The value premium within and across GICS industry sectors in a pre-financial collapse sample

Scislaw, Kenneth E. Article The value premium within and across GICS industry sectors in a pre-financial collapse sample Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Scislaw, Kenneth E. (2015) : The value premium within and across GICS industry sectors in a pre-financial collapse sample, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 3, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2015.1045214 This Version is available at: https://hdl.handle.net/10419/147758 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 FINANCIAL ECONOMICS | RESEARCH ARTICLE The value premium within and across GICS industry sectors in a pre-financial collapse sample Kenneth E. Scislaw 1 * Abstract:A portfolio manager employing a top-down/bottom-up method who seeks to capture the value premium long promised in academic literature would want to first determine whether the premium exists across industries and not just observed in firm-specific book-to-market (BE/ME) relationships. Next, the investor would want to know if BE/ME characteristics are stable across these defined homogeneous groups or whether there is considerable variation. Results show that certain industries appear to have a natural or structural tendency to reflect either a high or low BE/ME characteristic. Results also shows that growth-oriented industry BE/ME characteristics appear to be more stable than value-oriented industries over time. Moreover, stocks from growth-oriented industries tend to cluster at high rates in the lowest BE/ME quintile, while stocks from value-oriented industries appear more evenly distributed across middle BE/ME quintiles over time. Value stocks found in growth sectors outperform value stocks in value sectors, contrary to prior published results. The January premium exists both within and across Global Industry Classification Standard industry sectors, but the value premium is not subsumed by the January effect in either analysis. Subjects: Econometrics; Economic Theory & Philosophy; Investment & Securities Keywords: value premium; portfolio management; value stocks; GICS; industry groups 1. Introduction Value investment management techniques described many years ago by Graham, Dodd, and Cottle (1962) and employed over the years by such notable practitioners as Michael Price and Sir John Templeton are not homogeneous. Two methods are generally employed when constructing a value-oriented portfolio. First, a manager utilizing what is known as a bottom-up approach typically ignores macroeconomic and industry-specific data, and targets a value stock defined and preferred by that manager. The second method involves a combined top-down/bottom-up approach. The value manager first makes an active *Corresponding author: Kenneth E. Scislaw, Breech School of Business, Drury University, 900 North Benton Avenue, Springfield, MO 35801, USA E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, UK Additional information is available at the end of the article ABOUT THE AUTHOR Kenneth E. Scislaw’s resume reflects a 35-year involvement with retail, institutional, buy-side, sell-side, investment, and academic research segments of the finance profession. He is currently an assistant professor of Finance at Drury University. He has taught finance at several universities around the world including Drexel University (USA), University College Dublin (IRE), and the University of St Andrews (UK). He worked for almost 20years for major global investment firms including Merrill Lynch in New York and for the investment billionaire, Sir John Templeton. PUBLIC INTEREST STATEMENT A 25-year lineage of research exists that says stocks with high book-to-market accounting characteristics are “riskier” than those with low characteristics. Thus, investors should be able to capture this risk and improve their investment portfolio performance by purchasing stocks within industry groupings that exhibit such characteristics. This article suggests that the task is more complex, predictable in some instances and unpredictable in others. Received: 26 January 2015 Accepted: 27 March 2015 Published: 20 May 2015 © 2015 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 18 Page 2 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 industry or sector allocation from the top, and then actively fills those sector allocations from the bottom with stocks deemed to be appropriate to the value manager, for example, those with high book-tomarket (BE/ME) characteristics. A manager employing a top-down/bottom-up method who seeks to capture the value premium long promised in academic literature would want to first determine whether the premium exists across industries and not just observed in firm-specific BE/ME relationships. Next, the investor would want to know if BE/ME characteristics are stable across these defined homogeneous groups or whether there is considerable variation. If BE/ME observed across industry groups is stable and temporal variations small, then the value manager could strategically allocate funds away from industry groups that historically exhibit a weak premium, and then away from individual stocks found within those industry groups that exhibit low BE/ME characteristics. The resulting portfolio should allow a manager the best opportunity to capture the value premium promised originally in the work of Rosenberg, Reid, and Lanstein (1985), and later most notably in Fama and French (1992, 1993). Of course, the difficulty in assessing an industry impact on the BE/ME effect is made difficult, because BE/ME is by nature an accounting construction with considerable differences in meaning and interpretation across industry groupings. The first goal of this paper is to contribute to the body of literature evaluating within-industry and across-industry value premium characteristics using the Global Industry Classification Standard (GICS), a proprietary coding system jointly produced by Standard & Poor’s and Morgan Stanley Capital International. The choice to use GICS rather than other schemes to allocate stocks to a particular industry grouping is substantiated in the research of Bhojraj, Lee, and Oler (2003) who find GICS to be materially different (and better) than other classification systems. The second objective of this paper is to provide further information about BE/ME characteristics, both within and across industry sectors. Banko and Conover (2006) find the value effect related to both firm and industry risk characteristics—albeit the latter with less power to explain returns. However, if industry group BE/ME characteristics are not stable and predictable, then investors would have a difficult time strategically capturing the promised value premium when allocating funds ex ante across industry groups. Results presented here confirm observations by Banko and Conover (2006) that BE/ME characteristics vary considerably across industry groupings. However, the annual ordering of industry BE/ME appears to be relatively stable and potentially predictable for investors. Certain industries appear to have a natural or structural tendency to reflect either a high or low BE/ME characteristics. This paper also shows that growth-oriented industry BE/ME characteristics appear to be more stable than value-oriented industries over time. Moreover, stocks from growth-oriented industries tend to cluster at high rates in the lowest BE/ME quintile while stocks from value-oriented industries appear more evenly distributed across middle BE/ME quintiles over time. Banko and Conover (2006) observe that value stocks in (distressed) value industries perform better than value stocks in (less distressed) growth industries. Arguments by Banko and Conover (2006) should be robust to the use of a different industry classification system and robust to a different sample period. Results in this paper show that returns for the more recent sample period are materially different from those observed by Banko and Conover (2006). Value stocks found in growth sectors actually outperform value stocks in value sectors. However, during the observation period, growth sectors experience negative ROA, a reversal of what Banko and Conover (2006) observe in earlier sample periods. Therefore, results here are not inconsistent with arguments by Banko and Conover (2006) that the value premium results from investor risk pricing of distress. Next, this paper provides a check on the strength of the value premium within and across GICS industry sectors by controlling for the January anomaly. Curiously, the well-documented January effect possesses characteristics similar to the value effect. Loughran (1997) suggests the value effect is in fact partially driven by the January effect, among other factors. Conversely, Dhatt, Kim, and Mukherji (1999) observe that most of the value premium in small-cap stocks occurs outside the month of January. Results in this paper show that the January premium exists both within and across GICS industry Page 3 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 sectors, but the value premium is not subsumed by the January effect in either analysis. The strength of the value premium within sectors survives even after removing January returns, consistent with findings in Daniel and Titman (1997). Further, the average value premium computed across GICS industry sectors is virtually identical to the premium computed when January returns are omitted. Results do not suggest the value premium is stronger in the 11 months, February to December, as observed by Dhatt et al. (1999). Nor are results consistent with findings in Loughran (1997) that the value premium is boosted in part by January returns. 2. The Global Industry Classification Standard The decision to use the GICS in this paper rather than the North American Industry Classification System (NAICS), the Standardized Industry Classification System (SIC), or the Fama and French industry codes (FF) is motivated by Bhojraj et al. (2003) who find GICS to be a superior industry classification system. The authors find GICS to be superior at explaining co-movement in stock prices and cross-sectional variations in forecasted growth rates, financial ratios, and valuation metrics—issues critical to academic research findings.1 Additionally, Bhojraj et al. (2003) find that sorting stocks by GICS creates materially different industry samples than when sorting by the other three classification systems. NAICS samples map to SIC at a rate of 80% and FF map at 84% to SIC. GICS, however, map to SIC-defined samples at a rate of only 56% of the time. The authors find that NAICS, SIC, and FF “differ little from each other in most applications.” In other words, researchers who perform industry analyses utilizing GICS rather than the more common SIC and FF classification systems might experience results that are different from those in prior research. These differences, if any, could be very informative as to the outcomes observed in prior research. Another important reason to use GICS rather than FF codes is that any research attempting to reconcile academic research with market-based portfolios should use definitions and methods commonly employed by investors. Several important market-based financial products are now constructed based on GICS.2 3. Characteristics of the data sample and methodology Historical US stock returns and GICS industry codes are observed for all active and inactive US firms trading on the NYSE, AMEX, NASDAQ exchanges, and all other over-the-counter stocks (OTCBB, Pink Sheets, and “Other-OTC”) using the Compustat/Research Insight database. GICS history in Research Insight is unfortunately only available for this research beginning June 1999. The beginning of the sample period reflects approximately six months of the tail-end of the dotcom price exuberance, and then followed by a serious and lengthy return reversion by the same growth-oriented companies. The first half of the sample period includes a slowing of the US economy resulting from the dotcom collapse and the economic shock from the attacks on 11 September 2001. The second half of the sample period through May 2007 consists of a steady economic recovery and expansion. The sample is intentionally truncated to omit returns generated during the recent global financial collapse, which is arguably a very low probability tail-event period. Thus, statistical results and conclusions in this sample period may not be representative of conditions experienced during the period of the financial collapse due to the extraordinary nature of the economic period. Precise beginning and ending dates of the data sample are largely a function of portfolio construction techniques common to the value premium literature. In order to make inferential claims that can be linked to findings in prior research, the truncated sample period must reasonably reflect return and volatility characteristics observed in, for example, Fama and French (1993) and Davis, Fama, and French (2000). For robustness, the limited sample of NYSE, AMEX, NASDAQ, and all “other” OTC stocks are independently sorted 5×5 on size and then on BE/ME characteristics as in Fama and French (1993). Results (not shown) are not surprising. Valueoriented portfolios outperform growth portfolios across all size quintiles. Small size portfolios outperform large size portfolios across all BE/ME quintiles. Fama and French three-factor model coefficients for the 96-month return sample are similar to those for much longer periods. SMB and HML factor loadings for the 25 (5×5) portfolios are statistically significant and consistent with expectations. Large Page 4 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 stocks load negatively on the SMB factor while growth stocks load negatively on the HML factor. These results provide some comfort that statistical inferences presented in later sections are not simply a function of data mining. Industry and sector returns and characteristics in this paper are generally observed using equal weights as in Fama and French (1992) rather than value weights as used subsequently in Fama and French (1993) and many others. The choice of equal weights is driven by the desire for comparability to equal-weighted return observations in Banko and Conover (2006), a paper that asks similar questions to those here. While it is worth mentioning that Banko and Conover (2006) state their results are robust to the choice of equal or value weights it is well known that equal-weighted average monthly returns are generally higher and more volatile than those calculated using value weights.3 General industry characteristics for market equity (ME) and BE/ME using the GICS classification system are presented in Table 1. Table 1. Industry group BE/ME characteristics ordered by median BE/ME. June 1999 to May 2007, (n=96) BE/ME rank Industry group Sample size Median ME Mean BE/ ME Median BE/ME Std. Dev. BE/ME 1 Pharmaceuticals, biotechnology and life sciences 3520 324 149.84 0.38 0.25 0.08 2 Health care equipment and services 3510 458 106.31 0.94 0.41 0.10 3 Software and services 4510 587 88.72 0.49 0.42 0.21 4 Household and personal products 3030 54 53.07 0.87 0.44 0.13 5 Telecommunication services 5010 93 209.47 0.77 0.50 0.20 6 Technology hardware and equipment 4520 651 130.05 0.65 0.50 0.17 7 Media 2540 161 279.53 0.68 0.51 0.13 8 Energy 1010 274 239.99 0.96 0.52 0.15 9 Food, beverage, and tobacco 3020 134 159.95 0.86 0.56 0.08 10 Commercial services and supplies 2020 300 103.59 0.96 0.58 0.13 11 Utilities 5510 134 1,344.05 0.80 0.59 0.05 12 Capital goods 2010 430 150.54 1.06 0.62 0.15 13 Food and staples retailing 3010 44 436.43 0.86 0.62 0.13 14 Banks 4010 723 106.69 0.74 0.65 0.13 15 Materials 1510 264 206.88 1.28 0.65 0.16 16 Retailing 2550 280 238.71 1.38 0.66 0.26 17 Diversified financials 4020 165 190.73 1.39 0.67 0.21 18 Consumer services 2530 196 116.67 1.36 0.67 0.23 19 Transportation 2030 94 314.27 0.88 0.69 0.20 20 Real estate 4040 251 363.25 1.09 0.74 0.17 21 Automobiles and components 2510 75 177.28 0.97 0.74 0.23 22 Consumer durables and apparel 2520 288 99.77 1.50 0.79 0.23 23 Insurance 4030 143 538.78 1.02 0.83 0.12 Notes: The sample is collected from all active and inactive US firms trading on the NYSE, AMEX, NASDAQ exchanges, and all other over-the-counter stocks (OTCBB, Pink Sheets, and “Other-OTC”) sourced from the Research Insight database. Securities not representing the primary trading equity of the company are omitted. GICS history in Research Insight is available only from June 1999 to May 2007. Stock GICS are observed at May of year t except for the initial year 1999 when data history limitations require GICS for 1999 to be observed in June rather than in May. Prior to 2003, the GICS industry group code 4530 representing the semiconductors industry is included in code 4520. The two industry groups are re-combined for the purpose of this research because no data for code 4530 is available prior to 2003. BE/ME equity is observed in Research Insight at month end December t−1. Market equity (ME) is observed at May of year t. The traditional portfolio formation date in prior research occurs in July of year t capturing returns from that date through June of year t+1. However, in order to maximize the length of the historical GICS time series available, a portfolio formation date of June was used. Monthly total returns are observed June to May and accessed in Research Insight. Stocks with negative BE/ME, stocks without data reporting for ME, GICS, BE/ME, and stocks within the GICS unassigned industry group “0” data are removed from the sample. Stocks with a ME less than $1 million are removed to mitigate problems associated with non-synchronous trading, bid-ask noise and error pricing. Page 5 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Unsurprisingly, results in Table 1 show that biotechnology, health care, software, telecommunications, and technology industry groups—those typically found in growth-oriented mutual fund portfolios—are found in the growth end of the BE/ME ranking when ordered by median BE/ME across the sample period. It is again unsurprising that industries exhibiting high BE/ME characteristics shown in Table 1 are the same as those most found in the value-oriented Franklin Templeton Mutual Shares fund ($16 billion in net assets). At 31 December 2014, the fund held almost a quarter of its portfolio (23%) in financial stocks.4 Cohen and Polk (1996) suggest that an industry group or sector may exhibit consistently high BE/ ME characteristics over time. The authors argue that a persistently high BE/ME characteristic may result from a unique accounting standard or the industry may simply be a riskier industry than others. Conversely, an industry whose BE/ME characteristic migrates from low to high may simply be under temporary distress. Insurance, transportation, financial, and consumer durables are observed in the high end of the median BE/ME ordering in Table 1. However, variation in BE/ME characteristics, computed as the standard deviation of the observed eight-year time series and presented in the last column of Table 1, is large enough to warrant caution by value investors in allocating funds based on the historical median BE/ME of these industry groups. Table 2 shows the temporal consistency of the annual median BE/ME ranking of GICS industry groupings, similar to the presentation in Banko and Conover (2006) who use SIC sorted groupings. Although periodic ranking migration does occur, specifically the energy and telecommunications industries, the overall temporal consistency in BE/ME ranking is fairly high. Pharmaceuticals exhibit the lowest relative median BE/ME characteristic for all but one of the eight years in the sample period while the insurance industry exhibits the highest median BE/ME characteristic in five of the eight years of the sample. The Pearson correlation coefficient evaluating the degree of association between the annual BE/ME ranking and the aggregate median ranking over the entire sample period is greater than 0.66 for each of the eight years, and most of the annual coefficients are above 0.80. High positive correlations for the annual BE/ME rankings with the eight-year median for that industry are, of course, somewhat predictable given that the rankings are subsets of the aggregate data used to compute the median. However, the consistency of the resulting high correlations across time suggest that some predictability in observing industry ordering of BE/ME characteristics may be possible. Banko and Conover (2006) perform similar temporal consistency tests for 21 industries defined by SIC. The average range of BE/ME rank migration for each of their 21 industries is 14 places. This compares to an average annual range of BE/ME rank migration shown in the last column of Table 2 of only 11 places for the 23 industries defined by GICS (albeit for a shorter time period). The four lowest BE/ME ranked industries migrate on average only five places, suggesting that extreme growth-oriented industries exhibit some level of temporal BE/ME stability. Fama and French (1997) observe HML factor loadings for 48 industry groupings using SIC codes and find that loadings vary considerably across industries and vary considerably across time. The authors find the results “distressing” with negative implications for any precise computation of a company’s cost of equity capital. Cohen and Polk (1996) and Nelson (2006) both attempt with some success to resolve the three-factor model’s difficulty in explaining returns when stocks are sorted by industry. Results from Banko and Conover (2006) are consistent with those from Cohen and Polk that the value effect is indeed found across industry groupings but at a much lower level of power than at the firm level. For comparability, Table 3 shows equal-weighted monthly returns of GICS sorted industry groups regressed on the Fama–French three-factor model, June 1999 to May 2007. Results in Table 3 show that intercepts for equal-weighted excess industry returns are similarly problematic for the explanatory power of the three-factor model. Four of twenty-three intercepts, or 17%, are statistically different from zero. This compares to 21% of intercepts using SIC codes in Fama and French (1997).5 As in earlier research, individual industry factor loadings for market, SMB, and HML are almost all statistically significant. Thus, model results when employing GICS codes for the present sample period are not materially different Page 6 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Table 2. Annual ranking of GICS industry BE/ME characteristics. June 1999 to May 2007, (n=96) BE/ME rank Industry group 2000 2001 2002 2003 2004 2005 2006 2007 Median BE/ ME rank Minimum BE/ME rank Maximum BE/ME rank Range BE/ ME rank 1 Pharmaceuticals, biotechnology and life sciences 3520 1 2 1 1 1 1 1 1 1 1 2 1 2 Software and services 4510 2 1 10 3 6 3 2 4 3 1 10 9 3 Health care equipment and services 3510 8 6 3 2 3 4 4 2 4 2 8 6 4 Household and personal products 3030 5 7 7 4 2 2 3 5 5 2 7 5 5 Technology hardware and equipment 4520 11 4 4 5 20 5 7 10 6 4 20 16 6 Energy 1010 20 8 2 6 4 13 8 3 7 2 20 18 7 Telecommunication services 5010 3 3 6 11 21 9 6 17 8 3 21 18 8 Media 2540 4 5 9 8 9 6 14 15 9 4 15 11 9 Food, beverage, and tobacco 3020 6 11 8 9 5 14 16 12 10 5 16 11 10 Commercial services and supplies 2020 7 9 12 12 10 7 10 13 10 7 13 6 11 Utilities 5510 10 13 5 10 7 22 22 18 12 5 22 17 12 Capital goods 2010 15 16 11 13 14 12 11 8 13 8 16 8 13 Retailing 2550 12 10 22 14 18 10 9 7 11 7 22 15 14 Diversified financials 4020 18 14 19 18 13 8 12 11 14 8 19 11 15 Food and staples retailing 3010 9 12 13 7 15 19 21 16 14 7 21 14 16 Consumer services 2530 22 20 18 17 12 11 5 6 15 5 22 17 17 Banks 4010 13 17 14 15 8 15 17 21 15 8 21 13 18 Materials 1510 17 15 15 19 16 16 13 14 16 13 19 6 19 Transportation 2030 14 18 20 20 17 18 15 9 18 9 20 11 20 Automobiles and components 2510 16 19 23 21 19 17 19 22 19 16 23 7 21 Real estate 4040 23 22 17 16 11 21 20 19 20 11 23 12 22 Consumer durables and apparel 2520 21 21 21 22 22 20 18 20 21 18 22 4 23 Insurance 4030 19 23 16 23 23 23 23 23 23 16 23 7 Pearson correlation: annual median BE/ME ranking with the overall sample period median BE/ME ranking 0.76 0.94 0.80 0.93 0.66 0.86 0.81 0.75 Average rank range 11 Notes: The sample is collected from all active and inactive US firms trading on the NYSE, AMEX, NASDAQ exchanges and all other over-the-counter stocks (OTCBB, Pink Sheets, and “Other-OTC”) sourced from the Research Insight database. Results are formulated as in Table 1. Page 7 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 from those when sorting stocks by SIC codes for earlier periods.6 For investors, HML loadings shown in Table 3 generally confirm the growth (risk) orientation of industry groups such as pharmaceuticals (−0.68, t=−2.96) compared to the value (risk) orientation of industry groups such as insurance (0.63, t= 7.83). HML risk loadings are also generally consistent with the rank order of median BE/ME. The Pearson correlation between median BE/ME characteristics and HML factor loadings is 0.70 (t=3.07). While correlation results are generally unsurprising since HML is itself crafted from BE/ME rankings, the consistency and predictability of results in Table 3 are nevertheless helpful to investors who may attempt to capture a risk-based value premium by observing industry BE/ME accounting statistics. Table 3. Equal-weighted excess monthly returns of GICS sorted industry groups regressed on the Fama–French three-factor model. June 1999 to May 2007, (n=96) BE/ME rank GICS industry group Code Median BE/ME a b s h t(a) t(b) t(s) t(h) R2 1 Pharmaceuticals, etc. 3520 0.25 1.59 0.92 1.55 −0.68 (2.49)*(3.80)*(4.51)*(−2.96)* 0.69 2 Health care equipment and services 3510 0.41 0.82 0.84 0.94 0.25 (1.86) (6.61)*(6.62)*(1.92) 0.62 3 Software and services 4510 0.42 1.22 1.49 0.90 −0.84 (1.72) (10.21)*(4.45)*(−3.49)* 0.72 4 Household and personal products 3030 0.44 0.64 0.66 0.55 0.25 (1.26) (5.04)*(4.30)*(1.33) 0.35 5 Telecommunication services 5010 0.50 0.62 1.27 0.71 −0.37 (1.03) (7.00)*(4.39)*(−1.92) 0.63 6 Technology hardware and equipment 4520 0.50 1.03 1.55 1.17 −0.47 (1.93) (9.10)*(6.32)*(−2.94)* 0.80 7 Media 2540 0.51 −0.11 1.12 0.48 −0.05 (−0.22) (9.59)*(3.50)*(−0.36) 0.62 8 Energy 1010 0.52 1.49 0.97 0.48 0.80 (2.55)*(5.50)*(2.86)*(4.41)* 0.32 9 Food, beverage, and tobacco 3020 0.56 0.56 0.50 0.45 0.53 (1.79) (7.25)*(5.43)*(5.16)* 0.37 10 Commercial services and supplies 2020 0.58 0.40 0.93 0.61 0.34 (0.93) (10.96)*(5.62)*(2.66)* 0.55 11 Utilities 5510 0.59 0.18 0.60 0.22 0.75 (0.67) (7.02)*(3.43)*(8.61)* 0.51 12 Capital goods 2010 0.62 0.85 0.99 0.58 0.37 (2.26)*(11.56)*(5.57)*(2.96)* 0.65 13 Food and staples retailing 3010 0.62 −0.08 0.78 0.44 0.59 (−0.24) (8.52)*(4.75)*(4.80)* 0.50 14 Banks 4010 0.65 0.45 0.38 0.22 0.48 (1.98) (7.59)*(2.95)*(6.31)* 0.38 15 Materials 1510 0.65 0.30 1.08 0.53 0.72 (0.89) (14.17)*(5.30)*(6.50)* 0.66 16 Retailing 2550 0.66 0.21 1.07 0.60 0.42 (0.40) (9.63)*(3.55)*(2.15)* 0.48 17 Diversified financials 4020 0.67 1.05 0.98 0.53 0.19 (2.48)*(9.98)*(4.96)*(1.44) 0.59 18 Consumer services 2530 0.67 0.55 0.78 0.62 0.53 (1.39) (8.71)*(5.59)*(3.55)* 0.50 19 Transportation 2030 0.69 0.31 1.12 0.45 0.66 (0.63) (8.95)*(3.34)*(4.34)* 0.52 20 Real estate 4040 0.74 0.52 0.42 0.37 0.50 (1.86) (7.32)*(5.11)*(5.83)* 0.42 21 Automobiles and components 2510 0.74 −0.37 1.09 0.55 0.67 (−0.69) (8.98)*(3.89)*(3.82)* 0.46 22 Consumer durables and apparel 2520 0.79 0.05 0.99 0.54 0.57 (0.12) (11.45)*(4.70)*(4.27)* 0.59 23 Insurance 4030 0.83 0.28 0.77 0.18 0.63 (1.14) (11.84)*(2.63)*(7.83)* 0.62 Pearson correlation with Median BE/ME −0.64 −0.23 −0.73 0.70 t-Stat. (−2.82)* (−1.05) (−3.15)* (3.07)* Notes: Industry t-statistic use heteroskedasticity-consistent errors. *t-Statistic significant at the 5% level. Rpt −Rft =a+b[Rmt −Rft]+sSMBt+hHMLt+et Page 8 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Figure 1 provides further information on the consistency of BE/ME characteristics that may be beneficial for investment professionals seeking to allocate funds across industry groups. For this presentation, all NYSE, AMEX, NASDAQ, and all “other” OTC stocks are annually sorted into BE/ME Figure 1. Average annual percentage of GICS industry group stocks appearing in various BE/ME quintiles, June 1999 to May 2007 (n=96). Page 15 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Average monthly returns in Table 6, computed after excluding superior January returns, are by definition smaller than those shown earlier in Panel B of Table 4. However, the average monthly HI-LO value premium in sector returns for small-cap stocks in Table 4 (1.74%) is almost identical to the premium for small stocks in Table 6 (1.82%). Results show that the average value premium computed across GICS industry sectors is not impacted by January returns—although slight variations in individual sector premia are naturally observed. Moreover, results in Table 6 (excluding January) when compared to those earlier in Table 4 (including January) do not suggest the value premium is stronger in the 11 months excluding the month of January as observed by Dhatt et al. (1999). Nor are results consistent with findings in Loughran (1997) that the value premium is boosted in part by January returns. 6. Conclusion This paper helps to establish the body of research literature using the Global Industry Classification Standard, a system that Bhojraj et al. (2003) argues is superior for testing many industry-related research questions. Moreover, several important financial products are now constructed based on the GICS industry classification system. Any research attempting to reconcile academic research with market-based portfolios should use definitions and methods common and available to investors. Results for a pre-financial collapse sample period show that GICS industry groups exhibit large differences in BE/ME characteristics over the sample period; thus, potentially providing opportunities for investors to capture the value premium in average returns by strategically allocating funds to targeted industry groups. Further, the annual ranking of industry BE/ME appears to be relatively stable and potentially predictable for investors. The four lowest BE/ME ranked industries migrate to higher BE/ME characteristics on average only five places, suggesting that extreme growth-oriented industries have considerable temporal BE/ME stability. Value-oriented industry groupings are less stable over the sample period. Stocks from growth-oriented industries tend to cluster at high rates in the lowest BE/ME quintile while stocks from value-oriented industries appear more evenly distributed across the middle BE/ME quintiles over time. This means that the relatively poor returns generated by low BE/ME growth stocks may largely originate in a few persistently poor performing growth-oriented industry groups. If growth industries (or sectors) consistently underperform value industries, then investors can use these temporal characteristics to allocate away from these industries. However, Table 4 shows the relationship to be more complex. During the sample period, high BE/ME value stocks residing in low BE/ME growth sectors actually outperform value stocks in value sectors. The value premium is shown to disappear in large-cap stocks both within and across industry sectors. This finding is consistent with results in Loughran (1997) and problematic for the specification of the three-factor model as well as for a risk-based explanation to the BE/ME effect. Results in Table 4, using a sample period subsequent to that in Loughran (1997), appear to undermine the argument in Fama and French (2006) that Loughran’s observation of a weak value premium in large stocks is sample specific. This paper shows that the value premium is found to be statistically significant in all but one sector containing small-cap stocks and not statistically different from zero in all sectors containing large-cap stocks. Using within-sector data and across-sector data, this paper provides additional evidence confirming results in Haug and Hirschey (2006) who observe a strong January anomaly in more recent time periods. Results from within-sector tests of the value premium in Table 4 still survive once returns from the month of January are removed. Equally important, the across-sector association between sector BE/ME and the value premium in small-cap stocks remains statistically significant. Loughran (1997) argues that the BE/ME effect and the value premium are, in large part, driven by the January effect. However, results in Table 6 show that the average value premium computed across GICS industry sectors is not impacted by January returns. Results do not suggest the value premium is stronger in the 11 months, excluding the month of January, as observed by Dhatt et al. (1999). Nor are results consistent with findings in Loughran (1997) that the value premium is boosted in part by January returns. Page 16 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Funding The authors received no direct funding for this research. Author details Kenneth E. Scislaw 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-3753-9283 1 Breech School of Business, Drury University, 900 North Benton Avenue, Springfield, MO 35801, USA. Citation information Cite this article as: The value premium within and across GICS industry sectors in a pre-financial collapse sample, Kenneth E. Scislaw, Cogent Economics & Finance(2015), 3: 1045214. Notes 1. Chan, Lakonishok, and Swaminathan (2007) find that both GICS and FF industry coding systems yield “sets of economically related stocks.” They compared GICS and FF systems with a mechanical industry clustering method, and find GICS and FF performs well in capturing out of sample return covariance as well as co-movement in fundamental characteristics such as sales growth. 2. The Standard & Poor’s Company uses GICS for its highly popular SPDR® exchange traded funds. S&P converted its ETF funds to the GICS system in June 2002. The giant Vanguard investment firm also uses GICS to classify stocks to their various sector ETFs. Internationally, several stock exchanges such as the Toronto, ASX in Australia, and Nordic exchanges use GICS for stock listing classifications. According to the sales literature produced by S&P, 8 of the top 10 sell-side investment firms and 9 of the 10 buy-side investment firms utilize the GICS system. The fact that Standard &Poor’s and Morgan Stanley own and manage the dominant S&P and MSCI global index products ensures that GICS will be heavily used by the practitioner community to construct any index-related industry sub-classifications. S&P announced that the conversion of their popular S&P/Citicorp equity growth and value indexes to GICS was completed in July 2005. Yet another popular classification system in wide use by practitioners is the Industry Classification Benchmark (ICB) by Dow Jones Indexes and FTSE. The popular Dow Jones I-Shares utilize the ICB classification system. 3. See Chiang (2002) for a comprehensive literature survey and analysis of the effect of statistical return weighting methods on the value effect. 4. Mutual fund portfolio holding data source: Morningstar.com. 5. When using the value-weighted return computation method employed by Fama and French, none of the intercepts are statistically significant. The average across-industry alpha of 0.30 for value-weighted returns is almost identical to the average absolute regression intercept of 0.28 across the 48 value-weighted industry portfolios in Fama and French (1997). 6. Prior criticisms of the three-factor model for persistent negative correlation between alpha and the HML factor loading is also not remedied by using a different industry coding system. The correlation (not shown) between industry intercepts and HML slopes in threefactor model regressions in Table 3 remains negative and statistically significant (ρ=−0.53, t=−2.87). 7. Chan et al. (2007) find that two-digit GICS codes provide lower differences between return correlations for stocks in a particular sector and correlations for all other stocks outside that sector when compared to the four, six, and eight digit codes. While not optimal, two-digit GICS sorted sectors still reflect a considerable range of BE/ME characteristics. 8. For robustness, a check was also performed using the Fama and French average 50th percentile ME breakpoint on size, and results (not shown) are not materially different. 9. The Hi-LO value premium was statistically significant at the 5% level for all sectors below the 50/50 ME breakpoint and once again not statistically different from zero for all sectors above the size breakpoint. 10. Average annual sector ROA sorted into five BE/ME quintiles are observed for the current sample period (not shown). Hi-Lo quintile ROA statistics are distinctly negative for growth-oriented sectors and positive for value-oriented sectors (ROA/BEME ρ=0.72, t=2.57). Therefore, results are not inconsistent with arguments by Banko and Conover (2006) that the value premium results from investor risk-pricing of distress. 11. The January premium may simply be the result of data snooping as generally suggested by Lo and MacKinlay (1990) and Fama (1998). Fama argues that most market anomalies disappear after certain tweaks in statistical methods. 12. Results shown in Table 5 represent stocks above and below the Fama and French 25th percentile average size breakpoint for the sample period. The January premium completely disappears in large stocks in sorts using an average 50th percentile (below 50th/above 50th) size breakpoint. References Banko, J. C., & Conover, C. M. (2006). The relationship between the value effect and industry affiliation. The Journal of Business, 79, 2595–2616. http://dx.doi.org/10.1086/ jb.2006.79.issue-5 Bhojraj, S., Lee, C. M. C., & Oler, D. (2003). What’s my line? A comparison of industry classification schemes for capital market research. Journal of Accounting Research, 41, 745–774. http://dx.doi. org/10.1046/j.1475-679X.2003.00122.x Chan, L. K. C., Lakonishok, J., & Swaminathan, B. (2007). Industry classifications and the comovement of stock returns. Financial Analysts Journal, 63, 56–70. Chen, N., & Zhang, F. (1998). Risk and return of value stocks. The Journal of Business, 71, 501–535. http://dx.doi.org/10.1086/jb.1998.71.issue-4 Chiang, K. C. H. (2002). Portfolio return metric: Equal weights versus value weights (Working Paper). Retrieved from SSRN: http://ssrn.com/abstract=313428 Cohen, R. B., & Polk, C. K. (1996). The impact of industry factors in asset-pricing tests (Kellogg Graduate School of Management working paper). Retrieved from SSRN: http:// ssrn.com/abstract=7483 Daniel, K., & Titman, S. (1997). Evidence on the characteristics of cross sectional variation in stock returns. The Journal of Finance, 52, 1–33. http://dx.doi. org/10.1111/j.1540-6261.1997.tb03806.x Davis, J. L., Fama, E. F., & French, K. R. (2000). Characteristics, covariances, and average returns: 1929 to 1997. The Journal of Finance, 55, 389–406. http://dx.doi.org/10.1111/jofi.2000.55.issue-1 Dhatt, M. S., Kim, Y. H., & Mukherji, S. (1999). The value premium for small-capitalization stocks. Financial Analysts Journal, 55, 60–68. http://dx.doi.org/10.2469/faj.v55.n5.2300 Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49, 283–306. http://dx.doi.org/10.1016/ S0304-405X(98)00026-9 Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47, 427–465. http://dx.doi.org/10.1111/j.1540-6261.1992. tb04398.x Page 17 of 18 Scislaw, Cogent Economics & Finance (2015), 3: 1045214 http://dx.doi.org/10.1080/23322039.2015.1045214 Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. http://dx.doi.org/10.1016/0304-405X(93)90023-5 Fama, E. F., & French, K. R. (1997). Industry costs of equity. Journal of Financial Economics, 43, 153–193. http://dx.doi.org/10.1016/S0304-405X(96)00896-3 Fama, E. F., & French, K. R. (2006). The value premium and the CAPM. The Journal of Finance, 61, 2163–2185. http://dx.doi.org/10.1111/jofi.2006.61.issue-5 Graham, B., Dodd, D. L., & Cottle, S. (1962). Security analysis (4th ed.). New York, NY: McGraw-Hill. Haug, M., & Hirschey, M. (2006). The January effect. Financial Analysts Journal, 62, 78–88. http://dx.doi.org/10.2469/faj.v62.n5.4284 Houge, T., & Loughran, T. (2006). Do investors capture the value premium? Financial Management, 35, 5–19. http://dx.doi.org/10.1111/fima.2006.35.issue-2 Lakonishok, J., & Smidt, S. (1988). Are seasonal anomalies real? A ninety-year perspective. Review of Financial Studies, 1, 403–425. http://dx.doi.org/10.1093/rfs/1.4.403 Lo, A. W., & MacKinlay, A. C. (1990). Data-snooping biases in tests of financial asset pricing models. Review of Financial Studies, 3, 431–467. http://dx.doi.org/10.1093/rfs/3.3.431 Loughran, T. (1997). Book-to-market across firm size, exchange, and seasonality: Is there an effect? The Journal of Financial and Quantitative Analysis, 32, 249–268. http://dx.doi.org/10.2307/2331199 Nelson, J. M. (2006). Intangible assets, book-to-market, and common stock returns. Journal of Financial Research, 29, 21–41. http://dx.doi.org/10.1111/jfir.2006.29.issue-1 Reinganum, M. R. (1983). The anomalous stock market behavior of small firms in January: Empirical tests for tax-loss selling effects. Journal of Financial Economics, 12, 89–104. Retrieved from http://www.sciencedirect.com/ science/article/pii/0304405X83900296 Roll, R. (1983). Vas ist das? The turn of the year effect and the return premia of small firms. The Journal of Portfolio Management, 9, 18–28. Retrieved from http://www.iijournals.com/doi/abs/10.3905/ jpm.1983.18?journalCode=jpm Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive evidence of market inefficiency. The Journal of Portfolio Management, 11, 9–16. http://dx.doi.org/10.3905/jpm.1985.409007 Rozeff, M. S., & Kinney, Jr., W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3, 379–402. http://dx.doi.org/10.1016/0304-405X(76)90028-3 Appendix A. The GICS sector and industry group sub-classifications. Code Sector Subcode Industry groups 10 Energy 1010 Energy 15 Materials 1510 Materials 20 Industrials 2010 Capital goods 2020 Commercial services and supplies 2030 Transportation 25 Consumer discretionary 2510 Automobiles and components 2520 Consumer durables and apparel 2530 Consumer services 2540 Media 2550 Retailing 30 Consumer staples 3010 Food and staples retailing 3020 Food, beverage and tobacco 3030 Household and personal products 35 Health care 3510 Health care equipment and services 3520 Pharmaceuticals, biotechnology and life sciences 40 Financials 4010 Banks 4020 Diversified financials 4030 Insurance 4040 Real estate 45 Information technology 4510 Software and services 4520 Technology hardware and equipment 4530 Semiconductors and semiconductor equipment 50 Telecommunication services 5010 Telecommunication services 55 Utilities 5510 Utilities Source: MSCI Barra (classifications effective through 29 August 2008). 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