The influence of behavioral biases and culture on
investor reaction to analyst recommendation revisions:
Evidence from the G7 countries
Alexandra Maria Stroe
Master Thesis
MSc IB&M – International Financial Management
Supervisor: Dr. B. Qin
Co-assessor: Dr. W. Westerman RIJKSUNIVERSITEIT GRONINGEN
FACULTY OF ECONOMICS AND BUSINESS
UPPSALA UNIVERSITET FACULTY OF SOCIAL SCIENCES
Abstract: The game investors play in the financial markets has represented the focus of research for many finance practitioners. Researchers have been motivated at the same time by the profits that can be obtained from discovering a successful long-term trading strategy and by the empirical anomalies documented in the markets. The main theory surrounding this topic is the Efficient Market Hypothesis (EMH) stating that information is timely and accurately reflected in the prices in such a manner that investors cannot profit in the long run. Nevertheless, some empirical proof states otherwise. This paper argues that the cultural dimensions of individualism/collectivism and uncertainty avoidance developed by Hofstede (1983) have an impact on the behavioral biases of investors which will translate in variations across cultures in the manner in which investors react to analyst recommendation revisions, both as transmitted in the magnitude of abnormal returns and abnormal trading behaviors. Results for the G7 countries for the period September 2007 – September 2011 support this hypothesis.
Key words: Analyst recommendation revision, overconfidence, biased-self attribution, herding, individualism, collectivism, uncertainty avoidance, buy-and-hold abnormal return, abnormal trading volume and the G7 countries.
Acknowledgements:
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1.
RESEARCH OUTLINE
1.1. Introduction
Understanding investors’ behavior in international financial markets and finding profitable trading strategies that can provide sustainable returns has been one of the main preoccupations in finance. The main theory surrounding this field of research is the Efficient Market Hypothesis (EMH), introduced by Eugene Fama (1965), stating that stock prices reflect all available information in a timely and accurately manner and therefore no long-term profitable strategy can be found. The assumption that people are rational and behave accordingly is a well-known idealization in the field of finance, representing the foundation of most important economic and financial theories. This approximation of the reality is motivated by the argument that departures from rationality will quickly disappear under the discipline of the market (Kahneman, 2003). Nevertheless, this does not happen in all the cases. Zajonc (1980) argued in this respect “We sometimes delude ourselves that we proceed in a rational manner and weigh all the pros and cons of the various alternatives. But this is probably seldom the actual case”.
For example, momentum strategies that suggest buying well performing stocks and selling poorly performing stocks (Jegadeesh and Titman, 1993) or strategies based on overreaction or underreaction to company specific events (Daniel et al., 1998; Barberis et al., 1998), although criticized for not taking into account transaction costs, did provide significant excess risk-adjusted returns. More relevant evidence is represented by the equity premium puzzle1 or by the January effect2, which nevertheless disappeared after being identified. Even so, success stories do exist and the legendary Warren Buffett’s strategy of investing in value stocks (stocks with low price-earnings multiples) as opposed to growth stocks is one of them. At the same time,
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1 In order to reconcile the much higher returns of stocks compared to government bonds in the United States,
individuals must have implausibly high-risk aversion according to standard economics models.!
2The January effect is a calendar-related anomaly in the financial market where financial security prices increase in
irrationality of the market was more than obvious in cases such as the “crash of 1987” or “the Internet bubble”.
Behavioral Finance complements the teachings of the EMH by relaxing the assumptions of the latter and arguing that given the complexity of the marketplace, the decision making processes of investors are often influenced by psychological factors. This “open-minded approach” (Thaler, 1993) towards finance has therefore emerged as a response to the need of modeling financial markets, with less than fully rational investors. Behavioral finance gained an extraordinary explanatory power, which proved extremely useful in situations where traditional finance failed to correctly predict market interactions. “Psychological forces influence individual and group behavior in many contexts. So we argue that to capture important features of accounting, we must go beyond the assumption of perfect rationality” (Hirshleifer and Teoh, 2009).
Previous research has tried to explain the documented market anomalies by arguing that in order to cope with the complexity of the decision making process, investors employ most of the times shortcuts, which allows them to develop estimations before fully analyzing all the available information. This process, also called “heuristic simplification” (Baker and Nofsinger, 2002), causes numerous psychological biases. To offer one example, overconfidence is one of the most-studied biases and can be understood either as overestimation of one’s abilities or as an excessive trust in the truth and accuracy of one’s beliefs. Nevertheless, behavioral finance until now has only been credited explanatory power and has been criticized for not being able to provide a consistent, widely accepted predictive solution for deciphering the game of the stock markets. “Although it is not obvious how the empirical securities market phenomena can be captured plausibly in a model based on perfect investor rationality, no psychological theory for these phenomena has won general acceptance” (Daniel et al., 1998).
backgrounds behave, form their decisions and the manner in which they organize their trading strategies. The aim of this paper is therefore to study how cultural dimensions which identify and characterize members of different nations influence the impact of the behavioral biases on the decision making process of individuals, in particular of investors trading on the stock exchanges. To document this, the paper will focus on investor reaction around the announcement of company specific events as transmitted in analyst recommendation revisions. Analyst recommendations were chosen because they are considered to be the most straightforward and less noisy way of transmitting information to the market. Specifically, Elton et al. (1986) note that analysts’ recommendations provide “a clear and unequivocal” signal of a course of action. As a result, the direction and magnitude of the revision can be quickly included in the decision making process of the investor. Analysts’ recommendation revisions have also been found to have predictive power for future stock returns (Womack, 1996; Jegadeesh et al., 2004). Finally, there is no research until now studying the impact of cultural factors and behavioral biases on investors’ reaction to analyst recommendation revisions. The present study will focus on revisions (changes) in analyst recommendations and not on the level of the recommendation as numerous tests show that the predictive power of the former is more robust than the predictive power of the latter (Jegadeesh et al., 2004; Jegadeesh and Kim, 2006). Moreover, recommendation changes are thought to capture also qualitative aspects of a firm’s operations, such as managerial abilities, growth opportunities etc. that cannot be observed in quantitative signals, which is consistent with analysts’ claim that they bring new information to the market (Jegadeesh et al., 2004).
addressed in this paper. The individualism index is regarded as the most comprehensive in terms of both the range of countries and the number of respondents involved (Kagitcibasi, 1997). Moreover, both dimensions can be logically connected to investor psychology, as documented also by previous literature (Chui et al., 2010; Schmeling, 2009). The use of these dimensions is also motivated by their popularity and wide acceptance among researchers in an extensive range of business disciplines.
Although culture is considered to be extremely important for the effective study of financial phenomena (Reuter, 2011), there has been limited research on the explanatory power of culture in the field of finance although existing studies clearly show that the value systems of countries play a crucial role in the decision-making processes of market participants (Chui et al., 2002; Stulz and Williamson, 2003). Chui et al. (2010) show that national culture (in particular, individualism) drives certain behavioral biases that are assumed to generate momentum profits. Schmeling (2009) extends this result, showing how behaviors may be related not only to individualism, but to collectivism (its opposite) as well, and to uncertainty avoidance. Dou et al. (2010) provides the first study (not published yet) investigating the association between the cultural dimensions of individualism and uncertainty avoidance to earnings momentum profits. Mcknight and Todd (2006) acknowledge the possibility that institutional differences in banking, research or legal and cultural differences across countries explain the neutral performance of their documented trading strategy in Northern Europe. Nevertheless, there is no study until now documenting the impact of cultural factors on investors’ decisions when faced with analyst recommendation revisions.
(September 2007 until and including September 2011) and continuing the work of papers such as the one of Jegadeesh and Kim (2006), which focuses on the same group of countries (G7). Representing some of the most developed stock markets in the world, the study is also expected to be characterized by increased transparency and accuracy in the recommendations provided by analysts and timely adjustments of stock prices. If, nevertheless abnormal returns will be documented, the results could be easily attributed to psychological and cultural factors more than to the inefficient mechanisms of the financial markets. Moreover, as the main focus of this study is the impact of cultural differences on investors’ reactions, as explained by psychological motivations, the choice of the sample is also motivated by the diversity in cultures among the countries as expressed in the individualism index and uncertainty avoidance Index.
In terms of practical relevance, this topic helps in understanding what matters to investors as they form valuation judgments and in predicting how they will likely react to different recommendation revisions depending on their cultural background. Moreover, it can help managers in organizing a strategy by which they can time information releases to the market and offer them hints of how they can differentiate this strategy across the countries where the company operates.
1.2. Research Question
Given the before-mentioned findings and observations, the main research question that this study will attempt to answer, among other secondary effects can thus be formulated as follows:
Do the cultural dimensions of individualism/collectivism and uncertainty avoidance influence the impact of behavioral biases on investors’ reaction to analyst recommendation revisions?
1.3. Structure of the paper
2.
LITERATURE REVIEW
2.1. Behavioral Finance and the Impact of Culture
2.1.1. The role of behavioral biases for the decision process of investors
People face decisions all the time. Understanding the reasoning behind their decision making process or predicting future behaviors is nevertheless a very difficult task, because while they are always “human”, people may not be in all the cases “rational” (Baker and Nofsinger, 2002). The key to successful judgment resides in the ability to interpret and integrate information appropriately. People must weigh information differentially, according to relevance and must be able to qualify their interpretation of a given fact. Understanding decision processes does not come easily as all too often, expert judgment is regarded as a mysterious, intuitive phenomenon, incapable of being described precisely (Slovic, 1969). Hirshleifer and Teoh (2009) suggest that investors are affected by various psychological biases in different circumstances and that research must leave behind the notion of perfect rationality in the markets.
Several explanations have been put forward to document the observed anomalies in the market and to further be able to make predictions about the decisions that investors will take. Some research indicates that judgments about risk may also be the result of subconscious evaluations (see Loewenstein et al., 2001). One of the most researched behavioral biases is overconfidence and extensive evidence shows that people are overconfident in their judgments (Barberis and Thaler, 2003). One reason that people may be overconfident about their financial predictions is that they have positive ‘feelings’ about financial products, which are based on information that subconsciously enters their judgmental process. Erb et al. (2002) explain that investors may have a positive ‘image’ of a market sector such as ‘major pharmaceuticals’ because this sector is associated with ‘healing’, ‘beauty products’, ‘cleanliness’, and so on, whereas another sector such as ‘railroads’ may be associated with negative features such as ‘dirty’, ‘old’, ‘used by poor people’ etc.
with the alien and distant (Huberman, 2001). “For individual investors stocks are usually more than just the abstract bundles of returns of our economic models. Behind each holding may be a story of family business, family quarrels, legacies received, divorce settlements and a host of other considerations almost totally irrelevant to our theories of portfolio selection” (Miller, 1986). Daniel et al. (1998) use overconfidence and biased self-attribution to model investor behavior. The result is that investors hold too strongly to their own information and discount public signals. Barberis et al. (1998) rely on conservatism and the representativeness heuristic when they hypothesize that investors change sentiment about future company earnings based on the past stream of realizations and Coval and Shumway (2005) propose loss aversion for explaining the short term behavior of investors.
Therefore, while behavioral finance can be modeled to explain and even predict different anomalies in the market, it is not yet obvious how the empirical securities market phenomena can be captured plausibly in a model based on the teachings of this newer field of study. As no psychological theory for these phenomena has won general acceptance (Thaler, 1993), this paper suggests looking at the drivers of behaviors: what makes people think and behave in different ways? One of the answers and the most important in my opinion resides in cultural differences. Culture is deeply rooted in every society and has relatively stable and long-term effects on how individuals perceive the world, think, and make decisions (Hens and Wang, 2007). Differences among cultures are thought therefore to help in understanding individual decision-making process and the relations between individuals that affect their decisions.
2.1.2. The impact of culture on the decision making process of investors
of understanding the influence of the cultural background on economic decision-making. The authors propose that importing cultural competence into behavioral models can lead to cognitive debiasing, both on the short and long term.
The impact of culture on the world of behavioral finance has been measured empirically as well. The working paper of De Jong and Semenov (2002) study the cultural impact on the functioning of stock markets in US, Japan, Australia, New Zeeland and other 14 European countries by acknowledging that stock markets are more important in countries where people accept more uncertainty and view competition as a proper way of interacting, fostering efficiency into the markets. The national scores on the dimensions of uncertainty avoidance and masculinity are the ones employed by the study to translate these attitudes. Even more relevant, Chui et al. (2008) demonstrate that national culture can explain the variation in international price momentum profits for 41 countries. It is the first study in the finance literature to use Hofstede’s individualism index. They show that individualism is correlated with trading volume and volatility and provide evidence that countries scoring high on Hofstede’s (2001) individualism index yield greater momentum returns. In particular, the study shows that momentum profit strategies are strong in US stock markets, and weak in many Asian countries. The proposed explanation is that the ability to derive momentum profits may be, partly, driven by the individual mechanisms of over-confidence and self-attribution biases, which in turn are both fostered by and positively associated with the degree of individualism in a given country. Schmeling (2009) extends these results and investigates the relation between investor sentiment and future stock returns for 18 countries. Collectivistic countries and countries with a high uncertainty avoidance index show larger effects of sentiment on returns compared to individualistic and low uncertainty avoidance countries. In addition to the study by Chui et al. (2008), he shows how these behaviors may be related not only to individualism, but to collectivism and to uncertainty avoidance, as well.
overconfidence and trading volume is more straightforward: overconfident investors trade more, because they overestimate the precision of their information (Chui et al., 2010). On the other hand, in what concerns the stock markets of collectivistic societies, people are better integrated into strong groups and tend to overweigh consensus opinion, which therefore implies a higher influence of investor sentiment, through the impact of “herd-like behavior” or correlated behavior across individuals (Schmeling, 2009).
Dou et al. (2010) also expand on the work of Chui et al. (2010) by examining the influence of individualism and uncertainty avoidance on earnings momentum profits, using data from 41 countries. The study shows that the level of individualism in a country is positively associated with earnings momentum profits and that the level of uncertainty avoidance is negatively associated with earnings momentum profits. The model relies again on the behavioral biases of overconfidence and self-attribution bias for explaining the impact of the individualism dimension. Uncertainty avoidance measures individual tolerance for ambiguity and therefore the rationale behind the documented relation is that people in uncertainty-avoiding cultures are more anxious and take faster action so to decrease the level of ambiguity, which also decreases the price momentum phenomenon (Dou et al., 2010). While in the Chui et al. (2010) study, the association between price momentum returns and uncertainty avoidance is non-significant mainly because of the existence of a noisy range of macroeconomic information, the results are different in the case of earnings momentum. Because earnings surprises are less noisy than general economic indicators, the direction and magnitude of earnings surprises can be relatively quickly included into an investor’s decision-making processes and therefore the study proves a significant relationship between uncertainty avoidance and earnings momentum.
concerned with the quality of life” (Hofstede, 2001). Masculinity can be associated at most with well functioning stock markets, where competition is viewed as the proper way of interaction (De Jong and Semenov, 2002) but this can be hardly translated in any expected impact on investors’ reaction to public news such as analyst recommendation revisions. The power distance dimension refers to the extent to which national cultures expect and accept that power is distributed unequally in society. In high power distance societies, for example, people will more likely expect and accept inequality and severe hierarchies (Hofstede, 2001) but this can hardly have any psychological influences on investors’ reactions in the stock market. Finally, the fifth dimension, long/short term orientation is more than a time dimension as Hofstede (2001) observes: “Long term orientation stands for the fostering of virtues oriented towards future rewards, in particular perseverance and thrift. It’s opposite pole, short term orientation, stands for the fostering of virtues related to the past and the present, in particular, respect for tradition, preservation of ‘face’ and fulfilling social obligations.” The complete definition includes several more characteristics and values of this dimension are not provided for all the countries included in the Hofstede study. Any association to the present analysis is therefore difficult to construct.
2.2. Investors’ reaction to analyst recommendation revisions
2.2.1. US Empirical Evidence
Previous research related to analyst recommendations has mainly focused on documenting the market response to these news announcements, especially for stocks traded in the American market. Stickel (1995) and Womack (1996) find a significantly positive (negative) stock reaction to upgrades (downgrades), with the market response to downgrades being more severe. A theoretical explanation for this finding can be represented by the fact that downgrades are less frequent and more visible than upgrades. While upgrades usually incorporate fewer potential costs, analysts are aware that releasing a negative recommendation can be more costly due to potential losses in advisory fees, worsened brokerage firm – investment banks relationships, or limited flow of information from top management and investment contacts (Womack, 1996). Both studies focus on the period 1988 – 1991 and are among the first studies to acknowledge the importance of the reputation of analysts and size of the firms in the decision making process of the investors. Stickel (1995) explains that position on the All-America Research Team is an important determinant of analyst pay and reputation and their financial forecasts are found to be more accurate. Also, literature relates size (usually measured by the market value of stock) with differences in firms’ information environments, which translates in a greater impact in the case of smaller firms because information is in this case gathered and processed less frequently and therefore the impact of one piece of information is larger. Green (2006) follows Womack’s example when documenting only reactions to recommendation revisions from top brokerage houses and finds proof of abnormal returns even after controlling for transaction costs.
recommendation, while the former trade more to the occurrence of the recommendation. Also focusing on the response of different types of traders to analyst recommendation revisions, Malmendier and Shanthikumar (2007) observe that while small traders follow recommendations literally, large traders exert buy pressure following ‘strong buy’ recommendations and selling pressure following ‘hold’ recommendations, while no reaction is documented for ‘buy’ recommendations. The reasoning behind this is that while small investors do not fully account for analyst incentives, as captured by type of recommendation, analyst affiliation etc., large investors are more sophisticated processors of information.
Barber et al. (2001) research the impact of consensus recommendations and changes in consensus recommendations rather than looking at detailed, individual revisions as previous researchers did. They observe that purchasing (selling short) stocks with the most (least) favorable consensus recommendations, combined with a strategy of daily portfolio rebalancing and a timely response to recommendation changes, yield annual abnormal gross returns greater than 4%. Nevertheless, high trading levels are required to capture the excess returns generated by the analyzed strategies. Jegadeesh et al. (2004) also focus on consensus recommendation levels and changes in the consensus recommendations. However, they also account for differences in the type of stocks recommended, which leads to somehow different results. Consensus recommendations add value only for stocks with favorable quantitative characteristics such as value stocks and positive momentum stocks while worse returns are obtained for stocks with unfavorable quantitative characteristics. Contrary to other findings, they observe that the marginal predictive ability of the level of analyst recommendation is not significant and they anchor this finding in analysts’ failure to quickly downgrade stocks rejected by other investment signals.
sentiment and behavioral biases for the trading patterns of different types of investors when reacting to different types of news. Therefore, small investors’ net buying is positively related to the bullishness of the market sentiment and this is especially true for bad news. On the other hand, sentiment hardly has an impact on large traders’ response to firm news. The theoretical explanation can be again related to the fact that larger investors are more sophisticated processors of information.
Kliger and Kudryavtsev (2010) also study the impact of behavioral biases on investors’ reaction to analyst recommendation revisions by acknowledging the importance of the availability heuristic3. They find that positive stock price reactions to recommendation upgrades are stronger when accompanied by positive stock market index returns and negative stock price reactions to recommendation downgrades are stronger when accompanied by negative market index returns. This can be theoretically explained by the concept of outcome availability: high availability of gain and loss outcomes under financial uncertainty may result in amplified stock price reactions to contemporaneous analyst recommendation upgrades and downgrades. Moreover when financial risk is available in the presence of substantial stock market moves, abnormal stock price reactions to upgrades are weaker and abnormal stock price reactions to downgrades are stronger. This finding may be explained by risky investment scenarios made available to investors on such days, which are psychologically associated with fewer benefits for investors.
2.2.2. International Empirical Evidence
International studies are not as numerous as the ones performed for the American market, but they nevertheless portray different results in stock price reaction depending on the geography of the investment. Jegadeesh and Kim (2006) study the impact of recommendations revisions in the G7 countries (US, Britain, Canada, France, Germany, Italy and Japan). They observe that stock prices react significantly to recommendation revisions on the day of recommendation and on the following day in all the countries mentioned except Italy. Stock prices continue to drift up for upgrades and down for downgrades over the next two to six
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3!The!availability heuristic is introduced by Tversky and Kahneman (1974) and refers to!people’s tendency to
months. They find the largest price drifts in the US, followed by Japan. The most important explanation proposed for these findings is that US analysts identify larger mispricings because of their skills, rather than because the US market is less informationally efficient than the other markets.
Moshirian et al. (2009) perform a similar study for emerging market countries (Argentina, Brazil, China, Chile, Hungary, India, Indonesia, Israel, Korea, Mexico and South Africa) and observe a strong reaction to stock analyst recommendations and revisions on the event and following day. On average, emerging markets show a greater magnitude of return following both upgrades and downgrades compared with G7 countries. One possible theoretical explanation is again related to the observed positive bias in analyst recommendations. In this study the proportion of strong buy and buy recommendations on a yearly basis are at least double the number of underperform, sell and strong Sell recommendations and while analysts’ favorable bias is less severe than in the US, it is more prominent than in other G7 countries. Moreover, in the cross-section analysis, it is shown that the market-to-book ratio explains an important part of the buy-and-hold abnormal returns. As market-to-book ratio is a key indicator of the firm's growth prospects, the results indicate that the market and stock analysts respond more favorably to firms with higher growth opportunities.
3.
HYPOTHESES DEVELOPMENT
3.1. Individualism/Collectivism and investors’ reaction to news
“Individualism versus collectivism” is the first dimension introduced by Hofstede (1983), a bipolar construct, reflecting the degree to which individuals are integrated into groups or the degree to which people in a country tend to have an independent rather than an interdependent self-construct4 (Hofstede, 2001). On the individualist side, there are societies in which the ties between individuals are loose and everyone is expected to look only after themselves and the immediate family. On the collectivist side, from early ages, people are integrated into strong, cohesive groups and often their extended families continue protecting them (Hofstede, 1988). Markus and Kitayama (1991) observe that in many Western, individualistic cultures, “there is a faith in the inherent separateness of distinct persons” and individuals view themselves as “autonomous, independent persons” who think positively about themselves and focus on their own internal attributes. On the other hand, in collectivistic cultures, the self becomes meaningful and complete only when taken in the appropriate social relationships. In this case individuals view themselves “not as separate from the social context but as more connected and less differentiated from others” (Markus and Kitayama, 1991). To offer a practical example, while people in individualistic cultures, such as the United States, tend to believe that their abilities are above average, people in collectivistic cultures, such as Japan, are more humble and do not have this belief.
The cultural dimension of individualism/collectivism is expected to have a significant impact on investment behavior, as observed in investors’ reaction to analyst recommendation revisions. This impact would be intermediated by behavioral biases characterizing the decision process of investors. Culture can have a great impact on people’s attitudes toward risk, it can influence probabilistic thinking, it can result in a tendency towards overconfidence or self-attribution bias which in turn may manifest itself as different patterns in investment behavior as well as market trends across countries and regions (Hens and Wang, 2007). There are several more examples in previous literature relating individualism with overconfidence: Chui et al. (2008), Schmeling, (2009) and Dou el al. (2010). The connection between the two groups of individuals can also be
easily spotted from the previous features characterizing individualistic people: “autonomous, independent persons”, who think positively about themselves and focus on their own internal attributes, who tend to believe that their abilities are above average.
Nevertheless, there is no consensus in previous literature in what concerns the relationship between the cultural dimension of individualism/collectivism and stock returns or abnormal trading volume. Shefrin (2000) argues that there are two main implications of investor overconfidence: the first one relates to the nature of their bets while the second one refers to their trading behavior. Individualism and implicitly overconfidence influence therefore both returns and trading volume. It is also very important to differentiate between the impact of firm-specific news on returns and on abnormal trading volume. Beaver (1968) explains that while stock price changes reflect the average change in investors’ beliefs about a news, trading volume reflects individual investor’s interpretation of the announcement.
Firstly, the average impact of individualism and related overconfidence on investment decisions, as translated in abnormal returns will be analyzed. Overconfident investors have superior decision-making abilities and view themselves as more capable than their peers, which can make them take bad bets as they fail to realize that they are at an informational disadvantage (Shefrin, 2000). Psychologists have observed that overconfidence causes people to overestimate their knowledge, underestimate risks and exaggerate their ability to control events, and this especially translates in investment decision-making (Nofsinger, 2001). Investors belonging to more individualistic countries are more likely to be associated not only with overconfidence but also they tend to be characterized by the self-attribution bias (Daniel et al., 1998 and Chui et al., 2010). The model presented by DeBondt and Thaler (1995), relying on overconfidence and self-attribution bias explains that people overestimate their own abilities, perceive themselves more favorably than they are viewed by others, and attribute success to their own abilities and failure to exogenous factors, which offers them the opportunity to motivate their bad investment decisions and pride themselves on good results.
news from expectations, it may be possible that overconfident investors will only react to news that are in line with previous expectations and discount other public information. Dou et al. (2010) differentiate between two kinds of information: public and private and explain that individuals from individualistic societies will demonstrate underreaction to public information and overreaction to private information arising from self-confidence and self-attribution. Upon an analyst revision, investors’ expectation as regards the stocks of interest will change accordingly. More specifically, as recommendation revisions are considered public news, this will translate in an inverse relationship with abnormal returns. Investors from high individualistic societies turn to be overconfident about their own judgment instead of others (including analysts). Therefore, their expectations are likely to diverge, which would lead to diverging investing strategies that will offset each other and in turn lead to a weak overall market response.
Secondly, the individualism dimension as translated by overconfidence also affects decision making at individual level as translated in the abnormal trading volume. People in individualistic cultures tend to overestimate their own abilities relative to the others, to maintain and promote esteem (Markus and Kitayama, 1991), which makes them prone to self-attribution as well as to overconfidence (Kagitcibasi, 1997). Nurmi (1992) acknowledges, “This cross-cultural difference in self-attribution bias is typically explained by Western individualism and the collectivist orientation of Eastern cultures”. Yates, Lee, and Shinotsuka (1996) state that while individualism is clearly related to peer-comparison overconfidence, it is not necessarily related to overconfidence about general knowledge. Van den Steen (2004) further supports this idea by acknowledging that it is overconfidence about one’s success relative to others that drives investors’ trading actions. Moreover, Sherin (2000) argues that overconfident investors trade more frequently than is prudent which leads to excessive trading volume. Shiller (2000) also explains that overconfidence, however generated, appears to be a fundamental factor promoting the high volume of trade. “Without such overconfidence, one would think that there would be little trading in financial markets.”
Arguing from the side of collectivistic societies, Weber and Hsee, (1998) propose a theory entitled “the cushion hypothesis” which argues that people from countries scoring low on the individualism index (collectivistic countries) are more relaxed towards risk as they always rely on the help of their close family/group. In the event of news announcements such as analyst recommendation revisions, investors from collectivistic societies will adopt a more relaxed strategy both because they are motivated by considerate profits which can only be obtained form long-term investments and because even in the case of short-term losses they will be able to count on the financial support of their close peers. This will therefore be translated in their decision to trade and it is expected that collectivistic societies will trade less when confronted with news on the market. Following this line of reasoning, one can argue that collectivistic societies as opposed to individualistic societies would react slower to analyst recommendation revisions, which would translate in lower trading volumes.
3.2. Uncertainty Avoidance and investors’ reaction to news
Another relevant dimension, introduced by Hofstede (1988) is “uncertainty avoidance”, which indicates to what extent a culture programs its members to feel either comfortable or uncomfortable in “unstructured situations”, which are translated as being novel, unknown, surprising, different situations as compared to the usual ones. In other word, uncertainty avoidance refers to the extent to which people feel threatened by uncertainty and ambiguity and try to avoid it or protect themselves against it. In order to do that, they adhere to strict laws and rules, to safety measures, and a belief in absolute truth. People in societies high on uncertainty avoidance perceive uncertainty inherent in life, as a continuous threat that must be fought (De Jong and Semenov, 2002). In their quest to reduce ambiguity, they are motivated by security more than by achievement. Translated in the context of investors’ behavior on the stock markets, traders coming from high uncertainty-avoidance cultures would be more motivated in their actions by the threat of losing (therefore, by holding a safe position) than by earning excess returns. Uncertainty avoidance is linked to a society characterized by people’s preferences for rules, stability, and uniformity and closely related to psychological traits, widely discussed in behavioral financial economics such as conservatism and risk aversion. Identifying the connection between this cultural dimension and financial decisions, both Schmeling (2009) and Dou et al. (2010) employ the uncertainty avoidance index as a rough proxy for the tendency of individuals to react to news in the market.
a course of action (Elton et al., 1986), even more transparent than the signal provided by earnings surprises. As it is the case with the first studied cultural dimension, uncertainty avoidance would also translate both in returns and in trading volume. The impact on returns will be reflected in the average change in investors’ beliefs about news in the market, while the trading volume reflects individual investor’s interpretation of the announcement (Beaver, 1968). Firstly, the relationship between uncertainty avoidance and returns will be studied. Gray (1988) demonstrated that conservatism could be linked most closely with the uncertainty avoidance dimension. A preference for a more conservative attitude is consistent with strong uncertainty avoidance following from a concern with security and a perceived need to adopt a cautious approach to cope with the uncertainty of future events. Moreover, uncertainty avoidance also relates to a high level of risk aversion as translated in their reaction of resistance to everything that is new and reluctance to radical change. Investors from high uncertainty-avoiding countries are likely to be more conservative and more risk-averse. Nevertheless, it is not to be understood that investors in high uncertainty avoidance countries will not respond to news announcements (on the contrary, as it will be argued in the following paragraphs high uncertainty avoidance is expected to be translated in increased trading activity). As previously argued, high uncertainty avoidance is translated by high-risk aversion and this will be reflected by investors tendency to update their responses to analyst recommendation revisions. Nevertheless, uncertainty avoidance is also translated by high conservatism, which limits the strength of investor reaction (Barber et al., 1998). People from high uncertainty avoidance countries are motivated by security or threat of losing (e.g.: by holding a safe position) more than by achievement (e.g.: earning excess returns). Stability and uniformity is what dictates their actions and this results in weaker reaction to news. Investors scoring high on the uncertainty avoidance dimension will avoid volatile stocks, which will translate in low market returns (price changes) after analyst revisions.
H3: The degree of uncertainty avoidance characterizing investors from a country negatively influences the strength of their reaction to analyst recommendation revisions as translated in abnormal returns.
more emotional and more motivated by inner nervous energy (Hofstede, 1988), which can explain an increased reaction to news on the market especially in the context of uncertain circumstances. People in societies with low uncertainty avoidance accept uncertainty inherent in life much more easily and are more tolerant of behaviors and opinions that differ from their own; they are more indifferent and contemplative (Hofstede, 1988). Cyert and March (1963), state that people in uncertainty-avoiding cultures emphasize short-run reaction rather than anticipating run uncertainty and solve pressing problems rather than developing long-run strategies. As expected, the opposite holds for people with high tolerance for uncertainty (Dou et al., 2010). Hofstede (2001) further documents that people with low uncertainty avoidance often exhibit a low sense of urgency in ambiguous, unstructured situations, whereas people in uncertainty-avoiding cultures feel more anxious in such situations, and as a consequence tend to take immediate action to reduce the level of ambiguity. This prediction is expected to be reflected in their trading reaction to analyst recommendation revisions. As a result, investors in a high, relative to low, uncertainty-avoidance culture are likely to update their beliefs and trade more quickly to reduce the anxiety associated with recommendation revisions. Therefore,
H4: The degree of uncertainty avoidance characterizing investors from a country positively influences their trading behavior in response to analyst recommendation revisions.
4.
DATA AND METHODOLOGY
4.1. Data sources
The stock recommendation data for the companies belonging to the G7 countries are obtained from the Recommendations Detail file of the Institutional Brokers Estimates System (I/B/E/S) for the period September 2007 until September 2011. The detailed list includes a database entry for each recommendation made by each analyst pertaining to a corresponding brokerage house. Important variables recorded were the name of the company, the announcement date, the estimator (the brokerage house), the analyst name and the I/B/E/S recommendation code. Since brokerage houses have different denominations of their recommendations, in addition to the traditional ratings of sell, hold and buy, each recommendation received from an analyst is transformed into one of the Thomson Reuters standard ratings. The standard is a five point rating scale, consisting of the following levels: 1. Strong Buy, 2. Buy, 3. Hold, 4. Sell, 5. Strong Sell. I/B/E/S also records three date variables, namely activation date, revision date and announcement date. The activation date is the date when Thomson Reuters recorded the recommendation and the revision date is the date when the recommendation was confirmed by I/B/E/S with the contributor. The relevant date, which was taken into consideration in this study, is the announcement date, since that is when the recommendation was made public.
the previous one and it is classified as an upgrade when the value of the change is a negative number and as a downgrade when the value is bigger than zero (as a consequence of the standard coding system used by Thomson Reuters). Since previous literature (Stickel, 1995; Womack, 1996 and Jegadeesh and Kim, 2006) documents stronger reactions of investors to changes to and from the extremes, this paper will focus on the reaction of investors to Upgrades to Strong Buy and Upgrades from Sell and Strong Sell as well as Downgrades to Sell and Strong Sell and Downgrades from Strong Buy.
The sample of companies belonging to the seven developed countries is represented by the constituent firms of the most recognized stock indexes from each country: the S&P 500 for the US market, the FTSE100 for UK, the S&P/TSX Composite Index for the Canadian market, the CAC in France, the DAX, representing the German stock market, the FTSE MIB for Italy and the Nikkei 225 in Japan. These stock indexes portray an accurate image of the performance of the respective markets and usually denote a significant percentage (higher than 70%) of the corresponding market capitalizations. Moreover, the companies included in the index have representative market sizes compared to their peers and are well known to the public, which also denotes increased attention from investors and builds up the appropriate context for analyzing investors’ reaction to changes in recommendations. A list of the stock indexes from each country with corresponding share of market capitalization can be found in Table 1 from the Appendix.
The sample is composed of all the stocks that satisfy the following criteria:
a) There should be at least one analyst who issues a recommendation for the stock and revises the recommendation during the sample period;
b) The analyst name, estimator information and announcement date should be available on I/B/E/S and corresponding stock price on the revision date should be available on Datastream;
c) The recommendation should be either an upgrade or a downgrade from the previous recommendation by the same analysts.
book value and the return index for the S&P 500 value weighted-index in the case of the American market and for the Datastream indices for the other markets. A description of these variables can be found in Table 2 from the Appendix.
4.2. Methodology
The methodology part of this study will be firstly focused on the organization of the event study and computation of buy-and-hold abnormal returns (BHAR), which will firstly give an indication of whether analysts’ recommendation revisions provide investment value to the investors. Secondly, in the cross-sectional analysis, the paper will try to identify the main drivers for the documented profit opportunities and for explaining the abnormal trading volume. The hierarchical linear model will be employed in order to explain the influence of different factors at country, industry, company and year level. The analysis will explain the influence of the cultural factors differentiating societies and will control for the impact of variables such as characteristics of the stocks, strength of the recommendation and characteristics of the analysts. Taking into consideration the most important players and factors in the decision-making process (as documented by previous literature), this paper tries to offer a comprehensive analysis of what might determine investors’ trading behavior.
4.2.1. Variables
4.2.1.1.
Dependent Variables
Abnormal Return (BHAR): The first dependent variable in this research is represented by
the day that recommendations were publicly available for the companies listed on the Athens Stock Exchange. The abnormal return will be computed as a difference between the actual, documented return and the expected one, as proxied by the return of the corresponding market index from each country. The exact computation can be observed in the Methodology part.
Trading Volume (VOL): Alongside analyzing the price impact of analyst recommendation
revisions on abnormal returns, this paper will also take into consideration the extent to which investors’ trading volume is influenced by changes in recommendations and the impact of cultural and control variables on the abnormal trading volume. Palmon et al. (1994) proves that information released to the market is not reflected instantly in stock prices and this is observed both by looking at abnormal returns and trading volumes. Abnormally high trading volumes on the publication day and the following day are found for the Buy sample but not for the Sell sample. As a proxy for abnormal volume, Womack (1996) calculated a ratio of volume on the recommendation dates relative to the average volume over three months centered on the recommendation periods and found higher levels of abnormal trading for added-to-buy than for added-to-sell recommendations. Jegadeesh and Kim (2006) tested for the trading volume around recommendations revisions in the G7 stock market countries and discovered that analysts’ recommendation revisions add most value in the U.S. and Japan. Investors in these countries trade more intensely following recommendation revisions than in other countries. As in Womack (1996), Jegadeesh et al. (2006) and Lonkani et al. (2010), abnormal trading volume is computed as a ratio of volume on recommendation dates over the average volume centered on the recommendation date, using a 20-day pre- and post- event dates to compute the normalized volume. The abnormal volume for stock k on day t is defined in the following way:
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where !"#$%&!!is the number of shares traded on day t, suitably adjusted for any splits within this window.5
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4.2.1.2.
Explanatory Variables
Hofstede Individualism Index (IND) and Uncertainty Avoidance Index (UAI): The
individualism index (IND) and Uncertainty Avoidance Index (UAI) employed in this paper come from a cross-country psychological survey of IBM employee values conducted by Geert Hofstede between 1967 and 1973 in 72 countries (Hofstede, 2006). Out of the 72 countries surveyed, 40 of them had more than 50 respondents. The IND was computed from the country mean scores on 14 questions about the employees’ attitudes toward their work and private lives. The UAI is obtained from country mean scores or percentages, addressing themes such as rule orientation, employment stability and stress. Updated scores for an even larger sample of countries can be found on geert-hofstede.com. The corresponding values for the IND and UAI Index for the selected countries (G7) in this paper can be observed in Table 3. “Without making a comparison, a country score is meaningless” (Hofstede, G.6), therefore, the present study will employ the rank quartile for both indexes for the G7 countries as computed in reference to the complete sample of country scores found in Table 4 from the Appendix, including a list of the corresponding values for all countries as observed on Geert Hofstede’s official website.
Table 3: Hofstede's Cultural Dimensions of Individualism and Uncertainty Avoidance for selected countries
Country Individualism Index
(IND) Uncertainty Avoidance Index (UAI)
US 91 46 UK 89 35 Canada 80 48 France 71 86 Germany 67 65 Italy 76 75 Japan 46 92
The Individualism Index (IND) and Uncertainty Avoidance Index (UAI) values for the G7 countries as selected from the survey performed by Geert Hofstede (1967, 1973)
4.2.1.3.
Control Variables
There is also extensive evidence that sentiment affects the cross-section of returns differently for different investment types, e.g. value vs. growth stocks or small vs. large stocks.
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Baker and Wurgler (2006) for example find that sentiment effects are stronger among firms that can be characterized as one of the following criteria: young, small, unprofitable, distressed, extreme, growth or dividend-nonpaying firms. He et al. (2007) also document that the reaction of small traders to analyst recommendation revisions is stronger in the case of firms that prove to be more prone to sentiment by prior literature (small firms, young firms, growth firms and volatile firms). Schmeling (2009) explains the result by the fact that e.g. small or growth stocks are harder to arbitrage and harder to value than large stocks with a long and stable earnings history. Moreover, Stickel (1995), Sorescu and Avanidhar (2006) or Kliger and Kudryavtsev (2010) observe that revisions in recommendations that skip a rank usually have greater price impact and Stickel (1995), Mikhail et al. (2004), Sorescu and Avanidhar (2006) and Jegadeesh and Kim (2006) find that reputation of the brokerage houses also affects investors’ trading decisions. Following the example of previous studies this paper will also control for the impact of the market-to-book ratio and for the effect of firm size on investors’ reaction to analyst recommendation revisions as well as for the influence of the magnitude of the revision, type of the revision (upgrade or downgrade) and characteristics (reputation) of the brokerage houses to which the analysts issuing the recommendations belong.
Downgrades vs. Upgrades (DOWNGRADE): The study will differentiate between the impact of
downgrades and the impact of upgrades on investors’ decisions, both as reflected in the stock returns and in the abnormal trading volume. In order to do this, the variable DOWNGRADE will be used, which is a dummy variable taking the value 1 if the recommendation was a downgrade from a previous level and 0 otherwise. Stickel (1995), Womack (1996) and Brown et al. (2007) are just a few examples of studies that document a stronger reaction of investors to downgrades as compared to upgrades. A theoretical explanation for this finding can be represented by the fact that downgrades are less frequent and more visible than upgrades. While upgrades usually incorporate fewer potential costs, analysts are aware that releasing a negative recommendation can be more costly due to potential losses in advisory fees, worsened brokerage firm – investment banks relationships, or limited flow of information from top management and investment contacts.
Market-to-book ratio (MTBVRANK): The market-to-book ratio is used as a proxy for expected
growth firms have higher market-to-book values. Growth and value firms may have different information environments and hence, different potential value added by analysts. Fama and French (1992) document a negative relation between market-to-book ratio and future returns. Following these observations, Jegadeesh (2004) argue that if analysts pay attention to the predictive ability of these multiples, low market-to-book firms would receive more favorable recommendations that would result in higher returns. Nevertheless, their findings contradict their expectations; their subsequent research finds a positive relation between market-to-book ratios and the price impact of recommendation revisions both for international companies and US firms. On the other hand, McKnight and Todd (2006) find a negative relation between returns and market-to-book ratios (value firms outperform growth firms) for the sample of European firms, consistent with Fama and French (1992). He et al. (2007) argues that stock characteristics (market-to-book, size) will also reflect in short sale constraints that will impact trading activity and therefore price. They expect and find a positive relation between market-to-book ratio and net buying activity by small traders. Finally, Moshirian et al. (2009) finds that the market-to-book ratio is the primary indicator for Buy and Strong Buy recommendations, which is consistent with Jegadeesh et al.'s (2004) argument that analysts prefer to recommend high growth stocks because their glamorous characteristics, high trading activities and positive price momentum make them attractive to investors. In this study, the market-to-book variable is represented by the book-to-market quartile rank within each country measured at the end of the previous calendar year (MTBVRANK).
Size or Market Capitalization (SIZERANK): In what concerns the findings related to the impact
and as investors face high trading costs in smaller stocks, they are less likely to own the later rather than the former. On the other hand, Glezakos et al. (2011) found evidence that revised recommendations for small companies could provide returns up to four times as high as their large firms peers. In order to proxy for the size of the companies the size decile rank for each country based on market capitalization as of the end of the previous month of the revision is employed (SIZERANK).
Reputation of brokerage house (TOP): Alongside stock characteristics, investors may also be
influenced by the reputation of the brokerage houses issuing the recommendation, either directly or indirectly through the influence that top brokerages exert on the recommendations issued. As investment banking revenues typically comprise a larger percentage of revenue for large brokerages, it is generally presumed that analysts employed by larger brokerages face more severe conflicts of interest (Jegadeesh and Kim, 2006). In the 2003 Global Settlement Act, the SEC and other regulatory agencies levied fines totaling about $1.3 billion on ten large brokerages for conflicts of interest, delineating clear differences between large and small brokerage houses7. While conflicts of interest may weaken the value of recommendations from large brokerages, large brokers may also attract more skilled analysts because of their reputation and better compensation schemes. Several studies have analyzed the impact of analyst reputation on the recommendation they offer and the corresponding price impact. Stickel (1995) documents a greater influence on prices from recommendations issued by high reputation analysts and large brokerages and the later findings of Mikhail et al. (2004) reiterate these results. Sorescu and Avanidhar (2006) find evidence of return persistence following small revisions by high ability analysts and the opposite return pattern following large revisions of low ability analysts. Ability is here defined in terms of years of experience and reputation of brokerage house. Finally, Jegadeesh and Kim (2006) find that large brokers add more value through their recommendation revisions than small brokers, which suggests that the effect of compensation on analysts’ skills may more than offset the potential conflicts of interest in large brokerages. In this study, the reputation factor is represented by the variable TOP, which is a dummy variable equal to 1 if the revision is made by a brokerage that is one of the top 10
brokerages domestically (at the level of each country), when ranked by the number of recommendations issued in the analyzed period.
Magnitude of the revision (SKIP): Magnitude of the revision is also a very often documented
control variable employed in studies such as the ones performed by Stickel (1995), Sorescu and Avanidhar (2006) or Kliger and Kudryavtsev (2010). Revisions in recommendations that skip a rank were hypothesized as having greater price impact than other revisions because of the larger change in expectations. This hypothesis is supported by the study of Stickel (1995) but the differences appear to be temporary. Moreover, Sorescu and Avanidhar (2006) demonstrate that analyst ability rather than the strength of the revision is more important in determining returns. Kliger and Kudryavtsev (2010) find mixed evidence on the effect of the magnitude of the change on the event-driven abnormal returns but demonstrate that investors’ reactions to news remain significant even after controlling for the number of rating categories changed. In order to account for the strength of the recommendation revision, this paper will use of a dummy variable (SKIP) that takes the value of 1 if the revision skips a rank and 0 otherwise.
4.2.2. Sample and descriptive statistics
Table 5: Sample descriptive statistics
Country Stocks Stocks/ year Analysts Analysts/ year Brokerage firms Brokerage firms/year
US 487 454 2028 1057 253 169 UK 100 94 1042 510 122 80 Canada 244 196 716 393 123 73 France 40 40 766 346 98 66 Germany 29 27 632 302 89 61 Italy 40 39 473 231 73 56 Japan 216 200 536 336 41 31 Total 1156 1050 6193 3175 799 536
This table presents the descriptive statistics for the sample in terms of constituent stocks, revising analysts and corresponding brokerage houses. The sample includes all firms in the G7 countries that have at least two active recommendations from the same analyst in the IBES recommendations database, and also have stock return data on recommendation revision dates. The columns present the total and average number of stocks per year, the total and average number of analysts per year, and the total and average number of brokerage firms per year for the sample period September 2007–September 2011.
Figure 1: Cumulative stock market returns in the G7 countries from September 2007 to November
2011.
The S&P 500 value weighted-index is used to represent the U.S. market and the Datastream indices are used for the other markets.
Table 6 presents the distribution of upgrades and downgrades and their corresponding categories within each country. As it can be observed, there is a total of 35,431 recommendation revisions issued in the G7 countries for the companies included in the sample from September 2007 until and including September 2011. Out of these, 53% are upgrades while the rest represent downgrades from a previous recommendation levels. The observation that the number of upgrades exceeds the number of downgrades is consistent with previous literature (e.g. Womack, 1996, Moshirian et al., 2009) and it is observed for all the countries except Italy. Some important explanations for analysts’ upward bias in the recommendations they offer are the fact that analyst recommendations need to appear consistent with the business direction dictated by their firm (Moshirian et al., 2009) and the fact that downgrades are usually more costly than upgrades. As previously mentioned, Womack (1996) explains that analysts are aware that releasing a negative recommendations can be more costly due to potential losses in
!1.2% !1% !0.8% !0.6% !0.4% !0.2% 0% 0.2% 0.4% 3!S ep !07% 3!No v!07% 3! Jan !0 8% 3! Mar !0 8% 3! May !0 8% 3!Ju l!08% 3!S ep !08% 3!No v!08% 3! Jan !0 9% 3! Mar !0 9% 3! May !0 9% 3!Ju l!09% 3!S ep !09% 3!No v!09% 3! Jan !1 0% 3! Mar !1 0% 3! May !1 0% 3!Ju l!10% 3!S ep !10% 3!No v!10% 3! Jan !1 1% 3! Mar !1 1% 3! May !1 1% 3!Ju l!11% 3!S ep !11% 3!No v!11%
advisory fees, worsened brokerage firm – investment banks relationships, or limited flow of information from top management and investment contacts. Nevertheless, compared to previous studies where the proportion of strong buy and buy recommendations on a yearly basis is double the number of underperform, sell and strong sell recommendations across the sample (Moshirian et al, 2009), the difference is very small in the current sample probably because this study covers the period after the financial crisis. Nevertheless, in a similar study performed by Jegadeesh and Kim (2006) for the G7 countries, although the percentages obtained are very similar, there are more downgrades than upgrades in the sample period under analysis (November 1993 – July 2002).
Within each category, upgrades to strong buy and upgrades from sell or strong sell, and downgrades to sell or strong sell and downgrades from strong buy are examined separately. Since strong buy is the highest recommendation level (only 15% of the recommendations are upgrades to strong buy and only 13% are downgrades from strong buy), it is possible that a recommendation revision to or from this category conveys a stronger signal about the analyst’s opinion than a revision to or from any of the other recommendation levels. Moreover, as analysts rarely issue sell or strong sell recommendations (only 14% from the total are downgrades to sell/strong sell), additions to or removal from these categories may be viewed as a stronger signal by the market than other recommendation revisions. Also, as in previous papers (Jegadeesh and Kim, 2006), the frequency of downgrades outside the US is much larger which supports the idea introduced by Jegadeesh and Kim (2006) that analysts in the US face the largest conflicts of interest. In addition to this, the present study will also try to explain the differences by analyzing the influence of cultural and psychological factors.
Table 6: Distribution of analyst recommendation revisions in G7 countries Country All revisions No. Upgrades Downgrades
All To strong buy From sell/
strong sell
All To sell/strong
sell
From strong buy
No. (%) No. (%) No. (%) No. (%) No. (%) No. (%)
US 11961 6478 54.159 2223 18.585 1696 14.179 5483 45.841 1440 12.039 1873 15.659 UK 5073 2704 53.302 774 15.257 1010 19.909 2369 46.698 853 16.815 635 12.517 Canada 5918 3110 52.552 719 12.149 606 10.240 2808 47.448 561 9.480 660 11.152 France 2635 1363 51.727 399 15.142 552 20.949 1272 48.273 488 18.520 381 14.459 Germany 2310 1211 52.424 484 20.952 547 23.680 1099 47.576 494 21.385 383 16.580 Italy 2091 1029 49.211 287 13.725 422 20.182 1061 50.741 416 19.895 313 14.969 Japan 5443 2787 51.203 410 7.533 916 16.829 2656 48.797 825 15.157 428 7.863 Total 35431 18682 52.728 5296 14.947 5749 16.226 16748 47.269 5077 14.329 4673 13.189
