it is unlikely that these factors will affect stock returns in the future. As such, by performing the studies on stock returns as well as the possible factors or variables that is postulated to be affecting stock returns in the future, investors will be able to falsify the claims from many market pundits. With a better understanding on the true factors that may or may not affect stock returns through scientifically sound research, investors can then simply ignore the predictions made by market pundits, or to avoid being affected emotionally by their colorful predictions.
Consistent with the rise of computer technologies, to perform statistical analysis is nothing complex for the ordinary investors. Today, statistics concepts and software enable investors to investigate the variables or factors that could affect stock returns, and based on their findings, to make better or more rational investing decisions (Cavana et. al., 2001). In this dissertation, the various variables or factors postulated to be influential or predictive against stock returns in the future will be tested, investigated and verified.
This chapter is arranged as follow. Firstly, discussions will be concentrated on identifying the many different factors that are postulated to be able to affect stock returns in the future. For this, the different perceptions and views from successful investors, economists and academicians will be investigated. A review of the views from these parties and the factors that they argued to be influential in affecting stock returns will be presented. However, due to limited resources and time constraints of this study, only certain successful investors, economists or academic theories will be selected to be discussed. After reviewing the different factors that is perceived to be able to predict or affect stock returns in the future, a summary will be written to compile the many different ideas. The different factors will be categorized into different categories. Subjected to data availability, these factors will be employed as the research variables in the subsequent chapter. The relationships of these research variables to stock returns, on a historical context, will then be analyzed and studied.
In this section, the factors considered by successful investors in making their stock investing decisions will be discussed. While there are numerous famous and successful money manager available in the stock market, only a few famous money manager will be selected for discussion purposes due to time constraints. For this purpose, the successful investors selected include: Warren Buffett, John Neff, Peter Lynch, David Dreman and Joel Greenblatt.
Among the many successful investor, perhaps the most popularly known is Warren Buffet. Although Warren Buffet had never written any books to articulate his investment strategies, there are, nonetheless, many books written on him, and how to utilize his investment strategies for superior performance in the marketplace. Although the investment strategies of Warren Buffet are complicated, there are several financial variables or inputs that are often cited to be employed by the legendary investors in making his investment decisions. As discussed by Buffett & Clark (2002), the gross profit margin as well as the net profit margin of a company is a key criteria employed by Warren Buffett in analyzing the many companies available in the market. According to the authors, it is commented that companies that possess high profit margin, tend to possess certain competitive advantages in the industry. In contrast, companies with low profit margin often suffer from certain troubles that are actually causing the company to only able to achieve a low profit margin in the business. A common reason causing low profit margin is the industry structure is highly competitive, whereby there are many companies or rivalries competing with each other in the particular industry. Then, the authors also commented that companies with higher profit margins tend to be the monopoly (or near monopoly) in the particular industry. Aside from profit margin, as commented by Buffett & Clark (2002), the long term growth of earnings per share (EPS) is also a figure often emphasize by Warren Buffett in making investment decisions. Apparently, Warren Buffett emphasizes greatly on the long term track records of profitability of a company. Consistency of growth in EPS is perceived as valuable traits of successful companies with competent management. Then, as discussed in Buffett & Clark (2008), it is also cited that companies that able to achieve consistency high return on equity (ROE) is perceived as possible investment target by Warren Buffett. Accordingly, ROE is a good measure indicating the returns a company has been generating for the shareholders. After all, investors taking up huge risks by investing in companies managed by others are purely to get rewarded from a well-managed company. As such, as asserted by Buffett & Clark (2008), companies proven to be able to exhibit consistently high ROE achievement are those companies that may be excellent investment target for superior returns in the future. Such a view is also agreed by Dorsey (2004). Aside from that, it is also commented by Buffett & Clark (2008) that good companies are those that rely on expansion using internally generated funds. Warren Buffett is cited as often shy away from companies utilizing huge degree of leverage or debt in the capital structure. Debt to equity ratio is often employed to understand how a particular firm is geared. As discussed by Buffett & Clark (2008), Warren Buffet prefers those firms with zero debt. As such, the lower the debt to equity ratios, the lower the risks faced by the company. Nonetheless, there are many other aspects or issues emphasized by Warren Buffett in making the investment decision. Many of these aspects are qualitative in nature, and thus, may not subject to quantitative research method as to be conducted in the later section of this dissertation. Among the other qualitative aspects emphasized by Warren Buffett include: ethical, candid and prudent management, the branding of the products, the industry sector the company is situated in, the ease of understanding the business structure, the relative durable competitive advantages possess by a firm, the industry outlook, the growth prospects for a firm, and the existence of constant share buy back from the company (Jain, 2010).
John Neff is yet another successful investor, despite being a less well-known money manager. For John Neff, low P/E stocks are what look attractive in the market. According to the successful investor, P/E ratio is actually a measure on the expectation investors has on the growth prospects on a particular stock (Neff & Mintz, 1999). Thus, high P/E is often associated with high hopes and expectations, on the growth prospects as well as the hopes for future capital gains. However, there is one significant drawback on high P/E ratio built into a stock. Specifically, high P/E stocks are often those high flying growth stocks that can be highly risky, and could plummeted at the slightest sign of disappointment. However, on the other hand, there are little hopes on low P/E stocks. For these low P/E stocks, little or no growth at all for the earnings may not disappoint investors significantly. In contrast, when there are signs of improvements on the prospects or earnings of these low P/E stocks, it is easy to trigger fresh interests on the market place. This argument is similar to one presented by Wisdom (2009) in the discussion on reasons value investing may outperform the average market returns. In the words of Neff & Mintz (1999), investors should purchase stocks when they are out of favor and unloved, and then to sell them back to the marketplace when investors recognize the advantages of holding these stocks. Then, another important financial metrics tracked by John Neff is the growth rate of EPS for a particular firm. As discussed in Train (2000), John Neff is often famous for loving firms with track records of consistent and reasonable growth rate. However, high growth rate is often perceived as dangerous, because these firms with high growth rates are often built into high expectation, and causing these firms to be valued at high price multiples in the market. Besides, the growth rate of revenue is also highly emphasized by John Neff as important. This is not surprising, as rising revenue suggest a firm is making good progress in the marketplace, and has been able to sell the products or services to more people – and thus, generating higher revenue. Then, as discussed in Neff & Mintz (1999), another crucial financial variables watched closely by John Neff are that the good investment target should have good free cash flow. It is mentioned explicitly that high degree of free cash flow is an indication that the firm is making real money and profits from the business, as ultimately, cash is king. Besides, it is also perceived by the successful money manager that firms with high free cash flow often more readily able to implement share buyback program, to pay higher dividends as well as to make acquisition whenever it is beneficial for the firms (Train, 2000).
Another highly successful investor is the legendary fund manager, Peter Lynch – whereby most of the serious investors should already read the two books written by the legendary fund manager from United States. The high publicity of Peter Lynch, undeniably due to his highly impressive track records, is also due to his common sense approach to investing. For example, some of the best ideas provided by Peter Lynch to investors are as follow: invest in those things that you can understand, invest in simple business that even idiot can run, invest with company with a boring name, invest in dull business, invest in company with a disagreeable product line, invest in spin-off, invest in slow growth industry, invest in companies with niche in the marketplace, invest in companies producing products that must be repurchased frequently by customers, make investment when insiders are buyers, favor company that buyback shares and perhaps more importantly, it is inevitable to do the homework before investing for good investing performance in the future (Lynch & Rothchild, 1994). Apart from all of these qualitative factors, Peter Lynch tends to have several quantitative criteria that are used as input in the investing process. One of the most famous quantitative criteria is PEG ratios popularized by Lynch. Accordingly, P/E/Growth is the ratios obtained by dividing P/E ratio of a particular company with the growth rates of the EPS of that particular company. As discussed in Lynch & Rothchild (1989), PEG ratio is useful in identifying growth stocks that are selling at cheap pricing. This is essentially, called the growth at a reasonable price strategy (GARP). The rationale of such ratio is that for firms with higher growth rate, investors should be able to tolerate a higher P/E ratio for that stock. Thus, it is then, following from such argument, that firms trading at low PEG ratio are best investment target. Then, another variables examined by Peter Lynch is concerning the ratio of inventory to sales ratio. This is a ratio particularly useful to analyze the health of operations for manufacturing companies. Essentially, firms with higher growth in inventory when compared with the growth of revenue by that company is a red flag. This suggests that the inventory is not selling satisfactory, and thus, resulted in piling up of inventory, which can be risky for manufacturing firms. As articulated in Lynch & Rothchild (1989), investor should only invest in firms that exhibit a situation whereby inventories increase less than revenues, at the same rate as the revenue as or slightly faster than revenues. Apart from that, another key financial variables investigated are the debt to equity ratio of the firms. As commented in Lynch & Rothchild (1989), those conservatively financed firms are better investment target, and mathematically, it is advised investors to stick to invest in firms with debt lesser than 50% of the equity available in the capital structure. Such argument is consistent with views of other writers, including: Dorsey (2004), and Greenwald, Kahn, Sonkin & Biema (2001).
David Dreman is one of the great contrarian investors that are proven able to achieve highly impressive performance in the stock market. In his books, Dreman (1998) has been repetitively commenting on the key concept of contrarian investing, i.e., when a stocks had become well-known and favored by the mass, the guru investors is likely to be selling these stocks or just to avoid them. In order to be successful, as articulated by Dreman (1998), an understanding on market psychology is highly important. One of the most important phenomenon to be understood, according to the successful investor is that optimism and pessimism of investors often cause the best performing stocks to be highly undervalued while the worst performing stocks to be greatly undervalued. In the actual words of Dreman (1998), it is mentioned explicitly that: ‘empirical findings have been showing that the market expects the best futures for, as measured by the price/ earnings, price-to-cash flow, price-to-book value, and price-to-dividend ratios have consistently done the worst, while those stocks believed to have the most dismal futures have always done the best’. As such, there are several price based multiples or variables emphasized by Dreman (1998) in making investing decisions. Firms with lower price to earnings ratio, lower price to cash flow ratio, lower price to book value ratio as well as lower price to dividend ratios are potential high performers in the future. This is a similar concepts widely discussed by Kahn, Sonkin & Biema (2001) on strategies to outperform the market average returns through investing in neglected or boring stocks. Nonetheless, price multiples based financial ratios are not the only factors considered by David Dreman. Other equally important variables include the following: market capitalization of the firm, the earnings growth trend and track records, the payout ratio, return on equity and debt to equity ratios. To explain, it is argued that firms with larger market capitalization is often lower risks, and thus, more suitable investment targets. Besides, as those larger companies are often more in the public eyes, larger firms are less likely to suffer from accounting gimmickry. Then, earning trends are important because the past historical earnings records of any firms are crucial indication on the business nature and competency of the management team. Rising earning trends are indication that a firm at least able to grow profitably in the marketplace, and hence, a more suitable investment target. For this, Domash (2006) has a similar view – whereby to accumulate stocks of companies that exhibit rising earnings trends during market panic is a good stock investment strategies. Then, payout ratio is important as the differences of payout ratio of a firm compared to the historical payout ratio of that particular firm is crucial in affecting the probability of a firm in raising dividend payment in the future. According to Dreman (1998), firms with low payout ratios, when compared to the historical payout ratio tend to have higher chances of price appreciation, due to greater possibilities of increasing dividend yield of that stock. Then, similar to Warren Buffet, firms with high return on equity (ROE) and low debt to equity ratios are perceived as better companies to be owned. Regarding this issue, both ROE and dent to equity ratios are also widely discussed and perceived as important elements in stock investment by Wisdom (2009), Dorsey (2001) and Reese & Forehand (2009). Interestingly, as will be further discussed below, Clubb & Naffi (2007) found that the famous return on equity (ROE) figure is useful in explaining cross sectional stock returns in United Kingdom.
As discussed in paragraphs above, successful tend to have complex stock selections methods with many factors, both qualitative and quantitative to be considered in achieving extraordinary returns in the marketplace. However, Joel Greenblatt is the recently famous investors that claim that he has a magic formula- to make investment decision based only on two financial ratios, namely the returns on capital, and secondly, the earning yields of the particular stocks. To explain, earning yields are actually the inverse of price to earnings ratios. Thus, if assertion of Greenblatt (2006) is indeed true, investors can perform better by choosing a portfolio of firms that have the highest return on capital as well as the highest earning yields simultaneously. There are however, empirical evidences supporting the claims of Greenblatt (2006). For example, De Peña, Forner & López-Espinosa (2010) found that returns on capital are statistically significant in driving the stock returns in Spanish stock market.
It is highly surprising that a review of the books written by the successful investors, or by those authors trying to decipher the investment strategies of these successful investors found that many of the financial variables focused by these successful investors are common. Firstly, it is observed that the successful investors tend to look highly on the consistency of earnings track records. Then, the profitability ratios are also being studied and emphasized as crucial in making successful investment decisions in stock selection. Then, it is also understood that price multiples are commonly used to judge the relative attractiveness of a stock in the financial market. Price to earnings ratio, despite its popularity, are considered by the investors on if a stock is undervalued, fairly valued and overvalued. There judgment on the attractive value of P/E ratios, however, may be more complicated than it sound, as these successful investors seemingly have much complicated qualitative factors to be considered in relation to these financial ratios. Then, other crucial financial variables often perceived as important include the following: debt to equity ratio, return on equity and growth in revenues for the long term. Interestingly, none of the famous and successful investors really employ macroeconomic variables in making their investment decision making process (except for some investors, such as Warren Buffet, interest rates is incorporated to calculation of intrinsic values for the target company to be invested).
In this section, the views on the crucial variables argued by economists to be important in affecting or predicting stock returns will be articulated. The judgment against the usefulness of opinions from economists, tend to be contradicting and mixed. For example, as argued by Montier (2005), economist should be ignored, as in the study; it is found that their predictions are not better than randomness. However, such a view is not something widely acceptable in the real world, as there are a lot of popular best sellers on the topic of investment by economists on how to utilize economic indicators or variables to make better investment decisions. Indeed, the use of economic indicators is often well articulated in academic textbooks, on how stock returns might be affected by the various important economic variables that is powerful enough to influence or to track the business cycles.
For example, text book written by Bodie, Kane, & Marcus (2007) discussed the importance of economic variables in affecting the stock returns. Specifically, several macroeconomic variables, such as interest rates, inflation rate, money supply, oil prices, exchange rates, unemployment rate and industrial production are possible indicators to gauge the health of the economy and to predict the future returns on stock market. Then, there are actually successful economists that able to predict reasonably accurate on the downfall of the economy due to subprime crisis in United States. For example, Roubini and Mihm (2010) is good example, on relying of economic related knowledge able to warn investors on potential weaknesses in the economy, and thus, to make better preparation on the possible adverse situation of collapse of the stock prices. Overall, it can be also observed that many of the variables tracked and discussed by economists are also frequently incorporated into the research process of academicians. In the following section, the macroeconomic variables will be further discussed.
In the academic context, there are many factors being investigated and studied to understand the relationships of these factors to stock returns. One of the commonly studied models is indeed, the multifactor models – that is often designed to study if certain risk factors are being priced to explain the variation in stock returns in the real financial market. This section will be arranged as follow. Firstly, multifactor models will be introduced. Multifactor models can be categorized into many different types, namely: macroeconomic multifactor models, fundamental multifactor models and statistical multifactor models. The different categories of multifactor models will then be explained and discussed. Empirical researches and evidences concerning these multifactor models will be presented as well.
Risks and returns are the two key themes in finance or investment (Brigham & Houston, 2004). This is not surprising. Logically thinking, the higher the risks, investors simply demand higher expected returns to compensate for the higher risks involved. The tradeoff of risk and return is particularly obvious for investors. For example, when the risks associated with a stock is high; investors tend to shy away from that particular stock, causing it to become lowly priced. When the prices of that stock is low, then only certain investors may be willing to buy the stocks, to assume the higher risks for higher expected returns (due to the lower market prices for that particular stock). All of these notions are widely acknowledged by scholars and practitioners. Indeed, at the time of this writing, many theories have been postulated to attempt to explain the risk and returns relationship in financial market. One of the most promising framework of philosophies that researchers have been relying on to attempt to explain the risk and returns relationships in financial market is through application of multifactor model (Mateev & Videv, 2008).
Technically speaking, multifactor models are financial models that employ multiple factors (that are believed or perceived to be affecting stock returns) to explain stock returns. As discussed by Borys (2011), there are three different types of multifactor models, namely, the macroeconomic multifactor models, fundamental multifactor models and statistical multifactor models. To explain, in macroeconomic multifactor models, macroeconomic variables, those are often discussed by economists, such as interest rates, money supply, inflation rate, exchange rate, oil prices and others are employed to explain the variation of stock returns in a particular stock market. Then, fundamental multifactor models often employ the firms’ specific financial-related variables as the priced risk factors in explaining cross sectional stock returns. Lastly, statistical models, however, often are designed in order to compare the returns of various stocks based on the statistical performance of the respective stock in and of itself.
The many economic variables, often discussed and followed by economists are the frequent targets being investigated by scholars in studying these economic indicators relationships to stock returns. For example, Lettau and Wachter (2011) employed term structure of interest rates; real interest rates as well as inflation rates in explain stock returns. It is found that there are empirical evidences supporting that these macroeconomic variables are priced in explaining stock returns. Then, Borys (2011) had conducted a study to investigate the validity of macroeconomic multifactor models in explaining stock returns in Visegrad countries. The factors being employed in their model include the following: industrial production index, inflation rate, money supply, exchange rate, and commodity index and terms structure. It is found that the macroeconomic multifactor model developed indeed can explain the variance of stock returns in the Visegrad countries. Then, Mohanty & Nandha (2011) had also conducted a study to understand the changes in oil prices and its impacts to the stock returns of transportation industry in United States. It is found that the changes of oil prices are priced in explain stock returns of the transportation related companies in United States. However, the relationships between oil prices to stock returns of the companies being investigated differ across time frame and across companies. Then, a study by Al-Tamimi, Alwan & Rahman (2011) found that money supply and GDP is positive but not statistically significant related to stock returns in UAE financial markets. However, interest rates are non-statistically and positively related to stock returns. Nevertheless, consumer price index is statistically and positively weakly related to stock returns.
Overall, it s found that there are some empirical evidences supporting the idea that certain macroeconomic variables are priced in explaining stock returns. However, the empirical evidences are mixed and not consistent. In certain studies, there are no statistically significant relationships found between stock returns and macroeconomic variables. Thus, it can then be deduced and perhaps macroeconomic variables impacts to stock returns is weak, and cannot be easily observed.
Regarding the context of fundamental multifactor model, one of the commonly investigated multifactor frameworks is Fama-French three-factor asset pricing formula. Under the Fama-French three-factor model, two factors are included to explain stock returns. The first factor is often called the size premium, in which this factor represents the average returns of small firms compared to the large firms in a stock market. Then, the second factor is called the market-to-book-value ratio premium, as this factor is actually the differences of returns of stocks with low market-to-book-value ratio when compared to those stocks with high market-to-book-value ratio. As lower market-to-book-value ratio indicates that a firm is likely to be undervalued, this factor is also often perceived as the value premium by researchers (Cao, Parry & Leggio, 2011). Then, Lam & Li (2008) had conducted a study to investigated if the firm size factor (i.e., size premium) or value factor (i.e., market-to-book-value ratio premium) are being priced in Hong Kong. It is found that size premium is not a priced factor in explaining stock returns in stock exchange in Hong Kong. There are also limited empirical evidences that value premium is being priced in the stock exchange in Hong Kong. Overall, the author argued that different fundamental factors are responsible in affecting stock returns in Hong Kong. Similar results are also obtained by Wu (2011), in a study on risk factors prevalent in Shanghai stock exchange.
It is also interesting to found out that a study by Bondt (2008) provide empirical evidences that in the long term, stock returns are driven primarily by the fundamentals of the respective companies. According to empirical evidences from the study, the authors argued that in the short terms, macroeconomic variables such as exchange rate, commodity prices, momentum, and effects of seasonality may drive stock prices, but such impacts of macroeconomic variables are not permanent. In the long run, the fundamentals of the respective firms will determine the stock prices of the different stocks. Besides, it is also demonstrated that stock prices models employing fundamental variables will likely to outperform random walk model. Then, a study conducted by Cheng (2006) found that financial ratios are significantly related to the future returns of initial public offerings (IPOs) in Taiwan stock market. Specifically, cash flow related ratios as well as liquidity related ratios are useful in explaining future stock returns of the IPOs. In a nutshell, such findings encourage stock investors to further investigate the usefulness of fundamental variables in predicting stock returns in the highly complex and dynamic financial market.
Earnings related fundamental variables are often employed by scholars in studying stock returns behaviors. For instance, Campello & Chen (2010) found that financial constraints are significantly priced in financial market. To explain, it is found that the business fundamentals of financially constrained firms, such as the respective capital spending and operating earnings are sensitive to macroeconomic movement and is related to asset returns of the respective companies. Then, a study by Sorensen & Ghosh (2010) found that variables such as earnings power, earnings momentum, earning announcements as well as earnings surprises are related to variations in stock prices. It is articulated by the researchers that earnings are perhaps the most crucial variables affecting stock valuations and hence, stock returns. It is shown that those stocks that post positive earnings above expectation outperform those that stocks that perform below expectations. The differentials in stock returns are significant and consistent throughout the years, particularly, when the returns of stocks are investigated from a longer time perspectives.
Aside from earnings, other profitability related ratios or variables are often employed to investigate stock returns as well. For example, a study conducted by Sharma & Preeti (2009) found that traditional and famous fundamental ratios that are related to firms’ profitability, cash performance, operating efficiencies and liquidity are not statistically significant in explaining stock returns. However, other growth related financial variables, such as growth of earnings, research and development, and advertising expenditure are statistically significant in explain future stock returns. The authors suggested that fundamental analysis should then focus on growth prospect and it is possible to base on the growth of a firm to uncover potentially high performing stocks in the future. Then, cash flow is also often employed in the study of stock returns. According to a study carried out by Da (2009), it is found that the fundamental cash flow characteristics of firms are significant in explaining the cross sectional variation in stock returns. Then, a study by Clubb & Naffi (2007) found that the famous return on equity (ROE) figure is useful in explaining cross sectional stock returns in United Kingdom. The authors subsequently commented that fundamental valuation based on firm specific characteristics is important in explaining or predicting stock returns. Next, a study conducted by De Peña, Forner & López-Espinosa (2010) found that variables such as return on capital are statistically significant in driving the stock returns in Spanish stock market. It is then argued by the authors that return on capital is a powerful fundamental variables, that can be used as the proxy of fundamental of companies, to guide investors’ decision making process in Spanish stock market.
Dividends related information is also being employed by scholars in studying behaviors of stock returns. For example, Campbell & Ohuocha (2011) had performed an event study on whether stock dividend announcement may create value for companies traded in Nigeria. Their research findings suggested that companies that choose their own announcement date outside the Nigerian stock exchange announcement window tend to experience positive abnormal returns if the stock is more frequently traded. In other words, their findings provide important empirical evidences that stock prices tend to react to dividend announcement in Nigeria. Then, a study conducted by Brzeszczynski & Gajdka (2007) found that a portfolio of consistent dividend growth outperform the market index significantly. Their findings strongly suggest that dividend is indeed an important element in fundamentals of a stock in driving stock returns, particular in Polish stock market, as being investigated in their study.
There are also other scholars using other variables to investigate or predict stock returns. For example, Edmans (2011) employed employee satisfaction explain long term stock returns in United States. He found that those companies that had been awarded as one of the ‘Best Company to Work for in America’ indeed earned an abnormal returns of 2.1% above market average returns. Besides, it is also found that the companies with greater employee satisfaction tend to exhibit earnings surprises as well as announcement returns.
Then, in a study conducted by Bai & Green (2011), countries specifics variables are employed to study behaviors or characteristics of stock returns in a total of 13 emerging countries. Some non-popular factors, such as the degree of financial sector development, industrial concentration, geographical concentration and the legal environment are employed to explain the variation of cross sectional stock returns. In their study, it is found that different legal origins can explain the variation in stock returns in a particular country. This indicates that country specific characteristics are influential as well in explaining stock returns. However, the authors concluded that more studies are required to draw more conclusive discussions on how the different country specific factors may affect stock returns.
Aside from that, there are also scholars employing variables such as investor recognition in studying stock returns. Lehavy & Sloan (2008) found that investor recognition of a firm’s stock can explain relatively well on the variation of stock returns when compared to variables such as earnings and cash flow in the short term. The scholars concluded that in the short run, stock returns are positively related to changes in investor recognition. However, the future stock returns are negatively related to changes in investor recognition. Overall, based on their findings, the scholars suggested that investors should also incorporate issues such as investor recognition, apart from employing fundamental analysis in valuing firms in their investing decision making process.
Overall, this chapter had review the different factors often incorporated by the practitioners, economists and scholars in predicting stock returns, decision making related to stock investment as well as in studying the relationships between these factors to stock returns. It is founds that, for the successful investors, most factors being considered are related to firm specific variables. Firstly, it is observed that the successful investors tend to look highly on the consistency of earnings track records. Then, the profitability ratios are also being studied and emphasized as crucial in making successful investment decisions in stock selection. Next, it is also understood that price multiples are commonly used to judge the relative attractiveness of a stock in the financial market. Then, other crucial financial variables often perceived as important include the following: debt to equity ratio, return on equity and growth in revenues for the long term.
For the economists, macroeconomic variables that are perceived as influential and useful in estimating stock returns include interest rates, inflation rate, money supply, oil prices, exchange rates, unemployment rate and industrial production index. Then, it is found that scholars tend to employ both the views of the successful investors as well as the economists in their researches. Multifactor models are often designed and employed to understand the factors priced in explaining variation in stock returns. There are three different types of multifactor models, namely, the macroeconomic multifactor models, fundamental multifactor models and statistical multifactor models. A review of the existing literature found that there are some empirical evidences supporting the idea that certain macroeconomic variables are priced in explaining stock returns. However, the empirical evidences are mixed and not consistent. In certain studies, there are no statistically significant relationships found between stock returns and macroeconomic variables. However, the multifactor models are found to have better explanatory power, as discussed by Bondt (2008) and Cheng (2006). Among the fundamental factors that are found to be statistically significantly priced in explaining stock returns include the following: earnings, price multiple, profitability ratio, debt to equity, dividend related information as well as future cash flow.
Apparently, the views of the successful investors are justified and valid, as there are empirical evidences supporting the idea that the factors considered to be crucial in stock investment process by the successful practitioners are indeed priced in explaining stock returns. The empirical evidences regarding usage of macroeconomic variables to study stock returns are however, less satisfactory. Nonetheless, all of the findings presented above, strongly suggest that there are certain factors that can be used to explain stock returns. To study and research the market, indeed, can be profitable. Market may not be as random or as hard to beat as suggested by academicians that adopted the random walk theory or of efficient market hypothesis. In the next few chapters, factors that are probably useful in explaining stock returns will be further investigated.