Investor Sentiment and Stock Returns: A Cultural and
International View
Tilburg University School of Economics and Management Faculty of Economics and Business Administration Department of Finance Master Thesis Finance
Author: Job Andreas Eduardus Sinke ANR: 566377 Supervisor: Dr. J.C. Rodriguez Chairman: Prof. Dr. S.R.G. Ongena Date of graduation: 4 December, 2012
Investor Sentiment and Stock Returns: A Cultural and International View
Job Sinke Master Thesis December 2012
Abstract:
This paper examines if consumer confidence (a proxy for investor sentiment) affects expected returns internationally in 12 developed countries, looking at culture to be a possible determinant of cross-country differences. In line with earlier evidence I find that sentiment negatively forecasts aggregate stock market returns on average across countries. When investors are optimistic (pessimistic) future stock returns tend to be lower (higher). This relation holds for value stocks, growth stocks, small, mid and large cap stocks and for different forecasting horizons. Individual country results vary finding cross-country differences that can be explained by culture. This cross- sectional perspective provides evidence that the impact of sentiment on stock returns is more pronounced in countries scoring high on uncertainty avoidance, masculinity, long-term orientation and for countries that score low on individualism and indulgence, the effect of power distance is ambiguous.
Keywords: consumer confidence; investor sentiment; style/size differences; cultural dimensions; predictive regressions
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Contents 1. Introduction ...... 5 2. Theory and Hypothesis Development ...... 7 2.1. The sentiment-return relation ...... 7 2.2. Cross sectional differences between different styles or sizes of stocks ...... 9 2.3. Culture and the sentiment-return relation ...... 10 3. Data and Descriptive Statistics ...... 15 3.1. General data considerations ...... 15 3.2. Descriptive statistics ...... 18 3.3. Preliminary tests ...... 19 4. Predictive Regressions of Stock Returns on Consumer Confidence ...... 21 4.1. Methodology ...... 21 4.2. Results for panel regressions ...... 22 4.3. Results for individual countries ...... 24 4.4. Cross-sectional analysis ...... 26 4.5. Relation of results to earlier studies ...... 27 4.6. Discussion ...... 30 5. Conclusion ...... 31 6. References ...... 32 7. Appendices ...... 39 7.1. Appendix I. Different proxies for investor sentiment ...... 39 7.2. Appendix II. Details on consumer confidence surveys ...... 41 7.3. Appendix III. MSCI indices construction methodology...... 42 7.4. Appendix IV. Discussion of Hofstede’s his dimensions ...... 43 7.5. Appendix V. Tables ...... 44
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Tables Table 1. Countries used in the study ...... 44 Table 3. Descriptive statistics ...... 46 Table 4. Descriptive statistics ...... 47 Table 5. Correlations between cultural dimensions...... 48 Table 6. Panel unit-root tests ...... 48 Table 7. Granger-causality tests ...... 48 Table 8. Consumer confidence correlations ...... 49 Table 9. Sentiment coefficient in long-horizon regressions ...... 49 Table 10. Sentiment significance in long-horizon regressions ...... 50 Table 11. Economic magnitude of the sentiment effect ...... 51 Table 12. R-squares of sentiment regressions ...... 52 Table 13. Return predictability for individual countries across horizons ...... 53 Table 14. “Crisis” joint significance ...... 55 Table 15. Sentiment coefficient in long-horizon regressions: behavioral panels ...... 56 Table 16. Sentiment significance in long-horizon regressions: behavioral panels ...... 57 Table 17. Cultural joint significance ...... 58
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1. Introduction
The global stock market crash of 2007 and 2008 was an exceptional event. The MSCI World index of developed markets fell approximately 50 percent in dollar terms. Emerging markets fell even further. Individual country indices displayed falling returns, but with varying percentages. Such an event is often hard to explain by classical theories even ex post. The classical theories try to rationalize investor behavior, but in times of crisis this turns out to be difficult if not impossible. Behavioral finance rejects this neoclassical “economic” man or “rational” investor and recognizes investors to be “normal”. In this field the unbounded rationality is replaced by the observation of empirically established rationality defects. Or in other words behavioral finance is no longer based on the rationality of investors but accepts people, investors to behave different, normal, or irrational at times. Behavioral finance assumes this “bounded rationality” to be valid for most countries (Levinson and Peng, 2007). However, as seen in the MSCI World Individual country indices countries vary, cultures vary and cultural finance supports the diverging relevance of certain behavioral patterns between countries. Statman (2008) finds rationality to defect but in various magnitudes across countries. A stock market crash can therefore be explained by the behavioral approach. This field of research has recently attracted much interest, given the realization that investors might not behave “rational” after all. While the behavioral approach becomes more common, little attention has been paid to culture affecting finance. Guizo, Sapienza and Zingales (2006) document that culture may significantly affect economic outcome. One might wonder if finance is affected as well. More recently a field called “cultural finance” has established itself, which was named by Breuer and Quinten (2009). Existing economical and financial literature were reluctant to include cultural influences on financial questions for a long time. Values have been taken as given and culture is treated as a “black box” (Williamson, 2000). This reluctance to include culture comes from its definition and from methodological challenges when trying to operationalize culture. The absence of sufficient theoretical foundation can also explain the scarcity of research in this area. Recently culture is starting to gain interest of academics leading to papers with culture as the main subject (Guiso, Haliassos and Japelli, 2000; Chui and Kwok, 2008; Breuer and Quinten, 2009; Breuer and Salzmann, 2012). In different areas, culture is found to be able to explain country-specific differences. For example national culture turns out to be an important and effective factor for life insurance consumption (Chui and Kwok, 2008). Breuer and Salzmann (2012) link national culture to household finance using Schwartz’s cultural dimensions. Breuer and Salzmann (2008) also find culture to influence corporate governance. Breuer and Quinten (2009) introduce the term “cultural finance” and make way for a new field of research. The fields of behavioral and cultural finance show
-5- considerable similarities, they are both based on the notion of an irrational investor. The relation of sentiment and stock market returns has been greatly explored in existing literature; therefore the focus of this research lies in the cultural approach. This paper explores the sentiment-return relationship in a way that can be best classified in the “cultural finance” field of research and will attempt to identify culture as a cross-country determinant of variance in the sentiment-return relation. Earlier evidence of the sentiment-return relation is primarily focused on the U.S. stock market and is based on two assumptions: that investors are subject to sentiment (Delong, Shleifer, Summer and Waldman, 1990) and that there are limits to arbitrage (Shleifer and Vishny, 1997). This paper looks at multiple countries as well as the U.S. stock market, analyzing the international aggregate stock market. The stocks have been divided in size and style, as some stocks are harder to value and arbitrage then others, leading to different results (e.g. Baker and Wurgler, 2006; Lemmon and Portniaguina, 2006). Aggregate country level stock markets are obviously hard to value and hard to arbitrage. Macro data is known to be noisy and it is difficult to hedge away all idiosyncratic risk at country levels. Sentiment shocks will therefore affect stock markets on aggregate and not just different subgroups of stocks. The international approach taken by this paper allows for exploration of possible determinants of cross-country differences. In this paper I will look at cultural effects and test if the sentiment-return relation is more pronounced in countries pooled together based on cultural differences. The international dataset provides a natural out-of-sample test for earlier findings. Griffin, Ji and Martin (2004) or Ang et al. (2008) state the importance of testing market phenomena such as the sentiment effect out of sample. Pooling data across countries increases the power of tests which yield more reliable estimates (Ang and Bekaert, 2007). The question is no longer if there is a sentiment-return relation but how to explain the differences among countries. What drives the sentiment-return relationship? Is a more relevant question these days. I will examine the relationship between sentiment and returns in relation to Hofstede’s cultural dimensions of power distance, individualism, masculinity, uncertainty avoidance, long-term orientation and indulgence versus restraint. As the main contribution of this paper I find that national culture has significant impact on the sentiment-return relationship after controlling for macro economical factors. Further I find that there is a significant impact of investor sentiment on aggregate stock returns across countries using predictive panel-fixed regressions for a panel of 12 countries from all over the world. I find differences in the magnitude of impact when looking at different style and size stock indices and culture is a cross-country determinant of the magnitude of impact. The impact of sentiment on stock returns is more pronounced in countries scoring high on uncertainty avoidance, masculinity, long-term orientation and countries that score low on
-6- individualism and indulgence, the effect of power distance is ambiguous. The found effects remain significant after controlling for other standard risk factors, or macro economical risk factors. The remainder of the paper is structured as follows. Section 2 introduces the theoretical background, focusing both on the sentiment-return relationship and the presumed effects national culture has on the relationship. Section 3 presents the dataset as well as descriptive statistics. Section 4 reports methodology, the results of the research and will discuss. Finally section 5 will conclude.
2. Theory and Hypothesis Development
In the first part the sentiment-return relation will be explained, the second part will explain why there could be differences between types of stocks and which stocks should be the most affected. Third I will introduce culture as a factor determining the magnitude of the sentiment-return relation.
2.1. The sentiment-return relation The general finding of a sentiment-return relationship is not consistent with standard finance theory. The classic or standard finance theories are based on the assumption of a “rational investor”. The behavioral approach recognizes that investors are not “rational” but “normal”. This fact, leads to systematic biases in their beliefs inducing them to trade on non-fundamental information called sentiment. The classic theories state that stock prices reflect the discounted value of expected cash- flows and that irrationalities among market participants are erased by arbitrageurs, with sentiment not playing any role in the framework. Rationality however finds it difficult to explain the stock market collapse in 1929 (Delong and Shleifer, 1991; White 1990). Maybe it was “irrational exuberance” (Shiller, 2000) driving prices above fundamental values. More recently the “internet bubble” or “dot.com bubble” showed that the rapid rise and fall of technology stocks was due to excessively bullish sentiments returning to more normal values in 2000. Several papers find it hard to reconcile these examples with rational pricing (Lamont and Thaler, 2001; Ofek and Richardson, 2001). Shiller (1987) surveys both individual and institutional investors’ inquiring about their behavior surrounding the 1987 crash. The survey shows that most investors interpret the crash as the outcome of other investors’ psychology rather than changes in fundamentals such as earnings or interest rates. Siegel (1992) finds that changes in neither interest rates nor changes in future earnings account for the dramatic valuation changes around the October crash 1987. Moreover he concludes that shifts in investor sentiment are correlated with market returns around the crash. Sentiment thus plays a role in explaining variance in stock markets.
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In the behavioral approach waves of irrational sentiment, times of overly optimistic or pessimistic expectations, can persist and affect asset prices for significant periods of time. Brown and Cliff (2004) describe sentiment to represent the expectations of market participants relative to a norm: an optimistic (pessimistic) investor expects returns to be above (below) average, regardless of what average may be. Baker and Wurgler (2007) broadly define “investor sentiment, as a belief about future cash flows and investment risks that is not justified by facts in hand”. Clearly sentiment is not a factor that can be described as rational. Several studies offer models establishing the relationship between investors’ sentiment and asset prices. Delong, Shleifer, Summers and Waldman (1990) show theoretically that correlated sentiment of noise traders affects equilibrium stock returns. Baker and Wurgler (2006) find that sentiment-based mispricing is founded both on an uninformed demand shock (noise trading) and on limits to arbitrage. Brown and Cliff (2005) argue that sentiment is most like, a very persistent effect. Trader noise or uninformed demand shocks may thus be correlated over time and give rise to strong persistent mispricing. Regarding trader noise Barber, Odean and Zhu (2008a) find that trading by individuals is highly correlated which is consistent with systematic noise trading that does not wash out in the aggregate. Limits to arbitrage deter investors from eliminating mispricing (Black, 1986; Shleifer and Vishny, 1997) since beforehand it is unclear how long buying or selling pressure from overly optimistic or pessimistic noise traders will persist. Eventually every mispricing is corrected so high levels of optimism are on average followed by low returns and low levels of optimism or even pessimism are followed by high returns on average. Arbitrage forces can eliminate profitable short- term trading strategies but not for longer run mispricing (Brown and Cliff, 2005). Sentiment may drive asset prices away from fundamental values for extended periods of time but this is difficult to detect over short horizons (Summers, 1986). Betting against a sentimental investor is found to be costly and risky according to Shleifer and Vishny (1997). This leads to rational investors or arbitrageurs to be less aggressive in forcing prices to fundamentals as the standard theory would predict, resulting in limits to arbitrage. These limits come from short time horizons or from costs and risks of trading and short selling according to Delong, Shleifer, Summers and Waldman (1990). Market imperfections must lead to observed prices deviating from fundamental value (Grossman and Stiglitz, 1980). Thus it seems that sentiment can affect prices and that this effect is persistent. Investors are subject to sentiment (Delong, Shleifer, Summers and Waldman 1990), which seems intuitive given recent developments in the behavioral finance field. Many models find two types of investors; rational arbitrageurs who are free of sentiment and irrational investors prone to exogenous sentiment. These types are competing in the market and setting both prices and expected returns which lead to an aggregate affect of sentiment on stock market returns.
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Different models are explaining this bias that is called sentiment, using individual biases in investor psychology such as overconfidence, representativeness, conservatism and over- or under reactions. These psychological factors are said to be the source of the noise for traders. Daniel, Hirshleifer and Subrahmanyam (1998) find investors are overconfident about their private signals and they incorrectly attribute successful outcomes to their own abilities and blame bad outcomes to chance rather than their own mistakes, basing their research on self-attribution and overconfidence. Barberis, Shleifer and Vishny (1997) are using a model which has earnings as a random walk distribution but investors belief earnings to switch between a mean-reverting regime and growth- regime, a belief that turns out to be wrong. Investors see a pattern where none exists which is called representativeness. The investors are slow to update their beliefs of the regime in the light of new evidence, a psychological effect known as conservatism. Hong and Stein (1999) find under reaction to lead to trading by momentum factors. Overreaction results when the momentum investors have gone too far. Shefrin (2008) relies on differences in opinion across investors. Differences in opinion, even when investors have the same information, can be large (Miller, 1997). Persistence in sentiment measures is consistent with conservatism and biased self-attribution (Brown and Cliff 2004). When aggregated these models of individual psychological factors make predictions about patterns in market wide investor sentiment, stock prices and volume. Earlier evidence (Brown and Cliff, 2005; Baker and Wurgler, 2006) find a negative sentiment- return relation on the aggregate U.S. stock market level. Schmeling (2009) finds evidence for this relation on an international level. Baker, Wurgler and Yuan (2009) find that sentiment affects stock markets both globally and locally. This evidence leads to the first hypothesis:
H1: International investor sentiment predicts future aggregate returns. Optimism (pessimism) leads to a negative (positive) significant relationship between returns and sentiment, these relations are robust after controlling for fundamental factors.
2.2. Cross sectional differences between different styles or sizes of stocks There is also evidence (Baker and Wurgler, 2006; Lemmon and Portniaguina, 2006) that sentiment affects the cross-section of returns differently for different investment styles such as value or growth stocks and small or large stocks. This can be explained by the characteristics of the different stocks, small or growth stocks are harder to arbitrage and harder to value than large value stocks with a long and stable earnings history. Stocks of low capitalization, younger, unprofitable, high-volatility, non- dividend paying, growth companies or stocks in financial distress are likely to be disproportionally sensitive to waves of investor sentiment according to Baker and Wurgler (2007). Such stocks tend to be more costly to buy and sell short (D’Avolio, 2002). Wurgler and Zhuravskaya (2002) also state that such stocks have a high degree of idiosyncratic variation in their returns making betting on them
-9- riskier. This higher volatility may lead to second-guessing by investors who provide funds for the arbitrageurs leading to withdrawals from those arbitrageurs when mispricing is the greatest (Shleifer and Visny, 1997). Kurov (2008) finds investor sentiment affects trader’s behavior and market liquidity. Kumar and Lee (2006) show that retail investors, often characterized as noise traders, tend to overweight value stocks relative to growth stocks and that shifts in buy-sell imbalance of these retail investors are positively correlated with the returns of value stocks. This is a prime example of noise trader risk. Lee, Shleifer and Thaler (1991) identify noise traders with individual investors and show that small stocks are disproportionately held by individuals as opposed to institutions. Nagel (2005) finds a strong positive correlation between ownership by institutions and size. Chan and Chen (1991) identify that small firms are also associated with higher levels of financial distress risk. The key point is that the same securities that are difficult to value are difficult to arbitrage. This extra difficulty in arbitrage with these stocks should lead to higher sensitivity to investor sentiment, as the uncertainty of value means that the effects of overconfidence, self-attribution (Daniel, Hirschleifer and Subrahmanyam, 1998), representativeness and conservatism (Barberis, Shleifer and Vishny, 1998) are more pronounced. This evidence leads to the second hypothesis:
H2: The effect of sentiment is more pronounced for stocks that are harder to arbitrage and harder to value. Small and growth stocks are influenced more heavily by investor sentiment compared to large and value stocks.
2.3. Culture and the sentiment-return relation Not all people across the globe behave in the same way. “if, as argued by practitioners of behavioral finance, individuals have psychological biases that matter for finance, it would be surprising that individuals’ view of the world as determined by their culture does not matter for how they view and act in financial markets” Stultz and Williamson (2003 p.347). A collective set of common experiences that people will share, will influence cognitive and emotional approaches to investing according to Statman (2008). Culture should thus lead to cross country differences among people, investors and behavior. Culture is however very broad and channels through which it can enter economic models are ubiquitous and vague, so that is it difficult to design testable, irrefutable hypothesis. Culture is as described by Traindis et al. (1986) “a fuzzy, difficult-to-define construct”. Guiso, Sapienza and Zingales (2006) define culture as “those customary beliefs and values that ethnic, religious and social groups transmit fairly unchanged from generation to generation”. Hofstede and Bond (1988) provide a more comprehensive definition of culture in “the programming of the mind that distinguishes the members of one category of people from those of another. Culture is often composed in certain values, which shape behavior as well as one’s perception of the world.” Adler (1997) finds that
-10- culture influences our values, which affects our attitudes and then our behavior. This type of hierarchy has been empirically substantiated by Homer and Kahle (1988). Earlier literature has proven that culture can affect economics and finance. de Jong and Semenov (2002) focus on the stock market development of OECD (Organization of Economic Cooperation and Development) countries, finding that stock markets tend to be more developed in countries with lower levels of uncertainty avoidance and higher levels of masculinity. Chui, Lloyd and Kwok (2002) find that managers choose lower corporate leverage in countries with higher levels of conservatism and mastery using the cultural measures of Schwartz (1994). Guiso, Sapienza and Zingales (2003) have found religion to be associated with economic attitudes. Stulz and Williamson (2003) show that culture can affect predominant values, institutions and resource allocation in a country. Culture may lead to certain attitudes that are more conducive to certain outcomes (Guiso, Sapienza and Zingales, 2006). Culture can exert influence by affecting institutions in a country (Stulz and Williamson, 2003). Concluding that culture matters, is persistent (Guiso, Sapienza and Zingales, 2006) but also variant (Statman, 2008). Dimensions of culture can impact financial outcomes, through prior beliefs, values and preferences. The focus lies on dimensions of culture that are inherited by an individual rather than voluntarily accumulated. Becker (1996) finds that individuals have less control over their culture than over other social capital. Behavioral finance argues that imperfections in financial markets are due to a combination of psychological biases such as overconfidence, investor overreaction and various other human errors in reasoning and information processing. The behavior of investors is based on cognitive and emotional aspects of decision making. Psychology factors are however often quite vague and narrow, taking national culture as means of determining those values creates a more comprehensive approach, as it describes the mindset of people exhaustively. All of the psychological factors referred to by previous studies can be found as basic values in the cultural dimensions concept. Values with similar characteristics can be pooled in one cultural dimension. National culture thus presents a more systematic approach. Using Hofstede (1980) his traditional four dimensions in addition with two later added dimensions (2011) gives an indication if and how culture affects the sentiment-return relationship. For each of the dimensions theory can be related to the psychology factors in existing literature. Mainly overconfidence, self-attribution, representativeness, conservatism, herd-like behavior and persistence are part of the explanation why culture should affect the relationship. As argued above culture can affect economics and finance, the way in which I expect culture to affect the sentiment-return relationship is discussed in more detail below, deriving hypothesis from literature for all used dimensions. Individualism (IND): according to Hofstede (2001) measures the extent to which individuals are integrated into groups. In individualistic countries ties between individuals are lose, people are
-11- expected to care not much about persons beyond their immediate family. Hirshleifer and Thakor (1992) find that in high individualistic countries the first priority of agents is to take care of their own interests. Collectivistic societies are integrated into strong groups. Chui, Titman and Wei (2008) relate this dimension to psychological factors such as over optimism, overconfidence and self- attribution. Park and Lemaire (2011) find countries with a high individual score to search for more security in the form of insurance, indicating a link between UAI and IND. Hofstede (2001) states that individualism affects the degree to which people display an independent behavior rather than a dependent behavior. Moreover he argues that children in collectivistic cultures build their identity for their social system. Countries with higher levels of collectivism indicate a tendency to herd-like behavior. While Chui, Titman and Wei (2008) clearly link collectivistic behavior to the tendency of investors to herd, to explain the sentiment-return relation individuals also need to overreact. Jackson (2004) provides evidence for Australia that people overreact and create a negative relation between returns and sentiment in this way. Therefore I expect countries with high levels of individualism to be less affected by sentiment. Power Distance (PDI): is found by Hofstede (2001) to resemble the extent to which less powerful members of organizations and institutions accept and expect power is distributed unequally. In cultures with high power distance, people take inequality as granted, tolerate the concentration of power and are more reluctant to give up independence (de Jong and Semenov 2002). In cultures with small power distance values like trust, equality and cooperation are more important. Chui and Kwok (2008) find in high power distance countries that individuals expect superiors to be mindful of their welfare and take actions to reduce their risk in return for their surrender of power and acceptation of authority. However this also happens in low power distance countries where political leaders take actions to reduce risk as well. Institutions thus play a larger role in high power distance countries and those countries are said to be more collectivistic, theoretically showing correlation between PDI and IND. Chui, Titman and Wei (2008) also use proxies for “Stock market integrity”, Power distance is related to those proxies. Corruption for example is more likely to occur in high power distance countries. Schmeling (2009) also uses anti-director rights as a proxy for market integrity. This can be linked to PDI in stating that high power countries have institutions protecting welfare as individuals expect this from their superiors and take actions to reduce risk, this should also include shareholder protection. Indices for corruption perception and anti-director rights have also been used by La Porte et al. (1998). Although this paper does not test these variables directly PDI includes some of these characteristics. Markets with higher institutional quality should have better developed flow of information and are consequently more efficient (Chui, Titman and Wei, 2008). House et al. (2004) find a significant difference in the way information is shared; in high power distance countries information is localized and in low power distance countries
-12- information is shared. This shows differences between human reasoning and information processing. Hofstede (2011) finds in high power distance countries subordinates expect to be told what to do, and in low power distance countries they expect to be consulted, showing correlation with individualism and the herd-like behavior. As a result of these findings I expect high power distance countries to be more affected although some evidence is mixed. ` Uncertainty avoidance (UAI): this dimension measures the likely degree of overreaction across countries. Hofstede (2001) documents that people in more uncertainty avoiding countries act and react more emotional compared to countries with low levels of uncertainty avoidance. People in those countries act more completive and thoughtful. People in high uncertainty avoidance countries are not comfortable with ambiguity and uncertainty and try to avoid such situations. In countries with high levels of uncertainty avoidance people prefer certainty, security, and predictability and are reluctant to accept risks (Riddle 1992; Offerman and Hellman 1997). Investors might need to deal with the possibility that they do not possess some information which might affect future price movement, particular in inefficient markets according to Lucey and Zhang (2009). This information asymmetry triggers uncertainty, and this uncertainty may influence attitudes and propensities of investors and people. House et al. (2004) states that people in countries with high levels of uncertainty avoidance show a stronger resistance to change. In this way conservatism can be related to this dimension. Hofstede (2001) finds uncertainty avoidance to be correlated with collectivism since the uncertainty avoidance index captures cross-country differences in the propensity of people to follow the same set of rules and thus behave in the same manner. Higher levels of uncertainty avoidance indicate both tendencies towards overreaction and to herd-like behavior, leading to the hypothesis that countries that score high on this index are influenced more heavily by investor sentiment. Masculinity (MAS): evaluates whether gender differences impact roles in activities. Hofstede (2001) describes masculinity as the opposite of femininity referring to the distribution of emotional roles between genders in countries. Masculine countries emphasize factors such as being very assertive and competitive and having a willingness to seek competitive outcomes (de Jong and Semenov, 2002). Portfolio managers in countries with high masculinity are likely to overreact and show overconfidence when they invest in shares, while they behave conservatively in countries with low masculinity (Lucey and Zhang, 2010). Barber and Odean (2001) clearly show the values overconfidence and self-attribution to be more pronounced in men than in women. In masculine societies, performing, achieving and making money are given paramount importance (Gleason et al., 2000). In feminine societies, helping others and the environment, having warm relationships and the quality of life are key values. Women seem to take less risk as they strive for helping others and quality of life or security, so the effect of overreaction should be lower with feminine countries. Chui
-13- and Kwok (2008) find feminine societies to purchase more insurance, as they care more about family needs, showing a link with uncertainty avoidance. Based on this information the effect of investor sentiment is more pronounced in countries with high masculinity. Long-term orientation (LTOWVS): Research done by Hofstede and Bond (1988) led to this added fifth dimension. Further research done by Minkov (2010) allowed extending the number of countries for this dimension using World Value Survey data. Hofstede (2001) finds the long-term orientation societies to find the values orientated to the future important, in particular saving, perseverance, persistence and adapting to changing circumstances. Short-term orientated societies relate to the past and present, such as traditions, preservation of “face” and fulfilling social obligations. Long-term orientation seems to be based more on synthetic thinking whereas the short- run orientation is more analytical in thinking. Park and Lemaire (2011) describe this dimension as a variable that scores countries based on adherence to Confucian principles such as perseverance and thrift, respect of tradition and family values, and honor of parents and ancestors. Furthermore Hofstede (2011) finds that in short term orientated countries students attribute success to themselves and failure to luck, in the long term orientated countries success is attributed to effort and failure to a lack of effort, one can clearly see the self-attribution bias in these differences. Sentiment is a very persistent factor and the effects of sentiment can grow over time thus long term orientation should lead to more pronounced sentiment effects. Park and Lemaire (2011) find that Long-Term has a strong positive impact on life-insurance demand. The expected effect could be ambiguous or less clear because the self-attribution bias is based on short-term orientation and persistence on the long-term orientation. However the difference in thinking, synthetic to analytical, can create errors in reasoning and information processing tilting the expected effect that sentiment is more pronounced in long-term orientated countries. Indulgence versus restraint (IVR): this dimension has been added more recently following Minkov (2010) his research. Indulgence stands for a society that allows relatively free gratification of basic and natural human drives related to enjoying life and having fun. Restraint stands for a society that suppresses gratification of needs and regulated it by means of strict social norms (Hofstede 2011). According to Hofstede (2011) in indulgent societies positive emotions are more likely to be remembered opposed to negative. This can be explained as a form of self-attribution or at least uneven reaction to different signs, negative and positive. This implicates that under- and overreactions play a role for this dimension. This index is closely correlated with the traditional/secular-rational value of Inglehart (2006), which states importance of traditional values such as respect for authority and institutions. Childs often learn obedience and faith opposed to independence and determination in traditional countries. Power distance, institutions and authority are thus correlated with this index. And when looking at traditional values, conservatism, or
-14- reluctance to change can well be added to values characterized by traditional countries. In countries with high restraint levels, there are stricter norms, and there is more police found by Hofstede (2011). One can argue if these rules, institutions and increased focus on authority lead to better “stock market integrity“. I use this index as a rough proxy for market integrity and along with the other information the effect might not be as clear as with some of the other dimensions but I expect countries who are indulgent, or score high on this index to be more heavily influenced by sentiment as this stands for worse market integrity.
H3: The impact of culture on the sentiment-returns relationship is significant and culture is a cross- country determinant of the sentiment-return relation. Effects are more pronounced for countries that score high on UAI, MAS, ITOWVS and IVR, for countries that score low on IND and for PDI the effects is ambiguous.
3. Data and Descriptive Statistics
3.1. General data considerations This study takes a look at the effect of noise trader demand shocks on stock markets. The main focus of this paper is the cultural approach since the sentiment-return relationship is rather well established in earlier literature. The international aspect of the paper allows for country differences to become visible. The dataset is therefore build upon cultural differences between individual countries to find if culture is a cross-county determinant of the sentiment-return relation. The first task is to measure the effect of noise trader demand shocks on stock market returns. There is no consensus on the kind of proxy to use when measuring individual sentiment for a single country, so finding a reliable consistent proxy for multiple countries is certainly a difficult task. Earlier literature, although extensive, is not consistent in the use of proxies for sentiment. This paper will use consumer confidence as a proxy of investor sentiment1. Consumer confidence is available for several industrialized developed countries and for reasonable periods of time. It is measured slightly different across countries but it seems to be the only consistent way to obtain a sentiment proxy that is largely comparable across countries and one that is not calculated from trading data itself. The surveys in the different countries are chosen to resemble each other and contain the same core questions looking at the similar horizons2. In taking a cultural approach consumer confidence thus provides a natural proxy for sentiment, as it is international orientated and consistent across countries.
1 A short overview of alternative sentiment measures and evidence on consumer confidence as a proxy for investor sentiment can be found in appendix 1. 2 Appendix 2 gives details of the consumer surveys; it discusses similarities as well as differences between countries. -15-
Consumer confidence data is collected for all sample countries. For all 7 European countries the data comes from the “Directorate Generale for Economic and Financial Affiars” (DG ECFIN) which conducts research for the European Union. This data is also by used Jansen and Nahuis (2003) and Schmeling (2009). Several high quality surveys exist for the U.S. this paper employs the Michigan Survey as was done before by Lemmon and Portniaguinia (2006). For the other countries data comes from datastream and the surveys are selected based on the comparability. The index of Japan is only available on quarterly frequency, converting this data to monthly frequency is done using last available values for months without data as in Baker and Wurgler (2006). I use data on stock returns and consumer confidence for 12 developed countries. Data availability and significant cultural differences dictated the sample of countries. For example Sweden is excluded because it has almost the same values as Denmark for the cultural dimensions, so cross country determination of variations would become less clear. Austria is excluded due to a limited time horizon for the sentiment data. I include Australia, Canada, New Zealand, Japan, the U.S. and 7 European countries. These markets cover the lion’s share of international stock market capitalization; cover most liquid markets in the world – being the U.S., Europe and Japan- thus providing a representative sample. For all countries stock data is collected based on MSCI country indices including size and style differences. The indices are chosen instead of the largest stock exchange in the individual countries for comparability reasons. MSCI is formed following the same methodology in every country3. The MSCI indices are total returns including dividend and returns are in local currency to avoid currency and exchange rate effects. In the remainder of the paper these indices will be referred as MSCI (Aggregate market), SG (small-growth), SV (small-value), MG (mid-growth), MV (mid-value), LG (large-growth) and LV (large-value). The stock market data will be collected with a monthly frequency. With time horizon starting June 1994 ending in may 2012. Returns are however influenced by macro economics risk factors. Culter, Poterba and Summers (1991) establish that macro-economic variables explain approximately one-third of the variance in stock returns. To create an explanatory variable that is unrelated to fundamental risk factors some macro economics risk factors are added as control variables. I add four main macro economic factors, a selection based on earlier literature and data availability. Using these control factors the macro risk is netted out from the sentiment proxy. I acknowledge that this research might miss some important macro risk factors, but with the included set of control variables I feel comfortable that reasonable effort is made to clean the sentiment variable of this macro risk. This paper focuses on culture as a possible determinant of country specific differences, making controlling for macro risk factors less important. As missing some possible factors of macro risk will not lead to
3 A brief explanation of the used methodology of how the MSCI indices are constructed can be found in appendix 3. -16- dramatic changes in cross country differences when pooling countries together based on Hofstede his cultural dimensions. The controls employed are the detrended short rate, the term spread, the annual change in industrial production and the annual CPI inflation. The short term rate is the 3-month interbank lending rate for individual countries which is stochastically detrended. Detrended short rate is also used by Campbell (1991); Hodrick (1992) and Brown and Cliff (2005). Term spread is calculated by the taking the difference between the yield of 10-year government bonds and the yield of 3-month treasury bills as done by Fama and French (1989). Sources for this control variable are national banks, European central bank and the Federal Reserve System. Inflation is measured by the change in CPI (consumer price index) and the source is international financial statistics (IFS). CPI is included based on Baker and Wurgler (2006); Lemmon and Portniguina (2006). CPI is measured as monthly year to year changes and is displayed in a percentage as done by Schmeling (2009). Industrial production is the monthly year to year change and is measured in a percentage. The source is IFS which is consistent across countries. Industrial production is included based on evidence by Schmeling (2009) and Zouaoui, Nouyrigat and Beer (2011). Concluding that netting out macro risk effects is broadly done in the literature and necessary for an explanatory sentiment variable that is unrelated to fundamental or macro risk factors. Existing literature also highlights dividend yield to be an important macro risk factor but when using MSCI indices it is hard and time-consuming to construct dividend yields. I chose to use MSCI for good comparability also across different style and size stocks but the downside is that dividend yield cannot be added as a control factor. Baker, Wurgler and Yuan (2009) find that dividend in some markets is uncommon and dividends do not appear to be viewed by local investors as indicating stability. For the U.S. dividends show stability and therefore influence optimism and sentiment, however in an international setting this does not have to be the case. Thus dividend yield could be less important in an international setting. The results for the sentiment- return relation could still include some macro risk. However, when including dividend yield as a controlling factor this could still be the case. The data to explore cross country variance comes from dimensions of culture found by Hofstede. These are measured consistent across countries, the levels are used to pool countries in different panels. In this way culture serves as a possible determinant of cross-country variance. Chui, Titman and Wei (2008) use the dimensions individualism and uncertainty avoidance to motivate their herding and collectivism. Schmeling (2009) uses individualism and uncertainty avoidance as a possible determination of cross country variance. Zouaoui, Nouyrigat and Beer (2011) show that the impact of sentiment in countries culturally prone to herd-like behavior and overreaction is more pronounced based on Hofstede his dimensions. Extensive literatures in other fields also make use of Hofstede his dimensions to explain cross-country differences based on culture.
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Hofstede conducted a comprehensive study of how values in the workplace are influenced by culture based on employee value scores collected by IBM between 1967 and 1973 covering 70 countries and including about 88,000 respondents. First using only the largest countries which had the most respondents and later extending the analysis. In 2010 the 76 countries are listed from research partly based on replications and extensions to the IBM study. Hofstede’s (1980a) work has been criticized for: reducing culture to overly simplistic dimension conceptualization, failing to capture culture over time and ignoring within-country cultural heterogeneity (Sivakumar and Nakata, 2001). Despite this criticism, researchers have favored using his framework due to its clarity and parsimony4. This study looks at different countries and will try to find if culture affects the sentiment- return relationship. Since the field of research called “cultural finance” is rather new using Hofstede’s his dimensions as determinants of culture seem a good starting point. Further research can be done using different values and perhaps using a more complex measure of culture. Breuer and Quinten (2009) find that there is extensive literature using Hofstede’s his dimensions both in economics and finance. Using Hofstede’s dimensions thus seem a natural metric to use for the cross-country differences in culture. Table 1 provides the used countries, the number of participants for the consumer confidence index and the codes used for the countries. Table 2 provides an overview and a description of the variables used in the study including: variable names, used abbreviations, measures and sources.
3.2. Descriptive statistics Tables 3 and 4 provide descriptive statistics for the stock market data and the sentiment proxy. The tables provide country specific mean returns and standard deviations for stock markets and for sentiment the first-order autocorrelations are also provided. The statistics are shown for two time- series separately; table 3 runs from June 1994 until December 2006 and table 4 runs from January 2007 until May 2012. The periods are divided because of the economic crisis and this crisis can distort any findings and results, a first indication of any influence of the crisis can be seen by comparing the tables. The crisis became apparent in the summer of 2007, literature however concludes that macro variables have some predictive power therefore the period is split at the beginning of 2007 since macro variables may reflect the crisis before it became visible in falling stock markets. Table 3 represents the “normal” period. Value stocks have higher mean returns than growth stocks and lower standard deviations for most countries, a fact documented by literature by the
4 Appendix 4 contains a discussion of the advantages and the disadvantages of using Hofstede’s (2011) cultural dimensions. -18- value premium (Fama and French, 1998). The mean returns for small stocks are generally higher than for large stocks, evidence for the size premium (Fama and French, 1993). Overall small stocks have higher returns and standard deviations opposed to large stocks and value stocks have higher returns and lower standard deviations compared to growth stocks. Table 4 shows the “crisis” period. When comparing the table with the normal period some interesting differences can be seen. All returns are lower and standard deviations are generally higher. Some countries seem to be more affected by the crisis than others. For example Spain, Italy, France and the U.S. are affected the most and the Scandinavian countries, Australia and Germany seem less effected whereas New Zealand is almost not affected. This can be explained by the fact that the crisis predominantly hits Europe and the U.S., while Scandinavia, Australia and New Zealand are less influenced. The premiums found in the normal period are now less clear, change or even disappear. On average all returns decline for every country, some even realizing negative returns over this period. The crisis thus influences at least one side of the sentiment-return relationship. When looking at investor sentiment once again the effects of the crisis become clear. In the normal period the consumer confidence proxy means are higher on average than in the crisis period on average. This seems natural as during a crisis people are less optimistic or even pessimistic. Optimism should lead to overvaluation and lower future returns and pessimism should lead to undervaluation and higher future returns. The crisis period shows consumers to be pessimistic or at least be less optimistic so that the relation could differ. The effects of the crisis on the sentiment return relationship can be significant as clearly both sentiment and returns are affected. The descriptive statistics for the consumer confidence indices show a high degree of serial correlation in time-series. First-order autocorrelations are high and uniformly above 80%. Sentiment is quite variable and it shows strong persistence. The persistence can be explained to be a good characteristic for sentiment, as it suggests that investors are not too unsteady or inconstant and waves of optimism and pessimism may reinforce themselves. In the empirical analysis this serial correlations will be taken care of.
3.3. Preliminary tests The high autocorrelations in tables 3 and 4 suggest it to be interesting to test if the consumer confidence indices are unit-root non-stationary. These panel unit-root tests are displayed in table 6. The table includes: (a) tests for a common unit root (Levin, Lin, Chu, 2002), (b) individual unit-roots in the 12 consumer confidence series (Im, Pesaran and Shin) and (Philip-Perron-Fisher). The results show that consumer confidence, a proxy for sentiment, is data that is stationary, but highly persistent. Persistence is found to be a good characteristic as explained earlier. That sentiment turns out to be stationary is also statistically comforting. A stationary process is a stochastic process whose
-19- joint probability distribution does not change when shifted in time or space. Parameters such as the mean and variance do not change over time or position. In time series analysis, data is often transformed to be stationary as economic data is often seasonal. Non-stationary data is unpredictable and cannot easily be modeled or forecasted. The results may be spurious in that they can indicate a relationship between two variables where none does exist. The stationary characteristic thus allows me to proceed and use predictive regressions in trying to find a sentiment- return relation. As a simple test to check time-series dependencies between sentiment and stock returns I employ a Granger-causality test. Results for bivariate (stock returns and consumer confidence) Granger-causality tests and block exogeneity tests are shown in table 7. Following from the results there is a two-way “causality” such that sentiment depends on previous returns and returns depend on previous sentiment movements. Such a relation can be found for MSCI, SG, SV, MG and LV. In respect to earlier evidence the results provide a comforting first conformation that sentiment affects stock returns. Brown and Cliff (2004) find causality running from returns to sentiment for small and large stocks and find sentiment impacts returns of small but not that of large stocks. Schmeling (2009) finds two way “causality” for growth and value stocks. So in line with earlier literature small stocks have this two way “causality” and for mid and large cap stocks the results differ, finding the two way “causality” for MG but not for MV and for LV but not for LG. Qui and Welch (2006) find that sentiment should be related to some variable, returns, macro variables, etc. Sentiment must be driven by something and not just appear. Investors are overly optimistic or pessimistic following a series of good or bad news, returns or macro developments. So the results seem reasonable, in that sentiment drives returns and returns drive sentiment. Correlations between the measures of consumer confidence across countries are shown in table 8. The correlations are reassuring; the correlations are not prohibitively strong, so that essentially not one sentiment time-series is used for all countries. Cross country correlation coefficients range from -0.27 to 0.87. Several countries displays large correlations (e.g. Demark and Finland, Australia and New Zealand), some show nearly no correlation (e.g. Germany and Australia, France and the New Zealand) or negative correlation (e.g. New Zealand and Germany, U.K and Germany). These correlations are a first indication that there can be cross-country differences, an important indication as this paper focuses on exploring cross-country differences. Using a somewhat larger set of countries than used in this study table 5 gives the correlations between the cultural dimensions. They provide a check with the theory discussed before as correlations are derived theoretically. In the individual explanation of the effects of each dimension some correlations are already explored and these statistics confirm these correlations. Finding strong
-20- correlations for PDI with IND (-0.65), with UAI (0.57) and with IVR (-0.62). IND is correlated with both UAI (-0.54) and with IVR (0.55).
4. Predictive Regressions of Stock Returns on Consumer Confidence
The following section presents results of the predictive regressions and is structured as follows. 4.1 introduces the methodology; secondly the results from fixed panel regressions are presented in 4.2 Section 4.3 will present individual country findings and in 4.4 an attempt to determine the source of cross country variance is made, exploring the cultural effects. Section 4.5 relates this study to earlier literature and finally 4.6 will provide a discussion.
4.1. Methodology Earlier literature uses predictive panel regressions with different horizons. Schmeling (2009) uses a form of long-horizon predictive regressions to find if there is a relation between sentiment and returns. Brown and Cliff (2005) also employ long-horizon forecasts with overlapping horizons. For an easy comparison between the results this paper will also use this kind of regressions. Schmeling (2009) relates to the international character of this study, whereas Brown and Cliff (2005) relate the different style and size portfolios used in the study. The tests for sentiment effects on future returns are based on long-horizon return regressions of the form:
With the average k-period return for country i as dependent variable and several predictors on the right-hand side, including sentiment (sent) and macro variables summed in controls (TS, CPI, IND and SHORT). I estimate panel fixed-effects regressions so that all countries enter regressions jointly. Panel regressions are used to increase the power of the tests and to investigate if there is a significant sentiment-return relationship on average across countries. Second, I estimate the regression separately for each of the 12 countries in the sample and test for a significant impact of sentiment on future returns across horizons. The tests for significance will be for joint significance of the form: . To test the jointly significant impact at the 1,6,12,24 months horizon. This test has been employed earlier in the literature (Mark, 1995; Ang and Bekeart, 2007 and Schmeling, 2009) and is a more reasonable device to test for predictability rather than just testing individual horizons, due to the correlated results across horizons. Because of the lack of observations individually when looking at the 24 months horizon in the “crisis period” some caution is advised in interpreting these results. The joint
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significance test will therefore also be tested with the following limitations
Finally, I pool countries based on the scores of the cultural dimensions. I create two panels, one containing countries above the median and one containing countries below the median, this is done for each of the six dimensions. Using the different panels I run fixed-panel regressions of the form (1). To test for significance I employ Wald test of joint significance of the form:
. Testing the different panels allows for tests that shed light on the relation between cultural dimensions and sentiment. For example I can test if higher uncertainty avoidance leads to lower impacts of sentiment on returns. These seemingly simple regressions are however plagued by several econometric problems, so careful implementation of this method requires some corrections. The process of using average returns for the forecast horizon and than running regressions with these overlapping observations leads to strong correlation in the residuals. A second problem lies in the inclusion of a persistent independent or a predictor variable since this can bias the coefficient estimates as they are predetermined but not strictly exogenous (Stambaugh, 1999). More on the biased estimates of slope coefficients caused by persistent regressors is found by Valkanov (2003) or Ferson and Campbell (1991). Several authors rely on some sort of simulation procedure to account for these problems (Brown and Cliff, 2005, Schmeling, 2009). One could also quantify and adjust for biases using auxiliary regressions as done by Amihud and Hurvich (2004). Cambell and Yogo (2006) provide a method for efficient tests of stock return predictability in the presence of near unit-root regressors. Their method is however difficult to extend to multiple regressors and multi-period forecasts. Yet others have switched to using Hodrick (1992) standard errors (Ang and Bekeart, 2007; Lioui and Rangvid, 2007) and do not bias adjust coefficient estimates. Hansen and Hodrick (1980) provide standard errors correcting the overlapping and autocorrelation problems. However this correction does not work well in finite samples as found by Richardson and Stock (1989); Hodrick (1992); or Boudoukh and Richardson (1994). A second critique is that Hansen and Hodrick (1980) standard errors do not perform well when the degree of overlap is large relative to the size of the sample. In attempting to prevent this paper to become overly technical I chose to correct the bias using Hodrick (1992) standard errors rather than using a form of simulation. I do however acknowledge the shortcomings of this correction but as existing research uses this method as well, I am confident that this solves the problems in a reliable way.
4.2. Results for panel regressions Starting with the results for fixed-effects panel regressions (tables 9-12); these tables show separately the sentiment coefficients, the significance, the economic magnitude and R-squares. I
-22- show these results separately so that all style and size portfolios can be presented in the same table. In this way differences between the types of stocks become clear. First I will discuss results for the overall sentiment-return relation or hypothesis 1, second I will report the size and value effects or hypothesis 2. The results are universally negative on average as theory predicts. The coefficients reported are two times the standard deviation so the impact of sentiment is a rather large shock. Most coefficients become more negative over time. This pattern is consistent with limits of arbitrage hindering the ability of investors on profiting on mispricing that might persist for a significant amount of time (Brown and Cliff, 2005). The declining coefficients can also be due to long-horizon regressions (Hong, Torous and Valkanov, 2007). The decrease can result from a bias that mechanically generates significant results over longer horizons. This bias however is corrected by using Hodrick (1992) standard errors. The fact that for some style and size stocks the coefficients become less negative over a growing horizon is comforting, if there still is a bias this should not be the case. At the contrary Schmeling (2009) finds diminishing marginal impact which suggests that noise trading effects wash out over time, limits to arbitrage exist in the short run but become weaker in the long run. Overall the results are negative and significant thus supporting hypothesis 1 and finding results in line with earlier evidence. In the “crisis period” some positive coefficients are found, these results further confirm the first hypothesis. Optimism (pessimism) should lead to lower (higher) future returns leading to a negative (positive coefficient). Based on table 9 or the regression coefficient I find evidence for a value premium. Growth stocks are more heavily influenced than value stocks, at least for small stocks. For mid and large cap stocks this relation is not present, for those sizes value stocks are more influenced than growth stocks. Based on table 11 or significance no differences can be found for the small and midcap stocks. When looking at large stocks, value stocks are more significant than growth stocks, suggesting that value stocks are more influenced. Summarizing that the effects for value stocks are more pronounced than for growth stocks for mid and large cap stocks and for small cap stocks growth stocks are affected more heavily. Hypothesis 2 finds some support in general because there are differences between growth and value stocks but the evidence is mixed. The size effect is supported by these results as there are visible differences between small and large stocks. There is a pattern when looking at size. The small or mid cap stocks display more negative returns compared to large stocks. The coefficients find a clear relationship between size and the sentiment effect, with the effects being more pronounced for small stocks. The significance tells the same story, with the results being the most significant for small stocks. These results are in line with the theory and with previous results. Together with the results for the growth and value differences hypothesis 2 is supported in finding significant changes between sizes.
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It is interesting to note that there are observable differences found when looking at the different time periods. Before the crisis the negative relation is found as predicted since optimism should lead to lower future returns. However during the crisis positive coefficients are found on the 1 and 6 month horizons. When looking at sentiment during the crisis we can classify the sentiment as pessimistic or at least less optimistic. Pessimism should lead to undervaluation and higher future returns so for the short horizons the relationship is caused by pessimism coming from the crisis. At longer horizons the impacts turns negative again but lower than at the “normal period” pointing to the return of some optimism about the future after the period of pessimism. The results thus confirm the theory; optimism should lead to overpricing and lower future returns and pessimism to undervaluation and higher future returns. And one can conclude that the crisis definitely dampens optimism. Results are significant in economical terms when looking at the magnitude of the effect. The magnitude is calculated by taking the coefficient and multiplying the coefficient by the horizon and by the standard deviation of sentiment. Values in table 11 indicate the effect of one standard deviation increase in sentiment on the return over indicated horizon (in percent). For instance, a one standard deviation increase in sentiment of large value stocks is associated with a 3.08% decrease over a 12 months period. The average simple return for this portfolio is 12.7% concluding that there is an economically significant reduction. And not as severe, so the result seems plausible. Table 12 shows the R-squares of the regressions. They tend to be low which is usual for regressions that forecast stock returns. Looking at the change in R-squares sentiment seems to have quite some predicting power especially on the median and long horizons. This change resembles the added predictability when sentiment is added as a predicting variable. One has to be careful in interpreting the results for the crisis period. Due to a limited number of observations the results must be carefully interpreted and cautiously used to draw conclusions. This is especially the case with the 24 month horizon. With the individual countries results some special care will be given to this problem.
4.3. Results for individual countries This section presents the results for individual countries. Table 13 present the different time periods (panels A,B,C), different styles and size stocks are reported in the same table. The coefficients are mean coefficients across all horizons and the p-value is that of a joint significance Wald test. The tests for significance will be of the form: . There is quite some heterogeneity across countries, a significant relation between sentiment and returns is found with 8 countries for the aggregate market during the “normal” period, 2/3 of our sample. In the “crisis” 6 countries find a significant relation and for the full period 4 countries find
-24- significant relations. Sentiment seems to be country specific and hypotheses are not supported for all countries. When comparing periods the “crisis” seems to distort the results. When looking at both the “normal” and “crisis” periods the results are significant for more countries than for the full period. In most cases large differences between the crisis and the normal period can be detected. These differences probably cause the lack of significance when looking at the full period. In the crisis period the 1 month horizon causes positive coefficients and the mean coefficients are much lower because of that. Overall the coefficients are negative and significant. Sentiment effects seem to be country- specific and hypothesis 1 is not supported for all countries in the sample. When looking at the “normal” period on aggregate France, Germany, Spain and Italy are affected whereas Australia, Finland and Japan fail to be significant. The U.K. and U.S. cannot be seen as countries particularly prone to sentiment effects. The size premium does hold well in this sample of individual countries, on average the small stocks are influenced the most; if not usually the mid cap stocks are influenced more than the large stocks. With influence I mean that the coefficients are more negative for small or mid cap stocks. The significance of the regressions provides further evidence for the size relation. In the “normal” period mid cap stocks find a significant relationship in 8 countries for growth stocks and 9 for value stocks for large stocks a significant relationship is found for 7 countries for growth stock and at 6 countries for value stocks. Only for Denmark and the U.S. the coefficient for large stocks is more negative than for small stocks while remaining significant. The evidence for a possible value premium complements the findings of the panel-fixed regressions. For individual countries the results are also mixed. For small stocks the effect is more pronounced for growth stocks and for large the value stocks are most affected, finding similar results for individual countries. For Canada, Denmark and Italy growth stocks are more influenced than value stocks. The other countries corroborate the evidence found from the fixed panel regressions, finding the effect to be more pronounced with small growth stock while the being more pronounced for mid value and large value stocks. Both hypothesis 1 and 2 can thus not be accepted for all countries, a comforting results since I can now focus on the determination of these rather large cross-country differences. The 24 month horizon for the crisis period is somewhat limited due to the number of observation per country so the statistical power and significance of that test is lower than for other horizons. For that reason table 14 includes joint significance tests excluding this horizon. This table finds no real differences in the results found earlier but merely reinforces the relations stated before. The results become more reasonable, with more relations becoming insignificant so the differences between the normal and the crisis period are smaller.
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4.4. Cross-sectional analysis It is not clear what drives the differences between countries, not obviously location or size; therefore I explore the possibility that the culture of a country drives the magnitude of the effect. It is already documented that individualism and uncertainty avoidance are correlated. Whereas overconfidence and collectivism are said to partially cause the sentiment effect. Intuitively culture should effect the sentiment-return relation. I pool the countries based on dimensions by Hofstede (2011). Hofstede uses these dimensions basically to index culture in a country. Using different panels I can test the proposed cultural effects. Comparing these panels gives an indication of the effect that the dimensions have on the sentiment-return relation. The results are shown in tables 15 and 16 and clear some questions but cannot answer everything. Since I only pool countries based on the dimensions it is not clear which dimension is the most important. Moreover, other variables not included might drive the effect I assume to come from culture. However most dimensions show a pattern where only one of the subgroups is significant and the significant coefficients are larger than the insignificant. So there seems to be a difference between cultures. The tables 15 and 16 include the dimensions and the periods as well as the horizons. Again for comparability the significance and the coefficients are shown in separate tables. Due to the overall significance at the 24-month horizon the effect may be caused simply by the horizon rather than by the culture. Therefore to explore the culture effects table 17 shows joint significance over the 1,6,12 month horizons. A quick look at the tables shows large differences. The results for the above and for the below median panels show large differences at the one month horizon, the effects are of opposite sign for some dimensions. When looking at significance the average pattern is that only one of the two panels is significant. Overall the effects are in line with the theory of cultural effects. Power distance turns out to be somewhat ambiguous as both panels are significant during the crisis period and during normal periods the above median panel also comes close to being significant. The difference during the crisis can be explained by the corruption perception, a market integrity factor, during a crisis both countries that score high and low on power distance feel that the government has made mistakes thus raising the perception of corruption and less market integrity should lead to a more pronounced sentiment effect. Countries that score high on individualism are not affected, results are not significant. For collectivistic countries the effect is significant and negative coefficients are found. Differences between periods, “normal or crisis” are small. The more masculine countries are affected whereas results for feminine countries fail to be significant. The coefficients found are negative and become more negative over a longer period of time. Again there are small period differences. Countries that score high on uncertainty avoidance are the only countries that are affected by sentiment finding only significant results for these countries. This
-26- result is also found for countries scoring high on long-term orientation and for countries that are more restraint. Most relations between culture and sentiment are supported by theory. Only IVR behaves differently than the hypothesis predicted. This is probably due to correlation with IND implicating that countries with low IVR are collectivistic. This relation leads to herd-like behavior becoming an important factor for this particular dimension. Based on these simple tests one can conclude that culture indeed has an effect on sentiment; the sentiment-return relation even seems largely driven by culture. Most cultural dimensions have values such as overconfidence or conservatism, factors which determine the effect of sentiment according to psychological explanations. Also over- and under-reaction, representativeness, collectivism and herd-like behavior as well as self-attribution are factors used to explain the sentiment effect. Overall the cultural dimensions can be related to one of these values. So a countries culture, explains a countries behavior on aggregate and this behavior leads to sentiment affects.
4.5. Relation of results to earlier studies This section relates this paper’s findings to earlier documented effects. Due to the limited literature focusing on an international view some caution is advised since the sentiment-return relationship turns out to be very country-specific. Existing literature focuses on U.S. stock markets so although this can give an indication for the U.S. for other countries the results do not need to be exactly the same. Schmeling (2009) also has an international focus. Baker, Wurgler and Yuan (2009) find a global and local effect coming from sentiment. The next notion is that other evidence results from periods before the crisis, while Baker, Wurgler and Yaun (2009) comment on the crisis their data is not sufficient to make conclusions. When looking at the cultural factors some dimensions have been used to explore the sentiment relation, some have been used to find relations between other economic phenomena and yet others have not been used at all. Brown and Cliff (2005) provide a natural benchmark, the also provide results for the aggregate stock market, size, growth and value stock separately. They find a two standard deviation movement in their sentiment measure leading to a decline of stock returns of about 1.76% for a 6 month horizon 5.8% decline for a 12 month horizon. These numbers are similar 2.03% and 6.45% declines and thus seem reasonable and not implausible of size. To compare with a more international result Schmeling (2009) finds the declines to be 1.86% and 5.4%. Differences are a likely result of the countries used in this study. Most literature finds a negative relationship when optimistic and positive when pessimistic, consistent with my findings. Due to the length of the time series with both Schmeling (2009) and with Brown and Cliff (2005) it is probable that these time spans would have
-27- incurred crisis’s as well, the data runs for example from before the 1987 stock market crash. For that reason when comparing the results there is no distinction made between “normal” and “crisis”. For the effects of value and growth the literature does not provide a consistent picture. Brown and Cliff (2005) find a stronger effect for growth than for value stocks. Baker and Wurgler (2006) show that sentiment effects are similar size for both value and growth stocks. Baker, Wurgler and Yuan (2009) find in an international setting sentiment to be a statistically and economically significant predictor for high-sentiment-beta portfolios. The high-sentiment-beta portfolios are considered to be small, high volatility, distressed and growth portfolios. Lemmon and Portnaguina (2006) provide evidence that sentiment effects are significant for value but not for growth stocks. And Kumar and Lee (2006) show that noise traders tend to overweight value stocks relative to growth stocks. Schmeling (2009) finds value stocks to be more affected although both groups are significant. I find evidence consistent with Brown and Cliff (2005) in finding growth stocks to be more affected than value stocks for the small stocks. For mid and large cap stocks results are in line with Baker and Wurgler (2006) finding the relation to be more pronounced with value stocks. Many earlier papers have also looked at sentiment and the return of small and large stocks (Brown and Cliff, 2005); Lemmon and Portnaguinia, 2006 and Schmeling (2009). My results show that earlier U.S. evidence extends to international markets. Consistent with Schmeling (2009) based on international data, Brown and Cliff (2005) and Lemmon and Portnaguina (2006) based on U.S. data I find small stocks are more affected than large stocks. However my results differ in that there is a significant relation for large stocks which is not found by Schmeling (2009). Some evidence is in line for individual countries as well, mainly the U.S. data which finds a significant effect. For Australia Jackson (2004) finds no significant effect for noise trader induced returns and my results also are insignificant. Further Schmeling (2006) finds evidence for a significant impact in Germany. Extending the comparison to Schmeling (2009) he includes some of the same individual countries as I do. He finds strong relationships with Italy, Germany and Japan, where I find them for Germany and Italy but not for Japan. The effects of the U.K., U.S, Australia and New Zealand are consistent. Finland and Denmark show some differences mainly when looking at size and style. The differences are likely to come from the differences in portfolios were Schmeling (2009) uses only value and growth stocks, I use portfolios based on style and size differences. A second difference may come from the use of MSCI indexes were Schmeling (2009) uses returns found by Kenneth French. In looking at culture as an explanation of differences the results are consistent for at least two dimensions as only UAI and IND have been employed before. Schmeling (2009) finds the same results. Although not looking for the sentiment return relation but if sentiment affects stock crisis Zouaoui, Nouyrigat and Beer (2011) find that UAI and IND affect the sentiment relation in the same way as my results. They also give some indication that the sentiment effect is distorted by the crisis.
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They find investor sentiment to have an effect on the occurrence of a stock market crisis. Chui, Titman and Wei (2008) find the effects to be more pronounced for countries prone to herd-like behavior and overreaction. Market integrity is found to be significant by La Porta et al. (1998), Chui, Titman and Wei (2008) and by Schmeling (2009). Although this paper does not use a direct measure of market integrity some of the cultural dimensions (PDI, IVR) can be related to weaker or stronger market integrity, the results corroborate earlier finding that in countries with less market integrity the sentiment effects are more pronounced. Relating the other dimensions is harder since to my knowledge these dimensions have not been used to research the sentiment-return relationship up to this point. They are however found to be significant in earlier literature exploring different economic phenomena. Chui and Kwok (2008) find the inclusion of cultural factors to increase the predictive ability of the regression model on life insurance. de Jong and Semenov (2002) find lower levels of uncertainty avoidance and higher levels of masculinity leading to more developed stock markets. Park and Lemaire (2011) find long-term orientation to be significant in a way that countries that are long-term orientated will want more insurance. They also find this to be true for countries that exhibit low power distance and high individualism and uncertainty avoidance scores. It is hard to directly relate these earlier results but the overall picture is the same. The inclusion of cultural dimension increases the predictability and Hofstede’s dimensions are significant, the scores can well be used to explain cross-country differences. In earlier research significant effects are found if a country scores high or low on a dimension but not for both, consistent with my overall findings. The negative relationship on average is very consistent with earlier evidence. The same goes for the positive relationship when sentiment turns out to be pessimistic. The panel regressions are consistent with Baker and Wurgler (2006) finding significant effects for both groups. For small stocks they are consistent with Brown and Cliff (2005) and for large and mid cap stocks my results resemble the results found by Lemmon and Portnaguinia (2006) and Schmeling (2009). Overall the most resemblance is found in comparing results with Schmeling (2009). Due to the mixed consistency with the U.S. found evidence and the resemblances with the internationally orientated research, my results do not exclusively support earlier findings in the U.S. literature. The size premium is very consistent and only differs in finding some significance for large stocks as well. The idea that countries more prone to herd-like behavior and overreaction are more heavily affected by sentiment is also consistent with both theory and with earlier results. Findings on different dimensions are consistent in finding similar relations found compared to literature using the dimensions but looking at different financial and economic phenomena. Overall the fact that there is a significant cultural effect is consistent with existing literature. The differences are presumably caused by the use of
-29- different proxies, different stock markets, the use of different macro risk factors as controls and of course different countries.
4.6. Discussion This paper gives a first indication that culture influences financial phenomena. Based on Hofstede his dimensions one can conclude that for the sentiment-return relation culture is significant in determining the strength of the effect of noise trading on returns. Since it is known that Hofstede his dimensions have several limitations or disadvantages further research is needed in this field focusing on other dimensions and a more complex measure of culture. For a first exploratory research to the possibility of culture affecting the sentiment-return relation Hofstede his dimensions prove to be a useful metric of culture. Furthermore this research uses consumer confidence as a proxy for investor sentiment. To check for robustness other proxies for investor sentiment could be tested on the effects culture as well. For replicability MSCI indices are used, again as a test of robustness of the results different indices can be used to see if this produces on general similar results. The main focus was to explore cultural effects and for that reason some macro risk factors may still be included, a more thorough research might include dividend yield, the default spread, unemployment rate or consumption rate. Finally the used method to correct for econometrical problems might be less technical; it may also fail to correct the bias fully. Further research might thus opt to use a more technical form to correct this problems and rely on the use of estimation methods to bias correct the coefficients and standard errors. Short of these limitations the main contribution still stands, finding culture to be a cross- sectional determinant of the sentiment-return relation. To my knowledge no existing literature covers the 6 dimensions of Hofstede to define culture when testing the sentiment-return relation. This paper thus looks upon the relation both internationally and culturally, a view that is uncommon in literature and a view that thus needs extra attention. I therefore suggest more research is done to explain financial phenomena via the cultural way.
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5. Conclusion
I investigate the relation between investor sentiment and future stock returns for 12 developed countries and find that consumer confidence as a proxy for investor sentiment is a significant predictor of expected returns on average across countries. The largest shift in magnitude is when expanding from a 1 month horizon to a 6 month horizon. Overall the effects incline when looking at mid and large cap stocks and decline over time when looking at small cap stocks. The effect is more pronounced for small growth stocks and for mid and large value stocks. However the predictive power of sentiment varies across countries and sentiment does not contain predictive power for several countries in the sample. In order to investigate the cross country differences I look at possible determinates of the strength of the sentiment-return relation and find that the influence of noise traders on markets varies cross-sectionally in a way that is economically intuitive. The effect is more pronounced for countries that have less efficient regulatory institutions or less market integrity, countries that are more prone to herd-like behavior or countries that score on cultural dimensions related to the psychological values overreaction, self-attribution, conservatism, representativeness and collectivism. The effects are more pronounced for countries with high scores on UAI, MAS, ITOWVS, low on IND and IVR and for PDI the effect is ambiguous. One cannot transfer evidence from the U.S. to other markets and presume that irrational noise traders move stock markets in general. Moreover cultural factors as well as institutional quality are strong determinants of the sentiment-return relation. High quality markets institutions seem to alleviate effects of noise trading. Culture is however not easily changeable so that sentiment effects should remain a persistent phenomenon in countries. The sentiment-return relation turns out to be very country specific and to be partially determined by cultural differences. When looking at the crisis the results might contain at least slightly optimistic messages. Sentiment has played its part in past bubbles and crashes, implicating that the returns have not declined by 50 percent but that markets have exceeded the decline justified by a rational look at fundamentals. Currently valuation levels may be justified but earlier returns may have been inflated by sentiment. Although the crisis would then seem less severe, this interpretation is less comforting when looking at the future of returns.
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7. Appendices
7.1. Appendix I. Different proxies for investor sentiment Existing studies use different measures for (unobserved) sentiment ranging from direct measures, to indirect measure, to self-constructed indices. For example closed-end fund discounts (CEFD) are used by Lee, Shleifer and Thaler (1991), by Swaminathan (1996) and by Neal and Wheatley (1998). While Lee, Shleifer and Thaler (1991) find contemporaneously correlation of closed-end fund discounts with small stock returns, Chen, Kan and Miller (1993) find evidence rejecting this. Swaminathan (1996) finds CEFD forecasts the size premium and that information in discounts is related to expectations of future earnings growth and inflation. Neal and Wheatley (1998) also find evidence to support the size premium based on research using CEFD as a proxy for sentiment. Baker and Wurgler (2006) show that the returns on equity are prone to speculation and are difficult to arbitrage making their prices sensitive for changes in sentiment with differences in style and size (small, growth) investment characteristics. They find low periods of sentiment are followed by high returns on small, young, unprofitable and dividend-nonpaying stocks using a constructed proxy based on CEFD and several other market-based variables including the number of IPO’s, turnover, ect. These studies use indirect measures of sentiment that are made up of time series of macroeconomic and financial variables and therefore might not exclusively represent investors’ sentiment. The use of direct measures or surveys is evenly wide spread. Charoenroek (2006) finds that changes in consumer confidence help to forecast aggregate market returns in the United States. Lemmon and Portniaguina (2006) use consumer confidence as a proxy and find evidence supporting the size premium but not supportive of any variance in value or momentum premiums. Brown and Cliff (2004) use a survey of the American Association of Individual Investors and do not find any evidence that fund discounts reflect investor sentiment. Menkoff and Rebitzky (2008) also use survey data. Qui and Welch (2006) report only weak correlation between CEFD and consumer confidence and that only the consumer confidence measures are correlated with a measure of investor sentiment derived from UBS/Gallup. Making the consumer confidence measure as a proxy for sentiment the better choice. They also find consumer confidence to yield a robust contemporaneous correlation with the size premium. Fisher and Statman (2003) report positive correlations between measures of consumer confidence as a direct measure of investor sentiment compiled by the American Association of Individual Investors. Doms and Morin (2004) find, after controlling for macro economic factors, that consumer confidence is responding to the tone and volume of economic news rather than economic content. This implies a presence of an irrational factor in consumer confidence.
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Qui Welch (2006), Lemmon and Portniaguina (2006) and Ho and Hung (2009) present several additional arguments to support the proxy for sentiment. Participation of individual households in financial markets has increased over the recent years, suggesting that consumer confidence measures may be useful in measuring how individual investors think and feel about the economy and financial markets. For the U.S. the consumer confidence index lines up with anecdotal evidence of changes in sentiment. The consumer confidence index turns out to be very highly correlated with changes in stock prices although consumers participating in the survey are not asked directly for their views on security prices. Shleifer (2000) states that because the consumer confidence index captures individual beliefs it reflects the philosophy of behavioral finance by including opinions of imperfect people who have social, cognitive and emotional biases. A critique might be that consumer confidence does not proxy for investor sentiment but only captures some relevant macro information about time-varying risk premia that is not controlled for by other macro risk factors included in the regressions (Industrial Production, Term Spread, Inflation and Short Interest Rate). Earlier evidence has already pointed out consumer confidence contains an irrational element and that it is well suited as a device for tracking trader noise sentiment (Doms and Morin, 2004). Schmeling (2009) finds that sentiment remains statistically and economically significant as a predictor while expected business conditions show no significant forecasting power. Therefore it seems unlikely that consumer confidence is just a simple business cycle proxy which is not driven to insignificance by other control variables.
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7.2. Appendix II. Details on consumer confidence surveys In the data discussion section it is said that the consumer surveys are selected to resemble each other. This appendix provides details on how the consumer surveys are carried out in different countries. The objective of this appendix is to highlight similarities and discus possible differences across countries. Internationally there is a standardized set of questions for surveying consumer confidence. This standard comes close to the survey of the University of Michigan. All surveys of developed countries make use of this standardized survey to ensure international comparability. The questions asked regard both the past and future financial situation of the household, the past and future economic situation more generally and purchases of durable goods. These questions are the most important in every survey, although most surveys do ask additional questions, which can differ across the countries used in this research. For example the survey of the European commission asks questions regarding the expected employment situation and the CPI developments and the Australian survey asks for long-term expectations. However given that the core questions are extremely similar across the countries included, one might expect that consumer confidence indices are comparable internationally. One difference in the consumer confidence indices is with respect to the seasonal adjustments. All countries do seasonally adjust but some differences appear in the way they adjust for the seasons. For example the European commission uses “Dainties” while Japan adjusts based on “X-11”. There is no evidence in our results that using different procedures may affect econometric estimates quantitatively. The second difference is the forecast horizon. While most surveys ask questions about future developments the horizons differ. Most surveys (European) ask for one year horizons, Australia includes some additional questions over 5 year horizons and the Michigan surveys asks for one year horizons focusing on financial household situations but 5 year horizon regarding economic situations, thus forecast horizons differ somewhat between countries. Finally, the numbers of participants differ by countries; these numbers are displayed in table 1. Most surveys are based on more than 1000 households but there are large differences between large countries and small countries for example Denmark has 1500 participants and France 3300. Outliers are the U.S. with only 500 participants and Japan having more than 5000 participants.
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7.3. Appendix III. MSCI indices construction methodology The MSCI indices are based on Global Investable Market Indices (“GIMI”) methodology. This methodology has several advantages since it aims to provide coverage of the relevant investable opportunity set with non-overlapping size and style segmentation. Second, there is a strong emphasis on investability and replicability of the indices through the use of size and liquidity screens. The size segmentation aims to balance the objectives of global size integrity and country diversification. There is also a balance between index stability and timely reflection of changes in the opportunity set. Finally a complete and consistent index family with standard, large cap, mid cap, small cap and investable market indices provides a range of styles and sizes. Additionally the method uses minimum free float requirements for eligibility and free float-adjusted capitalization weighting to appropriately reflect the size of each investment opportunity and facilitate the replicability of the indices as well as timely and consistent treatment of corporate events and synchronized rebalancings globally. This leads to international consistent indices that can be compared. In certain cases, where there are no qualifying securities, it is possible for MSCI Indices to be empty following a security deletion or GICS change. If an index becomes empty it would be dynamically discontinued or ‘ruptured’. This is the case for the large growth index in New Zealand. It is then possible for the index to be re-started once a new security qualifies for the index, and this index level would be rebased to an appropriate level at that time. MSCI global value and growth indices categorize value and growth securities using clear and consistent sets of attributes and a rigorous methodological frame work. Style characteristics are defined using 8 historical and forward looking variables. Each security in an underlying MSCI index is given an overall style characteristic derived from its value and growth scores and placed in one of the two categories. The adjusted market capitalization of each constituent of the underlying index is fully represented in the combination of the value and growth index with no double counting. The value attribute is defined using book value to price ratio, 12 month forward earnings to price ratio and dividend yield. For the growth classification long-term forward looking earnings per share (EPS) growth rate, short-term forward EPS growth rate, current internal growth rate, long-term historical EPS growth trend and long-term historical sales per share growth trend are used. The large cap indices cover all investable large cap securities with a market capitalization of the above average targeting 70% of each market’s free-float adjusted market capitalization. Mid cap indices cover the mid 15% and small cap indices cover all investable small cap securities with a market capitalization below that of the companies in the MSCI standard indices, targeting approximately 14% of each market’s free float adjusted market capitalization. While MSCI index covers all investable securities.
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7.4. Appendix IV. Discussion of Hofstede’s dimensions This appendix will discuss the main critiques that the work has suffered during its existence. Hofstede’s work on culture is the most widely cited in existence (Smith and Bond 1999; Hofstede 2001). Such a widely used body of work does however not escape criticism. Arguments supporting and criticizing his work will be briefly explained below. McSweeny (2002) is concerned that the 4 or later 6 dimensions offered by Hofstede imply a simplistic view of culture and other dimensions should be considered to explain culture more wholly. Hofstede (2002) acknowledges this shortcoming but argues that the major dimension are the indentified but not necessarily all dimensions that differentiate cultures. He is also open to addition of other dimensions, LTOWVS by Bond (1991) and IVR by Minkov (2011). Williamson (2002) suggests that although Hofstede’s dimensions may seem simplistic in number and in their bi-polarity, they offer a method in which quantitative analyses can be pursued. Culture as said before is hard to test and operationalize, in order to simplify the operationalization and to allow at least some aspects of culture to be more easily applied researchers suggest the use of cultural indices. Further Hofstede his first assumption (1980) is that organizational, occupational and national culture are independent of each other thus he is ignoring within-country heterogeneity. Hofstede (2002) refutes this by stating that he measured differences between national cultures. He points out that national identity’s are only means available in identifying and measuring cultural differences. Often a point of criticize is the relative age of his data, so results come would from old data. This he combats this by the fact that the relative scores of his dimensions have been proven to be quite stable over decades. The forces that cause cultures to shift tend to be global or continent-wide. Meaning they affect many countries at the same time, so that if culture shifts is shifts in the same direction for these countries and their relative positions remain the same. Also culture is by definition a very slow changing factor, culture does not change overnight. Smith and bond (1999) conclude that large-scale studies published after Hofstede’s (1980a) work (Chinise Culture Connection, 1987; Schwarts, 1992, 1994; Trompenaars, 1993) have sustained and amplified Hofstede his conclusions. Ng, Lee and Soutar (2007) find Hofstedes’s and Schwartz’s value frameworks to be congruent in some ways. Researchers have used Hofstede his framework successfully to select countries that are culturally different in order to increase variance. Hofstede’s values are found to be relevant for additional cross-cultural research by Kirkman, Lowe and Gibson (2006).
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7.5. Appendix V. Tables
Table 1. Countries used in the study This table shows the set of countries used in the study, the code or abbreviation used in the study as well as the number of respondents to the consumer confidence surveys in each country.
Country Code Number of respondents to the consumer confidence survey Australia AU 1200 Canada CN 2000 Denmark DN 1500 Finland FN 1500 France FR 3300 Germany GER 2000 Italy IT 2000 Japan JAP 5000 New Zealand NZ 1000 Spain SP 2000 United Kingdom U.K. 2000 United States U.S. 500
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Table 2. Description of variables used in the study
Code Variables Measures Sources Macroeconomic variables CPI Inflation rate Year to year percentage of change International Financial Statistics IND Industrial production Year to year percentage of change International Financial Statistics TS Term spread Difference between the yields on 10-year government bonds National banks, European Central Bank and 3-month treasury bills and the Federal Reserve System. SHORT Short interest rate Stochastically detrended 3-month interbank interest-rate National banks, European Central Bank and the Federal Reserve System. Stock market variables MSCI Aggregate stock market index Total return of the index MSCI (datastream) SG Small growth stock index Total return of the smallest 14% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks characterized by growth SV Small value stock index Total return of the smallest 14% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks characterized by value MG Mid growth stock index Total return of the mid 15% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks characterized by growth MV Mid value stock index Total return of the mid 15% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks characterized by value LG Large growth stock index Total return of the largest 70% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks c characterized by growth LV Large value stock index Total return of the largest 70% of the market’s free float adjusted MSCI (datastream) market capitalization, with stocks characterized by value Investor sentiment indicator Sent Questions making up the consumer sentiment index. Directorate Generale for Economic and Financial Affiars, University of Survey Research Center, Datastream Cultural dimensions PDI Power distance index Hofstede's dimensions based on national culture, Geert Hofstede his website IND Individualism Survey on national level. Geert Hofstede his website MAS Masculinity Geert Hofstede his website UAI Uncertainty avoidance Geert Hofstede his website ITOWVS Long-term orientation Geert Hofstede his website IVR Indulgence versus restraint Geert Hofstede his website
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Table 3. Descriptive statistics This table shows descriptive statistics for the “normal” period meaning the period from before the crisis. It shows means (µ) and standard deviations (σ) for the market return, returns of value stocks, growth stocks and the size based differences. All returns are total returns which include dividends. The last three columns display the mean, standard deviation and first-order autocorrelation for the individual country consumer confidence indices.
"Normal" period, Time horizon: 1994m6 until 2006m12
Country MSCI Small growth Mid growth Large growth Small value Mid value Large value Confidence µ σ µ σ µ σ µ σ µ σ µ σ µ σ µ σ P(1) AU 0.99 3.10 1.62 2.73 0.61 4.00 0.80 3.74 1.62 2.73 1.32 2.41 1.30 3.04 107.37 7.19 0.67 CN 1.11 4.29 1.00 4.87 1.24 5.00 0.90 5.84 0.84 3.64 1.10 3.28 1.31 3.08 89.88 13.11 0.92 DN 1.26 4.58 1.16 4.83 0.77 5.98 1.07 5.50 1.58 4.88 1.18 6.19 1.37 3.75 10.60 4.59 0.83 FI 1.75 9.03 1.00 6.89 0.24 7.62 2.32 11.30 1.53 4.27 1.29 4.76 1.16 6.39 15.12 3.71 0.85 FR 0.99 4.82 0.88 5.55 0.97 5.42 0.91 4.46 1.13 3.66 1.19 4.06 1.18 4.74 -15.19 7.96 0.93 GER 0.93 5.53 0.31 5.98 0.69 5.32 0.90 5.72 0.66 3.78 0.95 4.29 0.77 4.52 -9.50 7.87 0.95 IT 0.96 5.49 0.69 5.29 1.19 6.89 0.83 5.41 1.25 4.68 1.12 5.57 1.08 5.17 -11.03 5.42 0.89 JAP 0.21 4.67 1.03 5.23 0.02 5.26 0.00 5.31 1.03 5.23 0.28 4.26 0.38 4.17 41.80 4.48 0.94 NZ 0.61 4.39 1.67 2.90 1.27 4.36 0.26 4.63 1.67 2.90 0.75 5.27 0.07 5.77 116.87 7.93 0.91 SP 1.41 5.42 1.11 4.26 1.06 4.87 1.10 6.33 1.64 3.64 1.79 4.40 1.52 4.70 -7.10 5.99 0.94 U.K. 0.79 3.41 0.82 5.24 1.00 5.47 0.67 3.93 1.12 3.82 0.95 3.64 0.92 3.28 -4.24 4.67 0.84 U.S. 0.99 3.88 1.10 5.71 1.00 6.05 0.82 4.41 1.13 3.60 1.18 3.34 1.03 3.12 95.21 8.31 0.89
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Table 4. Descriptive statistics This table shows descriptive statistics for the “crisis” period meaning the period from during and after the crisis. It shows means (µ) and standard deviations (σ) for the market return, returns of value stocks, growth stocks and the size based differences. All returns are total returns which include dividends. The last three columns display the mean, standard deviation and first-order autocorrelation for the individual country consumer confidence indices.
"Crisis” Period, Time horizon: 2007m1 until 2012m5
Country MSCI Small growth Mid growth Large growth Small value Mid value Large value Confidence µ σ µ σ µ σ µ σ µ σ µ σ µ σ µ σ P(1) AU -0.11 6.32 -0.14 5.13 0.19 4.84 0.19 4.84 -0.36 4.94 0.08 4.84 3.86 12.17 104.14 12.19 0.88 CN 0.16 4.61 0.16 7.22 0.35 5.59 -0.01 5.36 0.59 5.57 0.75 4.65 0.15 4.64 85.82 14.23 0.93 DN 0.50 6.13 -0.50 8.08 0.47 6.21 0.87 6.22 -0.11 9.23 -1.45 6.19 -0.57 8.72 8.19 6.80 0.88 FI -0.53 6.83 -0.02 8.11 0.66 8.03 0.56 8.58 0.06 7.72 0.13 7.70 -1.46 7.53 12.34 7.68 0.93 FR -0.32 5.43 0.19 6.38 0.07 5.58 -0.07 5.10 0.01 7.26 -0.19 6.63 -0.53 5.86 -19.76 9.08 0.93 GER 0.12 5.93 0.20 6.46 -0.09 7.78 0.37 5.04 0.97 7.48 -1.01 8.81 -0.10 6.72 -5.11 12.43 0.97 IT -1.04 6.70 -0.71 6.29 -0.97 5.85 -0.84 6.97 -1.53 6.83 -1.38 7.17 -1.00 6.87 -23.49 5.56 0.80 JAP -0.91 5.65 -0.70 5.27 -0.90 5.14 -1.12 5.93 -0.70 5.27 -0.66 5.55 -0.76 5.73 38.45 5.19 0.94 NZ -0.04 3.73 -0.14 3.40 -0.19 4.47 . . -0.14 3.40 0.75 4.47 0.80 4.35 107.78 8.48 0.85 SP -0.55 6.05 -0.70 6.45 -0.79 5.41 0.15 4.86 -1.35 6.54 -1.55 6.92 -0.68 7.97 -23.02 9.89 0.92 U.K. 0.23 4.68 0.61 5.40 0.30 5.06 0.42 4.65 0.18 6.55 0.04 5.48 0.10 5.23 -15.55 8.68 0.93 U.S. 0.24 4.91 0.63 6.46 0.42 6.05 0.45 4.80 0.43 6.45 0.21 6.21 0.01 4.86 71.38 9.41 0.83
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Table 5. Correlations between cultural dimensions This table shows correlations between cultural dimensions constructed by Hofstede (2011)
PDI IND MAS UAI ITOWVS IVR PDI 1.0000 IND -0.6503 1.0000 MAS 0.1425 0.0861 1.0000 UAI 0.5696 -0.5443 0.1015 1.0000 ITOWVS 0.2676 -0.1531 0.1470 0.2246 1.0000 IVR -0.6225 0.5536 -0.0076 -0.4461 -0.4830 1.0000
Table 6. Panel unit-root tests This table shows panel unit-root tests for consumer confidence indices. It shows t-statistics, p-values and number of observations for each test. The test by Levin, Lin, and Chu tests the null of a unit root assuming a common unit root process. The procedures of Im, Pesaran and Shin and Philips Perron- Fisher test null of unit assuming individual unit root processes. The lag length is chosen based on Schwarz’ Bayesian Information Criterion (SBIC) and test equations contain individual intercepts. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
Test-statistic P-value Obs Levin, Lin, and Chu -2.6875 ***(0.00) 2388 Im, Peraran and Shin -5.1366 ***(0.00) 2388 Philips Perron-Fisher 94.3468 ***(0.00) 2388
Table 7. Granger-causality tests Shown are the pair wise Granger-causality tests for sentiment and returns and tests for block- exogeneity. They are obtained from Vector Autoregressive Regressions (VAR) models including returns and sentiment. The table includes the market, the growth, the value and the size based returns. The lag length is chosen based on Schwarz’ Bayesian Information Criterion (SBIC). Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
MSCI SG SV MG MV LG LV Pairwise R→Sent ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) Sent→R **(0.02) *(0.08) *(0.08) **(0.05) 0.41 0.32 **(0.03) Block Exogeneity R→Sent ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) Sent→R ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00)
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Table 8. Consumer confidence correlations This table shows the correlations between the individual country consumer confidence indices.
AU CN DN FI FR GER IT JAP NZ SP U.K. U.S. AU 1.00 CN 0.42 1.00 DN 0.35 0.42 1.00 FI 0.37 0.46 0.62 1.00 FR 0.23 0.57 0.41 0.65 1.00 GER -0.01 0.25 0.19 0.45 0.65 1.00 IT 0.19 0.13 0.17 0.36 0.40 0.02 1.00 JAP 0.45 0.40 0.68 0.46 0.32 0.09 0.07 1.00 NZ 0.60 0.29 0.29 0.15 0.04 -0.27 0.28 0.48 1.00 SP 0.32 0.29 0.37 0.58 0.57 0.23 0.73 0.36 0.34 1.00 U.K. 0.46 0.51 0.53 0.61 0.48 -0.08 0.65 0.49 0.49 0.77 1.00 U.S. 0.32 0.28 0.30 0.55 0.50 0.05 0.69 0.34 0.33 0.87 0.79 1.00
Table 9. Sentiment coefficient in long-horizon regressions This table shows the coefficient on the sentiment regression of k-period returns on the lagged predictive variables. Explanatory variables are constant. The full data set is 2552 observations from 4/1994 to 5/2012. Each panel has k fewer observations due to construction of long-horizon returns. The “crisis” period starts with 780 observations and the “normal period” with 1772. Regressions are corrected for autocorrelation using Hodrick standard errors.
Time-Period MSCI SG SV MG MV LG LV A. 1-Month Horizon 1994m6-2012m5 0.028 0.019 0.029 0.024 0.021 0.019 0.023 1994m6-2006m12 -0.009 -0.049 -0.034 -0.028 -0.025 -0.010 -0.018 2007m1-2012m5 0.061 0.046 0.029 0.054 0.027 0.071 0.058 B. 6-Month Horizon 1994m6-2012m5 -0.019 -0.060 -0.050 -0.024 -0.029 -0.019 -0.029 1994m6-2006m12 -0.039 -0.111 -0.088 -0.053 -0.055 -0.033 -0.036 2007m1-2012m5 -0.023 -0.077 -0.098 -0.047 -0.066 0.000 -0.064 C. 12-Month Horizon 1994m6-2012m5 -0.030 -0.064 -0.048 -0.025 -0.034 -0.028 -0.040 1994m6-2006m12 -0.037 -0.093 -0.067 -0.036 -0.049 -0.033 -0.038 2007m1-2012m5 -0.049 -0.109 -0.113 -0.071 -0.077 -0.025 -0.085 D. 24-Month Horizon 1994m6-2012m5 -0.041 -0.057 -0.032 -0.035 -0.039 -0.040 -0.046 1994m6-2006m12 -0.042 -0.067 -0.034 -0.036 -0.049 -0.038 -0.047 2007m1-2012m5 -0.051 -0.095 -0.083 -0.072 -0.066 -0.043 -0.063
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Table 10. Sentiment significance in long-horizon regressions This table shows the Significance of the sentiment regression of k-period returns on the lagged predictive variables. Explanatory variables are constant. The full data set is 2552 observations from 4/1994 to 5/2012. Each panel has k fewer observations due to construction of long-horizon returns. The “crisis” period starts with 780 observations and the “normal period” with 1772. Regressions are corrected for autocorrelation using Hodrick standard errors. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
Time-Period MSCI SG SV MG MV LG LV A. 1-Month Horizon 1994m6-2012m5 **(0.04) 0.29 *(0.07) 0.14 0.13 0.25 *(0.10) 1994m6-2006m12 0.62 *(0.07) *(0.09) 0.21 0.15 0.67 0.29 2007m1-2012m5 ***(0.01) *(0.10) 0.32 **(0.04) 0.32 ***(0.01) **(0.04) B. 6-Month Horizon 1994m6-2012m5 0.16 ***(0.00) ***(0.00) 0.14 ***(0.00) 0.26 **(0.04) 1994m6-2006m12 **(0.03) ***(0.00) ***(0.00) **(0.02) ***(0.00) 0.15 **(0.03) 2007m1-2012m5 0.25 ***(0.00) ***(0.00) **(0.04) ***(0.00) 0.99 ***(0.01) C. 12-Month Horizon 1994m6-2012m5 **(0.02) ***(0.00) ***(0.00) *(0.09) ***(0.01) *(0.09) ***(0.00) 1994m6-2006m12 **(0.04) ***(0.00) ***(0.00) *(0.09) ***(0.00) 0.15 **(0.02) 2007m1-2012m5 ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) 0.18 ***(0.00) D. 24-Month Horizon 1994m6-2012m5 ***(0.00) ***(0.00) **(0.01) ***(0.00) ***(0.00) ***(0.01) ***(0.00) 1994m6-2006m12 **(0.01) ***(0.00) **(0.02) **(0.04) ***(0.00) *(0.07) ***(0.00) 2007m1-2012m5 ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00)
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Table 11. Economic magnitude of the sentiment effect This table shows the economic magnitude of a one standard deviation shock to sentiment, computed by multiplying the sentiment coefficient with both horizon and standard the deviation of sentiment. Explanatory variables are constant. The full data set is 2552 observations from 4/1994 to 5/2012. Each panel has k fewer observations due to construction of long-horizon returns. The “crisis” period starts with 780 observations and the “normal period” with 1772. Regressions are corrected for autocorrelation using Hodrick standard errors. The magnitude is displayed in percentages.
Time-Period MSCI SG SV MG MV LG LV A. 1-Month Horizon 1994m6-2012m5 0.24 0.17 0.26 0.21 0.19 0.17 0.20 1994m6-2006m12 -0.06 -0.33 -0.23 -0.19 -0.17 -0.07 -0.12 2007m1-2012m5 0.56 0.42 0.27 0.49 0.24 0.64 0.53 B. 6-Month Horizon 1994m6-2012m5 -1.02 -3.19 -2.64 -1.25 -1.52 -0.98 -1.53 1994m6-2006m12 -1.59 -4.51 -3.57 -2.15 -2.25 -1.35 -1.47 2007m1-2012m5 -1.24 -4.21 -5.38 -2.57 -3.60 -0.02 -3.52 C. 12-Month Horizon 1994m6-2012m5 -3.23 -6.82 -5.07 -2.69 -3.64 -2.94 -4.20 1994m6-2006m12 -3.03 -7.57 -5.41 -2.89 -3.96 -2.71 -3.08 2007m1-2012m5 -5.37 -11.91 -12.37 -7.80 -8.46 -2.74 -9.32 D. 24-Month Horizon 1994m6-2012m5 -8.65 -12.15 -6.76 -7.51 -8.34 -8.57 -9.67 1994m6-2006m12 -6.78 -10.93 -5.59 -5.79 -7.92 -6.11 -7.62 2007m1-2012m5 -11.28 -20.94 -18.11 -15.79 -14.42 -9.32 -13.80
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Table 12. R-squares of sentiment regressions This table shows the R-squares as well as the change in R-Squares when including sentiment as a predictor in the regression. Explanatory variables are constant. The full data set is 2552 observations from 4/1994 to 5/2012. Each panel has k fewer observations due to construction of long-horizon returns. The “crisis” period starts with 780 observations and the “normal period” with 1772. Regressions are corrected for autocorrelation using Hodrick standard errors. Change in R-square is calculated by deducting R-squares of regressions without sentiment with those including sentiment.
Time-Period MSCI Small growth Small value Mid growth Mid value Large growth Large value
A. 1-Month Horizon 1994m6-2012m5 0.06 0.00 0.08 0.00 0.08 0.01 0.04 0.00 0.04 0.00 0.04 0.00 0.06 0.01 1994m6-2006m12 0.01 0.00 0.02 0.01 0.02 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.01 0.00 2007m1-2012m5 0.21 0.01 0.24 0.01 0.21 0.00 0.21 0.01 0.16 0.00 0.18 0.02 0.17 0.01 B. 6-Month Horizon 1994m6-2012m5 0.11 0.01 0.13 0.02 0.12 0.02 0.07 0.01 0.08 0.01 0.09 0.01 0.10 0.01 1994m6-2006m12 0.05 0.02 0.09 0.06 0.08 0.06 0.03 0.02 0.04 0.03 0.06 0.01 0.03 0.02 2007m1-2012m5 0.36 0.00 0.39 0.03 0.42 0.05 0.34 0.01 0.35 0.03 0.29 0.00 0.36 0.03 C. 12-Month Horizon 1994m6-2012m5 0.16 0.02 0.16 0.04 0.13 0.03 0.11 0.01 0.12 0.03 0.13 0.01 0.15 0.04 1994m6-2006m12 0.11 0.03 0.12 0.07 0.09 0.07 0.06 0.01 0.06 0.05 0.13 0.02 0.08 0.04 2007m1-2012m5 0.41 0.04 0.48 0.12 0.47 0.13 0.44 0.07 0.46 0.08 0.31 0.01 0.40 0.10 D. 24-Month Horizon 1994m6-2012m5 0.24 0.05 0.19 0.07 0.14 0.03 0.17 0.03 0.21 0.05 0.18 0.03 0.26 0.07 1994m6-2006m12 0.25 0.04 0.16 0.07 0.11 0.04 0.14 0.02 0.18 0.11 0.24 0.01 0.23 0.08 2007m1-2012m5 0.72 0.14 0.74 0.23 0.66 0.17 0.72 0.18 0.67 0.15 0.66 0.09 0.64 0.15
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Table 13. Return predictability for individual countries across horizons This table shows the results for individual country regressions during the “full, “normal” and “crisis” periods. Future stock returns serve as the dependent variable and sentiment and control variables are used as predictive variables. The coefficients shown are the mean coefficients across the forecast horizons 1,6,12,24 months. The reported p-values refer to tests of joint significance, that coefficients across forecast horizons are jointly equal to zero. Regressions are corrected for autocorrelation using Hodrick standard errors. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
Panel A: "Full" period, 1994m6-2012m5 MSCI Small growth Small value Mid growth Mid value Large growth Large value Country µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val AU 0.041 0.91 0.014 0.68 0.014 0.68 0.051 0.65 0.009 0.62 0.034 0.83 -0.026 0.18 CN -0.005 0.51 -0.014 0.33 -0.014 0.71 -0.013 0.31 -0.022 ***(0.00) 0.011 0.71 -0.017 **(0.03) DN -0.059 ***(0.00) -0.149 ***(0.00) -0.124 ***(0.00) 0.013 **(0.02) -0.094 ***(0.00) -0.071 **(0.02) -0.115 ***(0.00) FN -0.007 0.15 -0.244 ***(0.00) -0.249 ***(0.00) -0.108 ***(0.00) -0.118 ***(0.00) 0.083 0.55 -0.083 ***(0.00) FR -0.127 ***(0.00) -0.110 ***(0.01) -0.081 *(0.07) -0.133 ***(0.00) -0.092 **(0.01) -0.130 ***(0.00) -0.120 ***(0.00) GER -0.130 ***(0.00) -0.194 ***(0.00) -0.165 ***(0.00) -0.134 ***(0.00) -0.146 ***(0.00) -0.119 ***(0.00) -0.129 ***(0.00) IT -0.040 0.53 -0.073 0.36 0.005 0.98 -0.075 0.54 -0.031 0.62 -0.068 0.27 -0.032 0.61 JAP 0.068 0.75 0.085 0.11 0.085 0.11 0.023 0.70 0.044 0.46 0.070 0.93 0.067 0.80 NZ 0.039 0.32 0.046 0.59 0.046 0.59 0.071 0.13 0.050 0.16 0.111 0.24 0.137 0.17 SP -0.004 0.68 0.040 0.50 0.050 0.14 -0.003 0.86 0.064 0.24 -0.056 *(0.08) -0.015 0.53 U.K. 0.007 0.64 0.009 0.75 -0.045 0.10 0.020 0.94 -0.044 **(0.05) 0.036 0.79 -0.014 0.28 U.S. -0.020 **(0.03) -0.021 *(0.09) -0.016 ***(0.00) -0.012 0.25 -0.005 **(0.03) -0.033 **(0.04) -0.009 **(0.03)
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Panel B: "Normal" period, 1994m6-2006m12 MSCI Small growth Small value Mid growth Mid value Large growth Large value Country µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val AU 0.036 ***(0.00) 0.032 0.32 0.032 0.32 0.052 *(0.06) 0.010 *(0.07) 0.037 *(0.08) -0.030 0.48 CN -0.065 0.29 -0.061 0.30 -0.051 0.41 -0.079 0.36 -0.048 *(0.08) -0.073 0.46 -0.056 0.10 DN -0.058 **(0.03) -0.096 **(0.04) -0.012 0.26 -0.047 ***(0.00) -0.005 **(0.01) -0.125 **(0.04) -0.058 **(0.04) FN 0.252 0.99 -0.184 ***(0.00) -0.211 ***(0.00) -0.019 **(0.04) -0.078 ***(0.00) 0.357 0.90 -0.112 0.15 FR -0.143 ***(0.01) -0.121 *(0.06) -0.105 *(0.08) -0.146 **(0.02) -0.109 **(0.01) -0.130 **(0.02) -0.141 **(0.02) GER -0.196 ***(0.00) -0.263 ***(0.00) -0.184 ***(0.00) -0.164 ***(0.00) -0.164 ***(0.00) -0.189 ***(0.00) -0.191 ***(0.00) IT -0.190 ***(0.00) -0.210 ***(0.00) -0.185 ***(0.00) -0.236 ***(0.00) -0.212 ***(0.00) -0.215 ***(0.00) -0.185 ***(0.00) JAP 0.056 0.98 -0.080 **(0.03) -0.080 **(0.03) 0.025 0.89 0.047 0.72 0.050 0.79 0.060 0.90 NZ 0.044 *(0.09) 0.009 0.72 0.009 0.72 0.050 **(0.03) 0.018 0.54 0.092 0.29 0.144 0.15 SP -0.164 ***(0.00) -0.135 ***(0.00) -0.166 ***(0.00) -0.246 ***(0.00) -0.135 ***(0.00) -0.176 ***(0.00) -0.206 ***(0.00) U.K. 0.022 0.41 0.003 0.98 -0.047 0.36 0.048 0.45 -0.039 0.30 0.060 0.30 -0.003 0.85 U.S. -0.045 ***(0.00) -0.043 0.15 -0.041 ***(0.00) -0.029 0.35 -0.034 ***(0.00) -0.046 *(0.06) -0.044 ***(0.00) Panel C: "Crisis" period, 2007m1-2012m5 MSCI Small growth Small value Mid growth Mid value Large growth Large value Country µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val µ Coef P-val AU -0.031 ***(0.00) -0.095 ***(0.01) -0.095 ***(0.01) -0.055 **(0.01) -0.071 ***(0.00) -0.033 **(0.01) -0.094 ***(0.00) CN -0.029 ***(0.00) -0.045 ***(0.00) -0.062 ***(0.00) -0.054 ***(0.00) -0.042 ***(0.00) -0.014 ***(0.00) -0.043 ***(0.00) DN -0.063 ***(0.00) -0.174 ***(0.00) -0.185 ***(0.00) -0.017 ***(0.00) -0.104 **(0.03) -0.035 ***(0.00) -0.170 ***(0.00) FN -0.108 ***(0.00) -0.260 ***(0.00) -0.234 0.12 -0.221 **(0.01) -0.218 ***(0.00) -0.056 ***(0.00) -0.124 **(0.01) FR -0.088 **(0.05) -0.105 *(0.09) -0.167 0.14 -0.096 **(0.04) -0.171 0.05 -0.143 ***(0.00) -0.108 0.14 GER 0.010 ***(0.00) 0.015 ***(0.00) -0.057 **(0.03) 0.019 ***(0.00) 0.012 ***(0.00) 0.005 ***(0.00) 0.003 ***(0.00) IT -0.245 ***(0.00) -0.198 ***(0.00) -0.192 ***(0.00) -0.161 ***(0.00) -0.214 ***(0.00) -0.243 ***(0.00) -0.253 ***(0.00) JAP -0.013 ***(0.00) -0.118 ***(0.00) -0.118 ***(0.00) -0.057 ***(0.00) -0.065 ***(0.00) 0.017 ***(0.00) -0.025 ***(0.00) NZ -0.008 ***(0.00) 0.005 ***(0.00) 0.005 ***(0.00) 0.054 ***(0.00) 0.074 0.52 . . -0.429 0.36 SP -0.037 0.13 -0.057 *(0.08) -0.088 0.13 -0.013 0.17 -0.052 0.27 -0.062 **(0.05) -0.034 0.27 U.K. -0.044 **(0.01) -0.061 ***(0.01) -0.112 ***(0.00) -0.050 ***(0.00) -0.091 ***(0.00) -0.028 **(0.02) -0.052 **(0.04) U.S. 0.040 ***(0.00) 0.027 ***(0.00) 0.009 ***(0.00) 0.043 ***(0.00) 0.027 ***(0.00) 0.028 ***(0.00) 0.051 ***(0.00)
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Table 14. “Crisis” joint significance This table shows the joint significance excluding the 24 months limited observations horizon. The table shows the “crisis” period. Regressions are corrected for autocorrelation using Hodrick standard errors. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
Country MSCI SG SV MG MV LG LV 2007m1-2012m5 AU *(0.05) **(0.03) **(0.03) *(0.07) ***(0.01) *(0.09) ***(0.00) CN 0.12 *(0.09) ***(0.00 **(0.04) **(0.01) 0.58 ***(0.00) DN 0.15 **(0.02) *(0.05) 0.44 **(0.03) 0.25 0.15 FN **(0.03) ***(0.00) ***(0.00) 0.01 ***(0.00) 0.25 **(0.02) FR ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) GER 0.12 **(0.06) ***(0.00) 0.29 0.30 *(0.08) 0.11 IT 0.19 0.21 0.16 0.24 0.18 0.20 0.15 JAP ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) **(0.03) ***(0.00) NZ 0.82 0.74 0.74 0.67 0.12 . 0.36 SP ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) ***(0.00) U.K. 0.23 0.61 0.14 0.25 0.33 0.33 0.13 U.S. *(0.08) 0.16 0.37 0.08 0.24 *(0.09) *(0.06)
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Table 15. Sentiment coefficient in long-horizon regressions: behavioral panels This table shows the results for the predictive regressions based on the two pooled subsets. The table shows the below and above median results. The first panel includes PDI, IND and MAS and the second panel includes UAI, ITOWVS and IVR. Regressions are corrected for autocorrelation using Hodrick standard errors. This table shows the regression coefficients.
Panel A: Time-Period PDI IND MAS < > < > < > A. 1-Month Horizon 1994m6-2012m5 0.044 0.008 0.028 0.034 0.044 0.018 1994m6-2006m12 0.037 0.007 0.021 0.027 0.046 0.005 2007m1-2012m5 0.038 -0.004 0.023 0.029 0.052 0.004 B. 6-Month Horizon 1994m6-2012m5 -0.019 -0.026 -0.039 -0.003 -0.001 -0.036 1994m6-2006m12 -0.026 -0.026 -0.045 -0.008 0.000 -0.048 2007m1-2012m5 -0.024 -0.053 -0.045 -0.004 -0.010 -0.046 C. 12-Month Horizon 1994m6-2012m5 -0.043 -0.028 -0.062 -0.011 -0.017 -0.042 1994m6-2006m12 -0.045 -0.028 -0.067 -0.011 -0.017 -0.047 2007m1-2012m5 -0.052 -0.054 -0.068 -0.011 -0.036 -0.050 D. 24-Month Horizon 1994m6-2012m5 -0.058 -0.033 -0.089 -0.017 -0.032 -0.051 1994m6-2006m12 -0.061 -0.039 -0.092 -0.017 -0.040 -0.056 2007m1-2012m5 -0.071 -0.061 -0.096 -0.019 -0.059 -0.059 Panel B: Time-Period UAI ITOWVS IVR < > < > < > A. 1-Month Horizon 1994m6-2012m5 0.042 0.015 0.041 0.005 0.015 0.042 1994m6-2006m12 0.036 0.013 0.036 -0.001 0.013 0.036 2007m1-2012m5 0.037 0.009 0.036 0.017 0.009 0.037 B. 6-Month Horizon 1994m6-2012m5 0.004 -0.049 -0.001 -0.052 -0.049 0.004 1994m6-2006m12 0.000 -0.045 -0.004 -0.054 -0.045 0.000 2007m1-2012m5 0.003 -0.055 -0.002 -0.047 -0.055 0.003 C. 12-Month Horizon 1994m6-2012m5 -0.007 -0.066 -0.015 -0.060 -0.066 -0.007 1994m6-2006m12 -0.008 -0.063 -0.015 -0.060 -0.063 -0.008 2007m1-2012m5 -0.008 -0.063 -0.017 -0.057 -0.063 -0.008 D. 24-Month Horizon 1994m6-2012m5 -0.019 -0.085 -0.027 -0.070 -0.085 -0.019 1994m6-2006m12 -0.019 -0.081 -0.025 -0.070 -0.081 -0.019 2007m1-2012m5 -0.021 -0.080 -0.030 -0.063 -0.080 -0.021
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Table 16. Sentiment significance in long-horizon regressions: behavioral panels This table shows the results for the predictive regressions based on the two pooled subsets. The table shows the below and above median results. Panel A includes PDI, IND and MAS and panel B includes UAI, ITOWVS and IVR. This table shows the regression significance. Regressions are corrected for autocorrelation using Hodrick standard errors. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10. Panel A: Time-Period PDI IND MAS < > < > < > A. 1-Month Horizon 1994m6-2012m5 **(0.05) 0.65 0.31 **(0.01) **(0.03) 0.33 1994m6-2006m12 0.13 0.69 0.46 **(0.05) **(0.03) 0.80 2007m1-2012m5 0.14 0.87 0.43 **(0.04) **(0.03) 0.84 B. 6-Month Horizon 1994m6-2012m5 0.39 0.13 0.14 0.83 0.94 *(0.07) 1994m6-2006m12 0.27 0.17 *(0.10) 0.57 0.99 **(0.02) 2007m1-2012m5 0.33 **(0.01) 0.11 0.80 0.66 **(0.04) C. 12-Month Horizon 1994m6-2012m5 **(0.04) 0.10 **(0.02) 0.42 0.40 **(0.02) 1994m6-2006m12 **(0.05) 0.13 ***(0.01) 0.40 0.43 **(0.02) 2007m1-2012m5 **(0.02) ***(0.01) **(0.01) 0.42 0.12 **(0.01) D. 24-Month Horizon 1994m6-2012m5 ***(0.00) **(0.03) ***(0.00) *(0.09) *(0.06) ***(0.00) 1994m6-2006m12 ***(0.00) **(0.02) ***(0.00) 0.12 **(0.04) ***(0.00) 2007m1-2012m5 ***(0.00) ***(0.00) ***(0.00) *(0.08) ***(0.00) ***(0.00) Panel B: Time-Period UAI ITOWVS IVR < > < > < > A. 1-Month Horizon 1994m6-2012m5 ***(0.00) 0.57 **(0.02) 0.82 0.57 ***(0.00) 1994m6-2006m12 ***(0.01) 0.63 *(0.05) 0.98 0.63 ***(0.01) 2007m1-2012m5 ***(0.01) 0.70 *(0.06) 0.48 0.70 ***(0.01) B. 6-Month Horizon 1994m6-2012m5 0.79 *(0.06) 0.97 **(0.02) *(0.06) 0.79 1994m6-2006m12 0.97 *(0.09) 0.83 **(0.02) *(0.09) 0.97 2007m1-2012m5 0.82 **(0.02) 0.90 *(0.05) **(0.02) 0.82 C. 12-Month Horizon 1994m6-2012m5 0.54 ***(0.01) 0.37 ***(0.01) ***(0.01) 0.54 1994m6-2006m12 0.54 **(0.02) 0.42 ***(0.01) **(0.02) 0.54 2007m1-2012m5 0.55 ***(0.01) 0.35 **(0.01) ***(0.01) 0.55 D. 24-Month Horizon 1994m6-2012m5 **(0.04) ***(0.00) *(0.06) ***(0.00) ***(0.00) **(0.04) 1994m6-2006m12 *(0.06) ***(0.00) *(0.10) ***(0.00) ***(0.00) *(0.06) 2007m1-2012m5 **(0.03) ***(0.00) *(0.06) ***(0.00) ***(0.00) **(0.03)
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Table 17. Cultural joint significance This table shows the significance jointly tested across the 1,6,12 month horizons. The reported p- values refer to tests of joint significance, coefficients across forecast horizons are jointly equal to zero. Regressions are corrected for autocorrelation using Hodrick standard errors. Stars refer to the level of significance: ***:0.01, **:0.05,*:0.10.
Time-Period 1994m6-2012m5 1994m6-2006m12 2007m1-2012m5 Cultural < > < > < > dimensions PDI **(0.04) 0.10 **(0.05) 0.13 **(0.02) ***(0.01) IDV **(0.01) 0.42 ***(0.01) 0.40 **(0.01) 0.42 MAS 0.39 **(0.02) 0.43 **(0.01) 0.12 **(0.01) UAI 0.54 ***(0.01) 0.54 **(0.02) 0.55 ***(0.01) ITOWVS 0.37 ***(0.01) 0.42 ***(0.01) 0.35 **(0.01) IVR ***(0.01) 0.54 **(0.02) 0.54 ***(0.01) 0.55
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