The Price of Sin Aversion: Ivory Tower Illusion or Real Investable Alpha?

Transcription

1 The Price of Sin Aversion: Ivory Tower Illusion or Real Investable Alpha? Hampus Adamsson a* & Andreas G. F. Hoepner b a Centre for Responsible Banking & Finance, School of Management, University of St. Andrews, The Gateway, North Haugh, St. Andrews, KY16 9SS, UK b ICMA Centre, Henley Business School, University of Reading, Whiteknights Campus, Reading, RG6 6BA, UK Abstract In their pioneering work, Hong & Kacperczyk [HK] (2009) document a significant outperformance of socalled sin stocks: publicly traded companies involved in producing alcohol, tobacco and gaming. Drawing on theories of neglected stocks and segmented markets, the authors suggest this outperformance to be due to a shunning effect caused by norm-constrained institutional investors who effectively pay a price for sin aversion. Motivated by a line of criticism against their research design, this study re-examines whether the sin stocks premium recorded by HK is actually investable for real world investors or derives from an ivory tower style research design? We limit our analysis to only those stocks included in the global equity index benchmarks of institutional investors thereby excluding thousands of stocks usually considered too illiquid in practice. Among these stocks, we still document the sin stock premium in HK s equal weighted sin-industry portfolios. Equal-weighted sub-industry portfolios are, however, practically barely investable for many institutional investors with their value-weighted benchmarks and are, if analysed in Fama-French models, exposed to a small cap bias in two ways. First, if small cap firms outperform large cap firms, then the equal-weighted portfolio is likely to outperform its value-weighted equivalent and a value-weighted market. Hence, we value-weight our sin-industry portfolios, which leads the premium for gambling firms to disappear. Second, Fama-French s model controls for return differentials between small cap and large cap firms within all industrial sectors. The model is not designed to capture return differentials between small cap and large cap firms in a single sector such as consumer goods, which could drive the returns of sub-industries in this sector such as alcohol and tobacco. Hence, we add within sector control variables to our model which leads to a complete disappearance of any premium to sin stocks at the global level. When we conduct the same analysis in HK s purely US universe, the sin stock premiums disappear even more convincingly. This implies that the sin stock premium and the price of sin aversion could be artefacts of an ivory tower research design and are not necessarily applicable to the real world. *Contact author: [email protected] Acknowledgements: We would like to thank Adrian Bell, Tony Berrada, Kais Bouslah, Chris Brooks, Rajna Gibson Brandon, Chris Godfrey, Guan Huang, Philipp Krueger, Ioannis Oikonomou, Olivierm Scaillet, Lisa Schopohl and Charles Ward and seminar participants at the ICMA Centre and the Swiss Finance Institute at University Geneva for their comments. We are very grateful to Robert Schwob of Style Research Limited for provision of data. All remaining errors are our sole responsibility. Authors are listed alphabetically. No author benefitted financially or otherwise from the presented results.

2 I. INTRODUCTION Ivory tower - [a] state of privileged seclusion or separation from the facts and practicalities of the real world (Oxford Dictionary of English) In their pioneering work, Hong and Kacperczyk (2009) [HK from now on] analysed the performance of so-called sin stocks: publicly traded companies involved in producing alcohol, tobacco and gaming. Over the period of 1965 to 2006, the authors found a portfolio of US sin stocks to yield a statistically significant four-factor alpha of 26 basis points per month. These sin stocks were reported to have relatively fewer institutional shareholders, and sell-side analyst coverage compared to the rest of the market. Drawing on theories related to neglected stocks and segmented markets (Arbel, Carvell, & Strebel, 1983; Heinkel, Kraus, & Zechner, 2001; Merton, 1987), HK argue that this outperformance can be explained by institutional investors desertion of sin stocks. Specifically, many institutional investors (e.g., managers of employee ownership funds, pension funds, university endowments) adhere to policies that restrict them from investing in unethical stocks. This creates limited risk sharing for those who choose to invest, whereby higher expected returns are required. If genuine, the sin stock premium recorded by HK has important implications for the theory and practice of the rapidly growing field socially responsible investing. That is, by adhering ethical and social values, investors may not only pay a financial cost due to less favourable risk/return trade-offs due to a reduction of the investable universe (Luc Renneboog et al. 2008b), but also due to the exclusion of sin stocks. Consequently, the work of HK has gained considerable attention in the socially responsible investing literature (Bénabou & Tirole, 2010; Derwall, Koedijk, & Ter 2

3 Horst, 2011; Edmans, 2011; Galema, Plantinga, & Scholtens, 2008; Kempf & Osthoff, 2007; Renneboog, Ter Horst, & Zhang, 2008a, 2008b) Despite increased academic attention (Fabozzi, Ma, & Oliphant, 2008; Fauver & McDonald, 2014; Liston & Soydemir, 2010; Lobe & Walkshäusl, 2011; Salaber, 2013), the work of HK remains by far the most influential paper on sin stocks performance 1 and has been discussed in the world s most famous media outlets including CNN, Financial Times and Wall Street Journal. However, when closely examining their research design it appears to be subject to several weaknesses. Firstly, while HK find evidence of significant effects of social norms on markets (p. 34), it should be noted that their findings primarily stem from a sample of US sin stocks. Although they provide limited empirical evidence from an international sample of sin stocks from seven markets (i.e., Canada, France, Germany, Italy, Netherlands, Spain, Switzerland, United Kingdom), these countries can be considered culturally similar the US 2. The assumption that social norms (and hence investors view of alcohol, gambling and tobacco stocks) are constant across markets appears somewhat naive. In fact, recent research show that the extent to which institutional investors shun alcohol, gambling and tobacco stocks vary significantly across markets (Fauver & McDonald, 2014; Liu, Lu, & Veenstra, 2014). If social norms are priced, then crosscultural differences in sin stocks performance should be present. Recent international evidence shed light on this issue. For instance, in direct contrast to HK, Durand, Koh, and Tan (2012) find alcohol, gambling and tobacco stocks to significantly underperform in seven Pacific-Basin markets. Similarly, Salaber (2013) finds alcohol and tobacco stocks to outperform in Protestant countries but not in Catholic countries. 1 For instance, in November 2014 HK had nearly ten times as many citations as the second most cited paper on sin stocks performance (Fabozzi et al. 2008) according to Google Scholar. For further media mentions, please see Kacperczyk s CV. 2 Furthermore, the robustness test includes only a limited number of control variables compared to the main analysis, and with a significantly shorter time period. 3

4 Secondly, certain characteristics of HK s data sample and research design cast doubt on the practical relevance of their results. For instance, the absence of sub-sample analysis between 1965 and 2006 implies that the recorded outperformance could have stemmed from a long time ago. Furthermore, HK s data sample appears to be hypothetical rather than investable. Specifically, the authors use NYSE, Amex and Nasdaq sin stocks, without considering common investable criteria used by institutional investors, such as market capitalization, liquidity or trading volume. Hence, whether the recorded sin stocks premium is present on an institutional investor level remains unclear 3. Finally, when analysing the risk-adjusted performance, HK regress an equalweighted portfolio of sin stocks on a value-weighted market benchmark 4. This implies that the outperformance could be driven by a small cap performance bias rather than sin stocks characteristics (Hoepner & Zeume, 2013). This argument is founded on the empirical observation that small stocks outperform large stocks (Fama & French, 1993). The exceptionally good performance could hence be due to an over-weighting of small cap stocks and underweighting of large cap stocks. In line with this argument, both Statman and Glushkov (2009) as well as Lobe and Walkshäusl (2011) find valueweighted sin portfolios to perform statistically insignificant from zero. Furthermore, in their multifactor regression framework, the risk factors (i.e., size, value, momentum) are constructed on an economy level rather than industry level. Hence, it is assumed that the risk characteristics do not vary across sectors. Fama-French s model controls for return differentials between small cap and large cap firms within all industrial sectors. Their 3 To our knowledge not a single study has focused on sin stocks listed on a major index, nor has any other attempts been made to construct data sample aimed to mimic investable universes of institutional investors (see, for instance: Durand et al. 2012; Fabozzi et al. 2008; Fauver & McDonald 2014; Lobe & Walkshäusl 2011; Statman & Glushkov 2009). 4 It should be noted that several other sin stocks studies also use equal-weighted sin portfolios (e.g., Fabozzi et al. 2008; Liston & Soydemir 2010) 4

5 model is, however, not designed to capture return differentials between small cap and large cap firms in a single sector such as consumer goods, which could drive the returns of sub-industries in this sector such as alcohol and tobacco. Hence, we aim to control for this potential bias in our re-examination. Re-examinations of influential work are relatively common. One such example would be the influential work Goyal and Santa Clara (2003) [The Journal of Finance]. Based on compelling empirical evidence, the authors argue against established asset pricing theory in that idiosyncratic (unsystematic) risk in fact matters when explaining the cross-section of stock returns. Subsequent papers were then published with the sole purpose of proving weaknesses in their research design, and hence cast doubt on their conclusions. For instance, Bali, Cakici, Yan, and Zhang (2005) [also Journal of Finance] found Goyal & Santa Clara s result to be driven by a liquidity premium stemming from characteristics of their asset universe (i.e., CRSP Nasdaq/Amex/NYSE stocks). Furthermore, with regard to their use of equal-weighted rather value-weighted stock risk measures, the authors find that [Goyal & Santa Clara s] conclusions disappear when the more natural value-weighted measure of average stock risk [ ] is used in predictive regressions (p. 906). Moreover, with respect to Goyal & Santa Clara s sample length (1962 to 1999), Wei and Zhang (2005, p. 605) criticize their lack of sub-sample analyses, and find that their strategy does not have sustained gains after Interestingly, the weakness in the work of Goyal and Santa Clara (2003) are in many ways similar to the ones of HK. That is, both studies use the same asset universe (i.e., CRSP Nasdaq/Amex/NYSE), use equal-weighted rather than valueweighted portfolios, and analyse long periods of data without any subsample analyses. Our critique of HK combined with its significant influence serves as the principal motivation for this particular study. Specifically, we aim to address three 5

6 shortcomings: First, we study the performance of sin stocks listed on a major international index. Hence, we take into account the investability criteria common to institutional investors. Second, we examine whether potential research design related shortcomings commonly present in the literature is likely to influence the results. Primarily, this concerns the performance evaluation setting where equal-weighted sin portfolios are compared value-weighted market benchmarks (Fabozzi et al., 2008; Hong & Kacperczyk, 2009; Liston & Soydemir, 2010). Although a few studies have analysed value-weighted sin portfolios (Lobe & Walkshäusl, 2011; Statman & Glushkov, 2009), the potential issues of the former approach has not previously been acknowledged. Our findings suggest that the sin stocks outperformance recorded by HK appears to be driven by a small cap performance bias. That is, when comparing equal-weighted portfolios with value-weighted portfolios, the former result in much higher economic and statistical significance. Nevertheless, the alcohol and tobacco portfolio to outperform on a global level, while the gambling portfolio performs statistically insignificant from zero. However, when we control for industry level risk factors, the previously recorded outperformance disappears. This suggests that the outperformance could stem from industry characteristics rather than sin characteristics. We conduct the same analysis at the US level where the sin stock premium disappears even more convincingly. By addressing these research questions, the present study makes several contributions to the literature. First, we study the performance of sin stocks listed on a major international index. Hence, we take into account the investability criteria common to institutional investors, which makes our results practically relevant. Second, we examine whether potential research design related shortcomings commonly present in the literature is likely to influence the results. Primarily, this concerns the performance 6

7 evaluation setting where equal-weighted sin portfolios are compared value-weighted market benchmarks (Fabozzi et al. 2008; Hong & Kacperczyk 2009; Liston & Soydemir 2010). Although several studies have analysed value-weighted sin portfolios (Lobe & Walkshäusl 2011), the potential issues of the former approach have not previously been acknowledged. Third, we advance the understanding of the role of social norms as a determinant of sin stocks performance around the world. While previous work generally analyse the performance of sin stocks in a single country (Hong & Kacperczyk 2009; Liston & Soydemir 2010) more recent studies provide international evidence (Fabozzi et al. 2008; Lobe & Walkshäusl 2011). Drawing on the framework of Inglehart & Baker (2000), we add to the international evidence on sin stocks performance by a systematic consideration of cultural dimensions. Finally, in addition to alcohol, gambling and tobacco stocks, we also consider the risk and return characteristics of stocks that considered controversial or sinful by other investors. While previous studies have applied broader definitions of sin stocks (Fabozzi et al. 2008; Lobe & Walkshäusl 2011), we analyse for the first time all stocks that are being shunned by religious investors. For instance, biblical investors consider contraceptive products as highly controversial and Islamic investors have a hostile stance towards financial institutions dealing with interest. In doing so, we make an important contribution to the growing field of faith-based investing. Specifically, we provide empirical evidence of the cost or benefit of employing faith-based business screens. 7

8 II. Data A. Asset universe rationale The process of analysing portfolios of assets is frequently practised both in academia and the industry, although the aim of the two is somewhat different. Academic research generally concerns aspects related to asset pricing, while the principal focus of institutional investors is to detect opportunities related to superior risk-adjusted performance (Vaihekoski, 2004). When choosing which assets to include, one of the key concerns from an institutional investor perspective is the degree to which these assets are investable. Given the large amount of assets these investors have under management, entering or exiting a position is likely to impact the price substantially, hence leading to risks associated with liquidity. Large enough investments could also lead to the requirement of managerial input, or requirement of filing insider reports, which necessarily is not in the interest of the fund manager (Arbel et al., 1983) 5. In response to these requirements, major providers of market index generally incorporate investability screens that exclude firms that do not fulfil requirements related to size, liquidity, minimum foreign investor ownership and trading volume. Past research on sin stocks performance has not accounted for the aforementioned investability criteria imposed by institutional investors. US specific research generally include all stocks covered by The Centre for Research in Security (CRSP) (Hong & Kacperczyk, 2009; Liston & Soydemir, 2010) or KLD Research & Analytics (Statman & Glushkov, 2009) and international studies typically use the stock universe defined by Thomson Reuters Datastream (Durand et al., 2012; Fauver & McDonald, 2014; Lobe & Walkshäusl, 2011; Salaber, 2007). In a default setting, the above-mentioned data sources do not apply restrictions concerning investability. To 5 For instance, the U.S. Securities and Exchange Commission (SEC) require investors that own more than 5% of the voting power in a company to file a so-called beneficial ownership report, or Schedule 13D. 8

9 shed light on this current lack of understanding, we focus our analysis on sin stocks listed on a major international index: namely the FTSE All-World index. The motivation for the choice of asset universe is threefold. First, the index is constructed using several investability related firm size and liquidity, hence fulfilling the criteria of being investable. Second, constituted of nearly 3000 in 47 developed and emerging markets, the investment universe is robust to a diverse range of cultural and economic regions. The large number of firms also has a statistical upside, as it will allow us to construct portfolios that include double-digit firms. Third, the index is fully integrated with the Industry Classification Benchmark (ICB). As illustrated in the next section, using ICB sector codes allow for an easy identification of sin stocks. B. Portfolio construction To identify sin stocks listed on the FTSE All-World index we use the Industry Classification system (ICB) framework 6. ICB is a taxonomy style classification system where each firm is allocated a sector and subsector code. In the beginning of each year we include firms with the following ICB sector codes: alcohol: 3533 (brewers) and 3535 (distillers & vintners), gambling: 5752 (gambling), tobacco: 3785 (tobacco). Total return data (where dividends are re-invested) for these firms are then retrieved from Thomson Reuters Datastream for the period January 2002 to December As the sample includes data from multiple countries, we obtain all return data in USD and then transformed into continuously compounded returns. As discussed in detail by Hoepner and Zeume (2013), the nature of HK s research design imply that their results could be driven by a small cap performance bias rather than superior sin firms characteristics. Specifically, the authors regress an equal- 6 This approach has been used in several previous studies (e.g., Durand et al. 2012; Lobe & Walkshäusl 2011). 7 We have not been able to obtain the FTSE All-World index constituents list prior to

10 weighted sin portfolio on a value-weighted market benchmark. In this setting, the sin portfolio has, in contrast to the benchmark, an overweight of small stocks and underweight of large stocks, as measured by market capitalization. Drawing on the empirical observation that small stocks outperform large stocks (Fama & French, 1993), this should result in better performance. To examine whether the results of HK is subject to such a bias, we construct both equal-weighted and value-weighted portfolios for our set of alcohol, gambling and tobacco stocks. The latter is re-balanced at the end of each year. We expect to observe the equal-weighted portfolios to perform relatively better than their value-weighted counterparts. To avoid the risk of making the analysis subject to a potential survivorship bias (Brown, Goetzmann, Ibbotson, & Ross, 1992) firms that die are included in the sample 8. C. Descriptive statistics Descriptive statistics of the alcohol, tobacco, gambling and the aggregated sin portfolio are reported in Table 1. It can be observed that all equal-weighted portfolios generate superior monthly mean returns compared to their value-weighted counterparts. In most cases, this difference is substantial. For instance, the equal-weighted gambling portfolio has a monthly mean excess return of 125 basis points while for the value-weighted counterpart has a mean excess return of 74 basis points. Despite higher returns though, three out of the four equal-weighted portfolios have lower standard deviations, indicating superior risk-to-reward characteristics. It can further be observed that the mean excess return for the market benchmark (i.e., the value-weighted FTSE All-World Index) is significantly below all portfolios. While the standard deviation of the alcohol, 8 The authors show that the results empirical studies that exclude firms that die during the sample tend to be biased upwards. 10

11 tobacco and aggregated sin portfolio is similar to the overall market, the gambling portfolio appears to be somewhat more volatile 9. III. Re-examination: Research Method & Discussion of Results A. Single and multifactor regressions To determine the risk-adjusted performance of the alcohol, gambling and tobacco portfolios we perform both single factor as well multifactor time-series regressions, which controls for size, book-to-market and momentum investment style risk factors. To correct for possible autocorrelation and heteroscedasticity present in the data, all regressions are estimated using use the Newey and West (1987) methodology. We first estimate the Jensen s Alpha (1968) which is obtained from the Capital Asset Pricing Model (CAPM). The model assumes that the expected return of a portfolio can be determined by its riskiness compared to the overall market, whereby the model determines a portfolio's actual performance. Fama and French (1993) report that over time, stocks with low market capitalization tend to outperform stocks with high market capitalization, and stocks with high book-to-market ratios (value stocks) tend to outperform stocks with low book-tomarket ratios (growth stocks). Based on this insight they propose an alternative model to the CAPM, which in addition to the market risk also accounts for the risk associated with size and value. In addition to the market, size and value, Carhart (1997) propose an extended model which also controls for the risk associated with momentum stocks. Similar to size and value stocks, Carhart finds previous winning stocks to outperform previous losing stocks over time. These factors are constructed as follows. For the size 9 also reports the descriptive statistics for the investment style risk factors. The rationale for the will be discussed in a later section. 11

12 (SMB) and value factors (HML), we group all stocks into a small portfolio (S) and big portfolio (B) based on market capitalization, where S represent the lower half and B the upper half. Based on the book-to-price ratio, we then split the universe in three (30/40/30), resulting in three additional portfolios: high book value (H), medium book value (M) and low book value (L). Based on 2x3 portfolios the size and value factors are defined as: SMB: average return (S/H, S/M, S/L) minus the average return of (B/H, B/M, B/L). HML: average return (S/H, B/L) minus the average return of (S/L, BL) The momentum (MOM) factor is defined as the return difference between a portfolio of last year s winning stocks (70%-100%) and a portfolio of losing stocks (0%-30). The Jensen s alpha, three and four-factor models can be estimated with the following regression specifications: (1) (2) (3) where is the return of portfolio i at time t, is the risk-free rate (the investment yield of a one month US T-bill) for time t. represents the abnormal return for portfolio i, the exposure to systematic risk and is the return of the market for time t. is the difference in return between stocks with small stocks and big stocks for time t, and for time t. is the difference in return between value stocks and growth stocks is the difference in return between previous winning stocks and previous loosing stocks. Finally, is the random error term. The results of the single and multifactor regressions (displayed in Table 2) report several interesting observations. First, it appears that the portfolio construction 12

13 methodology is an important determinant of financial performance. Specifically, when comparing the results of the equal-weighted portfolios (Panel A) with the valueweighted portfolios (Panel B) it can be observed that the former generate much higher economic and statistical significance. For instance, in a four-factor regression setting, alcohol and tobacco experience a decrease in alpha of 15 and 35 basis points per month, respectively. These results suggest that the sin stocks outperformance recorded by HK to some extent could be driven by their research design rather than characteristics of the sin firms. Supporting this notion is the fact that for the equal-weighted portfolios, all SMB coefficient factor loadings are positive and significant, indicating that a small cap tilt has driven the performance. However, since we do not entirely replicate HK s research design we cannot confirm that this is the case 10. Second, it can be observed that the gambling does not perform statistically different from zero in a value-weighted portfolio setting, and it has a much higher beta value compared to the alcohol and tobacco portfolios. It should further be noted that we acknowledge the low beta (i.e., 0.6) and adjusted r-squared value (i.e., 0.34) of the tobacco portfolio. To ensure this is not related to research design mistake on our part, we review other studies and find similar results 11. Arguably, the low adjusted r-squared value could be due to the small number of firms constituting the portfolio. B. Impact of industry level risk characteristics Following the bulk of the sin stocks performance literature, in section 3.1 we analyse the performance of sin portfolios using multifactor regression models that control for 10 Our analysis is different to HK in several aspects. First, we base our analysis on an investable international sample of sin stocks rather than US specific stocks. Second, while Hong & Kacperczyk analyse the performance of an aggregated portfolio of alcohol, gambling and tobacco stocks, we construct industry specific portfolios 11 For instance, Hong & Kacperczyk (2009) report a tobacco portfolio beta of 0.63 and Lobe & Walkshäusl (2011) a tobacco beta of 0.59 and adjusted r-square of

14 size, value (book-to-market) and momentum investment styles (e.g., Fabozzi et al. 2008; Fauver & McDonald 2014; Hong & Kacperczyk 2009; Liston & Soydemir 2010; Lobe & Walkshäusl 2011; Salaber 2013; Statman & Glushkov 2009; Visaltanachoti et al. 2009). In all aforementioned cases, the risk factors have been constructed based on the economic level universe of stocks 12. That is, the style portfolios are constituted of stocks across all industries. However, when analysing the performance of an industryspecific portfolio of stocks (e.g., alcohol, gambling, tobacco) using economic level risk factors, it is explicitly assumed that firm characteristics are homogeneous across markets. This is unlikely to be true as various sectors of the economy may receive different productivity shocks that will in turn result in different returns on capital for the firms of those sectors (Li et al. 2006, p.1638). Ultimately, this should lead to crossindustry differences in the composition of size, book-to-market and momentum portfolios (Hanhardt & Ansotegui, 2008). For the above reasons, we are interested to investigate the performance of our sin portfolios after controlling for unique industry investment styles. For this reasons, we construct additional risk factors as follows. Using Style Research, industry specific portfolios are created based on the ICB industry category above the respective sin portfolio. Hence, for alcohol and tobacco we use 3000 (consumer goods) and for gambling we use 5000 (consumer services). The industry specific size, value and momentum factors are constructed following the procedure of Fama & French (1993) and Carhart (1997). Third, as we are only interested to capture the unique industry characteristics and to avoid multicollinearity, we must then clean each industry level factor from its broader, economy level factor. To do this, we perform an 12 Previous research typically obtain these factors from the Kenneth French website ( (Hong & Kacperczyk 2009; Lobe & Walkshäusl 2011) or constructed in a similar setting: Following Fama and French (1993), the SMB and HML portfolios include all stocks for which we have data (Salaber 2013, p.158). 14

15 orthogonalization process where the industry level style factor is regressed on the corresponding economy level factor. We then construct a new factor by adding the intercept to residuals of the regression. Having created industry level risk factors, we then arrive at the following five-factor, and seven-factor regression model, respectively: (4) (5) where the subscripts eco and ind indicate the economy and industry factor level, respectively. Since alcohol and tobacco stocks belong to consumer goods and gambling belongs to consumer services, the above regression estimations are not applicable for the aggregated sin portfolio. We therefore perform one more orthogonalization process, this time with the two industry factors consumer services and consumer goods. Hence, we arrive at the following seven-factor and ten-factor regression model, respectively: (6) (7) where the subscripts cg and cs indicate the consumer goods and consumer services factor, respectively. The results of the five-factor and seven-factor regression are displayed in Table 3. When controlling for the industry specific size, book-to- 15

16 market and momentum risk factors, we can observe a drastic change in the performance of the sin portfolios. Starting with the equal-weighted portfolios (Panel A), there is a decrease in the statistical significance in all estimations (compared to the estimations in Table 2). After controlling for momentum, alcohol and gambling no longer perform statistically different from zero and the tobacco portfolio experience a decrease in alpha from 11 to 6 basis points per month. In Panel B it can further be observed that the previously reported outperformance of the value-weighted portfolios is gone. This implies that the previously recorded outperform of alcohol and tobacco stocks could be driven the characteristics of the broader industry they operate within, rather than firmlevel characteristics. Compared to three and four-factor estimations, both the five-factor and seven-factor regressions yield significantly higher adjusted r-square values (e.g., tobacco rises from 0.34 to 0.43). Hence, it appears that accounting for industry-specific risk factors better explain the performance of the sin portfolios. C. US level portfolios Given that HK concentrate their study predominantly on US stocks, we would not conduct a fair re-examination if our analysis remained entirely global. Hence, we reestimate all our models using the equivalent US data and display the results in Table 4 and Table 5, respectively. In line with the results documented by HK, the equalweighted aggregated sin portfolio significantly outperforms the market with 60 basis points per month (significant at a 5% level). Similar performance is true for the equalweighted alcohol and tobacco portfolios while gambling does not perform statistically significant from zero. Looking at the results for the value-weighted portfolios, however, gives a completely different perspective of performance. Specifically, the alcohol, tobacco and aggregated sin portfolio do not longer exhibit any significant 16

17 outperformance, and the gambling portfolio now underperforms with 42 basis points in a four-factor setting. Controlling for within industry risk factor exposure leads to negative alpha coefficients for the aggregated sin portfolio. Hence, when we conduct our global analysis in HK s purely US universe, the sin stock premiums disappear even more convincingly. This suggests that the sin stock premium and the price of sin aversion recorded by HK are artefacts of an ivory tower research design and, hence, arguably not applicable to the real world. IV. Sensitivity analysis: cross-cultural difference in sin stocks performance Motivated by cross-national differences in culture and social norms, recent work has come to focus on the link between variations in social norms and the effect on sin stocks performance. For instance, Durand et al., (2012) incorporate data on individualism (Hofstede 1980) and corruption perception to explore cross-national differences in sin stocks performance in seven Pacific Basin markets (i.e., Australia, India, Japan, South Korea, Malaysia, New Zealand and Singapore). Their findings reveal that institutional investor shareholdings in sin stocks are substantially smaller in markets culturally closer to the US (i.e., Australia, New Zealand), while the opposite is true in markets culturally diverse for the US (i.e., Japan, South Korea). In a G20 country context, Fauver & McDonald (2014) utilize World Value Survey (WVS) data to examine how countrylevel perception of sin stocks affect equity valuations of such stocks. The authors find that sin stocks have lower equity valuation in markets where they are perceived more controversial, and vice versa. Liu et al., (2014) employ consumption data related to alcohol, gambling and tobacco as proxy for social norm acceptance level of these stocks. They find that the institutional investor shareholdings in sin stocks are lower in societies that have more 17

18 strict views of sin stocks. Salaber (2013) finds the expected return of alcohol and tobacco stocks to be higher in Protestant countries compared to Catholic countries. Kumar et al., (2014) show that investors in countries with higher Protestant to Catholic ratios are less risk averse, as they are more likely to invest in lottery-type stocks and participate in initial public offerings. To investigate how our sample of alcohol, gambling and tobacco stocks perform in different cultural settings, we construct a set of cultural sub-portfolios following the Inglehart-Weizel Cultural Map of the World (see Figure 1). Inglehart & Weizel argue that that many fundamental values embedded in societies are closely correlated, whereby countries can be organized according to cultural values rather than geographical distances. When examining the results of the survey, the authors find that over 70% of the cross-national variance can be explained in a two-dimensional framework: Traditional/ Secular-rational and Survival/Self-expression. Forming portfolios based on the cultural map allow us to group stocks from several countries that share similar cultural characteristics. This has a clear statistical advantage as we can form portfolios double-digit firms. Based on firm domicile (obtained from the FTSE All-World constituent list) we group our sample of sin stocks according to the respective cultural region. In some of these regions, the number of sin stocks is few. For statistical reasons portfolios we therefore exclude portfolios with less than five firms, leaving us with sin stocks in five cultural areas: Catholic Europe (gambling), Confucian (alcohol, gambling), English (alcohol, gambling, tobacco), Latin America (alcohol) and Protestant Europe (alcohol). The results of the cultural sin portfolio regressions are reported in Table 6. It can be observed that the majority of the portfolios perform statistically insignificant from zero. This implies that sin stocks in many cultures do not perform differently from the 18

19 market. Interestingly, alcohol stocks appear to outperform in a Latin America context while underperform in Confucian context (in a four-factor regression setting). As illustrated in Table 6, societies in the Confucian region is more oriented towards secular-rational values while Latin American societies are more oriented towards traditional values. The latter confirms the findings of Durand et al., (2012) who find alcohol stocks to underperform in Japan and South Korea. Due to few number of tobacco stocks, we were only able to construct a tobacco portfolio for the English region. V. Robustness test: performance of other sin/unethical stocks The logic presented in Section IV, that social norms are not homogenous across markets, also implies that investors view of sin may vary. In fact, the literature does not offer a clear definition of sin stocks. While most studies follow the definition of Hong & Kacperyzyk (2009, p.16): publicly traded companies involved in producing alcohol, tobacco and gaming (e.g., Durand et al. 2012; Fauver & McDonald 2014; Liston & Soydemir 2010; Visaltanachoti et al. 2009), others use somewhat broader definitions. For instance, Fabozzi et al., (2008) include biotechnology stocks and Lobe & Walkshäusl (2011) focus on stocks shunned by socially responsible indices, leading to the inclusion of adult entertainment, defence and nuclear stocks. In this study, we aim to broaden the sample of stocks further. Specifically, we also include stocks that are perceived controversial or sinful by the growing segment of faith-based investors. While not perceived controversial by the general public, some products and services are considered sin in various religions, such as the concept of charging and paying interest, pork-related products (Islam), and abortion and contraceptives (Christianity). Inspired by the approach of Lobe & Walkshäusl (2011), 19

20 we, therefore, include in our sample stocks that are being shunned by faith-based investors. Specifically, we focus on stocks that are excluded from biblical and Islamic, and socially responsible investing indices. The advantage of this approach is threefold. First, when deciding which stocks to neglect, faith-based funds and indices consult experts in the respective religion. Hence, we avoid the challenging task of deciding whether or not a stock should be classified as controversial/sinful. Second, the approach will yield an important contribution to rapidly growing industry of faith-based investing. To the best of our knowledge, not a single study has specifically studied the performance of stocks neglected by these investors. We construct the additional set of portfolios as follows. First, we review the fact sheets of all major Biblical (Christian), Islamic and SRI indices and create an aggregated listed of the industries shunned by these indices. In total we identify 13 screens utilized by these indices (in addition to alcohol, gambling and tobacco): adult entertainment, biotechnology, defence/weapons, nuclear energy (SRI screens), abortion, contraceptives (biblical screens), broadcasting & entertainment, financials, hotels, media agencies, pork producers, publishing and restaurants & bars (Islamic screens) 13. To sort our list of constituents into these portfolios, we first review the list of sector codes available within the ICB framework. Here, we can identify sector codes for eight out of the 13 industries, namely: biotech (4573), broadcasting & entertainment (5553), defence (2717), financials (8000), hotels (5753), media agencies (5555), publishing (5557) and restaurants & bars (5757). Second, to identify firms associated with abortion, adult entertainment and contraceptives, we employ data provided by the Ethical Investment Research Service (EIRIS). The database includes data related to environmental, social and governance (ESG) factors for 3500 companies worldwide, 13 Some Islamic indices also exclude stocks associated with trading of gold & silver. However, we do not consider this category as we do not find a reliable method to identify such firms. 20

21 and the database has been used in a number of related studies (e.g., Bauer et al. 2005; Kempf & Osthoff 2007) 14. We select those firms that state that they are involved with any of the products or services above 15. Third, to identify firms associated with pork production and nuclear power, we use the S&P Capital IQ platform. Specifically, we search for keywords in company business descriptions that can be associated with any the above activities 16. It should be noted that this method comes with one limitation. Specifically, the data only specifies whether a firm has any association with a given product or service, and not specify the amount of profit generated from it. The method can, however, be justified by the fact that some investors prohibit any involvement (Derigs & Marzban 2008). Finally, having identified all constituents in our sample using the above three methods, we then construct value-weighted portfolios, following the same procedure as in section B. The results of the single, three, and four-factor regressions are displayed in Table 7. We observe that the majority of the portfolios do not exhibit risk-adjusted returns that are statistically distinguishable from the market benchmark. These include adult entertainment, defence, nuclear energy, abortion, broadcasting & entertainment, media agencies and pork producers. In contrast, the results indicate that firms associated with biotech, contraceptives, hotels and restaurants & bars significantly outperform while financials, hotels and publishing underperform. The underperformance of the financials portfolio could have significant (positive) implications for Islamic investors, 14 We do only have access do the EIRIS database prior to 2004 and after Hence, for the first two and the final two years of the sample, we rely on the 2004 and 2011 data, respectively. 15 We classify a stock as sin if the answer to any of these questions is yes. Abortion: (i) Does the Company offer healthcare services or information which may relate to abortion? (ii) Does the Company produce abortifacients?. Adult entertainment: (i) Does the Company provide adult entertainment services (other than through mobile telecommunications)? (ii) Does the Company provide adult entertainment services via a mobile telecommunications network? (iii) Does the Company provide adult entertainment services via a mobile telecommunications network? Contraceptives: Does the Company produce contraceptives? 16 This approach was first used by Lobe and Walkshäusl (2011). 21

22 given the size of the financial sector. Finally, it should be noted that the adjusted r- squared values vary between the portfolios (from 0.22 to 0.94), suggesting variations in the degree of explanatory power. VI. Summary and Conclusions The principal objective of this study was to re-examine HK s results on the performance on sin stocks (i.e., publicly traded companies involved in the production of alcohol, tobacco and gaming). HK find an equal-weighted portfolio of US sin stocks to outperform a value-weighted market benchmark. Drawing on theories of neglected stocks and segmented markets, the authors suggest this outperformance to be due to a shunning effect caused by norm-constrained institutional investors who effectively pay a price for sin aversion. In this study it was postulated that HK s results are driven by research design related issues rather than sin stocks characteristics. The main conclusion of this study is that the sin stock premium recorded by HK appears to be driven by a small cap performance bias. Fundamentally, an equalweighted portfolio will overinvest in (outperforming) small cap stocks and underinvest in (underperforming) large cap stocks. When analysing sin stocks in a value-weighted portfolio setting, we witness a significant decrease in financial performance. Furthermore, HK s use of economic level risk factors seems unsuitable when analysing stocks in individual sectors. Specifically, Fama & French s (1993) model controls for return differentials between small cap and large cap firms within all industrial sectors. The model is not designed to capture return differentials between small cap and large cap firms in a single sector, such as consumer goods. Effectively, this could drive the returns of sub-industries in this sector such as alcohol and tobacco. 22

23 Adding within sector control variables leads to a complete disappearance of any premium to sin stocks at the global level. Based on the results of the sensitivity analysis we find that sin stocks perform similar to the market in most cultural settings. This is, however, not true for alcohol stocks. Furthermore, we document mixed results for the performance of other stocks considered sinful and unethical by socially responsible and faith-based investors. This study is subject to a number of limitations. First, the focus in the empirical analysis has be solely on financial performance, and the issue of institutional shareholdings in sin stocks has not been considered. Second, the results of the analysis are based on the performance of hypothetical portfolios. Although efforts have been made to make the results practically relevant (i.e., exclusion of illiquid stocks), the research design can only highlight investment opportunities associated with these stocks. Third, different methods have been used to identify the additional sample of stocks used in the robustness test. We acknowledge that this approach is not ideal and may result in inconsistences. An interesting path for future research would be to join recent work (Durand et al. 2012; Fauver & McDonald 2014; Liu et al. 2014) in trying to advance the understanding the of how culture and social norms impact sin stocks performance around the world. 23

24 VII. Bibliography Arbel, A., Carvell, S., & Strebel, P. (1983). Giraffes, institutions and neglected firms. Financial Analysts Journal, 39(3), Bali, T., Cakici, N., Yan, X., & Zhang, Z. (2005). Does idiosyncratic risk really matter? The Journal of Finance, Bénabou, R., & Tirole, J. (2010). Individual and corporate social responsibility. Economica, 77(305), Brown, S. J., Goetzmann, W., Ibbotson, R. G., & Ross, S. A. (1992). Survivorship bias in performance studies. Review of Financial Studies, 5, Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52, Derwall, J., Koedijk, K., & Ter Horst, J. (2011). A tale of values-driven and profitseeking social investors. Journal of Banking & Finance, 35, Durand, R. B., Koh, S., & Tan, P. L. (2012). The price of sin in the Pacific-Basin. Pacific-Basin Finance Journal. Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction and equity prices. Journal of Financial Economics, 103(1), Fabozzi, F. J., Ma, K. C., & Oliphant, B. J. (2008). Sin stock returns. Journal of Portfolio Management, Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, Fauver, L., & McDonald, M. B. (2014). International variation in sin stocks and its effects on equity valuation. Journal of Corporate Finance, 25, doi: /j.jcorpfin Galema, R., Plantinga, A., & Scholtens, B. (2008). The stocks at stake: Return and risk in socially responsible investment. Journal of Banking & Finance, 32(12), Goyal, A., & Santa Clara, P. (2003). Idiosyncratic risk matters! The Journal of Finance, 58(3), Hanhardt, A., & Ansotegui, C. (2008). Do the Fama and French factors proxy for state variables that predict macroeconomic growth in the eurozone. Available at SSRN Heinkel, R., Kraus, A., & Zechner, J. (2001). The effect of green investment on corporate behavior. the journal of financial and quantitative analysis, 36, Hoepner, A. G. F., & Zeume, S. (2013). Fiduciary Duty and Sin Stocks: Is Vice Really Nice? Handbook of Institutional Investment and Fiduciary Duty, Forthcoming. Hong, H., & Kacperczyk, M. (2009). The price of sin: The effects of social norms on markets. Journal of Financial Economics, 93, Jensen, M. C. (1968). The performance of mutual funds in the period The Journal of Finance, 23, Kempf, A., & Osthoff, P. (2007). The Effect of Socially Responsible Investing on Portfolio Performance. European Financial Management, 13,

25 Liston, D. P., & Soydemir, G. (2010). Faith-based and sin portfolios: An empirical inquiry into norm-neglect vs norm-conforming investor behavior. Managerial Finance, 36, Liu, Y., Lu, H., & Veenstra, K. (2014). Is sin always a sin? The interaction effect of social norms and financial incentives on market participants behavior. Accounting, Organizations and Society, 39, Lobe, S., & Walkshäusl, C. (2011). Vice vs. Virtue Investing Around the World. Virtue Investing Around the World (May 9, 2011). Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The Journal of Finance, 42, Newey, W. K., & West, K. D. (1987). Hypothesis testing with efficient method of moments estimation. International Economic Review, 28, Renneboog, L., Ter Horst, J., & Zhang, C. (2008a). The price of ethics and stakeholder governance: The performance of socially responsible mutual funds. Journal of Corporate Finance, 14, Renneboog, L., Ter Horst, J., & Zhang, C. (2008b). Socially responsible investments: Institutional aspects, performance, and investor behavior. Journal of Banking and Finance, 32, Salaber, J. (2007). The determinants of sin stock returns: Evidence on the European market. Paris December 2007 Finance International Meeting AFFI- EUROFIDAI Paper. Salaber, J. (2013). Religion and returns in Europe. European Journal of Political Economy, 32, The Wages of Social Responsibility, (2009). Vaihekoski, M. (2004). Portfolio construction for tests of asset pricing models. Financial Markets, Institutions & Instruments. Wei, S., & Zhang, C. (2005). Idiosyncratic risk does not matter: A re-examination of the relationship between average returns and average volatilities. Journal of Banking & Finance. 25

26 Table 1 Descriptive statistics Portfolio Type Mean Max Min S.D Alcohol Equal-weighted Value-weighted Gambling Equal-weighted Value-weighted Tobacco Equal-weighted Value-weighted Sin Equal-weighted Value-weighted Benchmarks Market benchmark SMBeco SMBcg SMBcs HMLeco HMLcg HMLcs MOMeco MOMcg MOMcs The following table reports the descriptive statistics of the dependent and independent variables used in the empirical analysis. Column one displays the variable category and column two the variable type. Column three to six show the mean, maximum, minimum and standard deviation of the excess returns, respectively. 26

27 Table 2 Standard single and multifactor regression results of global sin portfolios Panel A: Global Equal-Weighted Portfolios Panel B: Global Value-Weighted Portfolios p p smbp hmlp momp Adj r2 p p smbp hmlp momp Adj r2 Alcohol *** *** *** *** *** *** ** *** *** *** *** ** * ** *** ** Tobacco *** *** *** *** *** *** ** *** ** *** *** *** *** *** ** *** Gambling *** *** *** ** *** *** *** *** *** *** ** *** *** *** *** *** *** SIN *** *** *** *** *** *** *** *** ** *** *** ** *** *** *** *** ** *** *** ** The following table reports the results of the Jensen s alpha (1968), Fama & French (1993) and Carhart (1997) regressions of the equal-weighted (panel A) and value-weighted (panel B) alcohol, gambling and tobacco portfolio, respectively. Column one displays the respective sin portfolio, column two the portfolio alpha and column three the portfolio exposure to market risk (beta). Column four to six report the smb, hml and mom factors, respectively. Finally, column seven reports the adjusted r-square. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 27

28 Table 3 Global Value-weighted portfolios controlled for within industry size, value vs growth and momentum vs contrarian effects p p smbeco smbcg smbcs hmleco hmlcg hmlcs momeco momcg momcs Adj r2 Alcohol *** * *** *** ** *** Tobacco *** ** ** ** *** *** * * ** *** Gambling *** * *** * * SIN *** * *** *** * *** * The following table reports the results of the three, five, seven, eight and ten-factor regressions of the global value-weighted alcohol, tobacco, gambling and aggregated sin portfolio, respectively. Column one displays the respective sin portfolio, column two the portfolio alpha and column three the portfolio exposure to market risk (beta). Column four to six report smb factor on the economy level (eco), consumer goods (cg) and consumer services (cs) level, respectively. Column seven to twelve report corresponding values for the hml and mom factors, respectively. Finally, column thirteen reports the adjusted r-square. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 28

29 Table 4 Standard single and multifactor regression results of US sin portfolios Panel A: US Equal-Weighted Portfolios Panel B: US Value-Weighted Portfolios p p smbp hmlp momp Adj r2 αp βp smbp hmlp momp Adj r2 Alcohol ** *** *** ** *** *** ** *** ** *** Tobacco ** *** *** *** *** *** *** ** *** *** ** *** ** Gambling *** * *** *** *** ** *** * *** ** * *** * *** SIN ** *** *** * *** *** *** ** ** *** *** *** ** The following table reports the results of the Jensen s alpha (1968), Fama & French (1993) and Carhart (1997) regressions of the equal-weighted (panel A) and value weighted (panel B) alcohol, gambling and tobacco portfolio, respectively. Column one displays the respective sin portfolio, column two the portfolio alpha and column three the portfolio exposure to market risk (beta). Column four to six report the smb, hml and mom factors, respectively. Finally, column seven reports the adjusted r-square. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 29

30 Table 5 US Value-weighted portfolios controlled for within industry size, value vs growth and momentum vs contrarian effects p p smbeco smbcg smbcs hmleco hmlcg hmlcs momeco momcg momcs Adj r2 Alcohol *** *** *** ** Tobacco *** ** * *** ** Gambling ** *** * *** SIN *** * *** * The following table reports the results of the three, five, seven, eight and ten-factor regressions of the global value-weighted alcohol, tobacco, gambling and aggregated sin portfolio, respectively. Column one displays the respective sin portfolio, column two the portfolio alpha and column three the portfolio exposure to market risk (beta). Column four to six report smb factor on the economy level (eco), consumer goods (cg) and consumer services (cs) level, respectively. Column seven to twelve report corresponding values for the hml and mom factors, respectively. Finally, column thirteen reports the adjusted r-square. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 30

31 Table 6 Single and multifactor regression results of value-weighted cultural sin portfolios Industry WVS region p p smbp hmlp momp Adj r2 Alcohol Confucian *** *** * *** * *** * *** English *** *** *** * Latin America *** *** *** *** *** *** ** Protestant Europe *** *** *** *** *** Tobacco English ** *** ** *** ** *** * * 0.23 Gambling Catholic Europe *** *** * *** * 0.78 Confucian * *** *** *** ** 0.08 English *** *** *** ** *** *** The following table reports the results of the Jensen s alpha (1968), Fama & French (1993) and Carhart (1997) regressions of the equal-weighted (panel A) and value-weighted (panel B) alcohol, gambling and tobacco portfolio, respectively. Column one displays the respective sin portfolio, column two the portfolio alpha and column three the portfolio exposure to market risk (beta). Column four to six report the smb, hml and mom factors, respectively. Finally, column seven reports the adjusted r-square. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 31

32 Table 7 Performance of other sinful and unethical industries Category Industry p p smbp hmlp momp Adj r2 SRI screens Adult entertainment *** *** *** *** *** *** *** Biotech *** ** *** ** ** *** ** Defence/weapons *** *** *** Nuclear energy *** *** * *** * Biblical screens Abortion *** *** *** Contraceptives *** * *** *** ** *** *** ** Islamic screens Broadcasting & Entertainment *** *** ** *** *** *** *** ** 0.69 Financials ** *** ** *** * *** ** *** 0.94 Hotels *** *** ** * ** *** ** * ** 0.72 Media Agencies *** *** *** *** 0.60 Pork producers *** *** * ** *** * ** * 0.25 Publishing *** * *** *** ** *** *** Restaurants & Bars ** *** ** *** * *** The following table reports the results of the Carhart (1997) estimations. Column one displays the screening category and column two the respective industry. Column three to seven shows the Carhart alpha, beta, smb, hml and mom factors.. Coefficient covariances and standard errors are made heteroscedasticity and autocorrelation are based on (Newey & West 1987). *, **, *** represent a 10%, 5%, and 1% significant level, respectively. 32

33 Figure 1 Ingelhart-Welzel cultural map of the world 33