Equity Risk Service Q1/2018. Estimating Betas for JSE-Listed Companies and Indices

Transcription

1 Equity Risk Service Q1/2018 Estimating Betas for JSE-Listed Companies and Indices prepared by The African Institute of Financial Markets and Risk Management (AIFMRM)

2 Contents 1 Introduction 4 2 The Basics of Risk Management 5 3 Use of the Service 8 4 Beta Tables FTSE/JSE Africa Index Series JSE-Listed Shares Questions and Answers 29 6 A Worked Example 31 7 Bibliography 36 8 Acknowledgements 38 9 Disclaimer 39 Equity Risk Service Q1/2018 2

3 Executive Summary AIFMRM s Equity Risk Service AIFMRM s Equity Risk Service is a continuation of the same service previously offered by BNP Paribas Securities South Africa (Pty) Ltd (BNP). This service is aimed at providing up-to-date risk measures and associated statistics for the stocks and indices that are listed on the Johannesburg Stock Exchange (JSE). The Equity Risk Service is based not only on the American and the British experience but also on an ongoing research programme at the University of Cape Town (UCT). The estimates are based on a price and volume series database sourced from the JSE Ltd, Bloomberg L.P. and INET BFA - IRESS. Unique Estimation Features This service differs from others in that the estimation procedure is refined and enhanced to improve the accuracy of the risk estimates. Two adjustments have been effected to the standard estimation procedure - the first improves the sampling-theory beta estimate by reducing sampling error, while the second further improves this estimate by reducing estimation error. Firstly, a thin-trading correction procedure, known as the trade-to-trade procedure, is implemented to preclude biases in sample beta estimates caused by thin-trading. Secondly, a Bayesian adjustment is implemented which reduces estimation error by incorporating prior distributional information on sample beta estimates. Prior research by BNP has shown that this adjustment improves the predictability of betas by approximately 20%. Market Proxies The JSE is unique in the sense that it is composed of three distinctly different types of shares: resources, financial and industrial shares. Investors are often concerned with the behaviour of shares in these markets relative to an index which characterises these markets separately, rather than relative to an overall market index. For example, many investors prefer to measure the performance of an industrial share relative to an industrial market index and a gold share relative to a mining index. To accommodate these preferences for each listed share, risk statistics relative to each share s characteristic market index as well as relative to the overall market index have been included. Proxies for the three main sectors as well as the overall market are considered to be the following: FTSE/JSE All Share Index (J203); FTSE/JSE Top 40 Index (J200); FTSE/JSE Financials and Industrials Index (J250); FTSE/JSE Industrials Index (J257); and the FTSE/JSE Resources Index (J258). Therefore, the tables which follow provide risk statistics (primarily betas in this report, but other statistics are provided in the accompanying Excel workbook) for all JSE-listed shares and indices relative to the aforementioned market and sector proxies. Equity Risk Service Q1/2018 3

4 1 Introduction The quantitative understanding of equity market risk has evolved significantly since the advent of Modern Portfolio Theory in 1952, see [Mar52], and the Capital Asset Pricing Model (CAPM) in the 1960 s, see [Tre61], [Tre62], [Sha64], [Lin65b], [Lin65a], [Mos66]. What has persisted is the fundamental understanding of equity risk as been decomposable into systematic and specific components, with the CAPMdefined beta providing the path to the first quantitative notion and measure of these risks. While more complex models have been advocated in academia and practice, the classical CAPM-defined beta is still widely used in the assessment of equity risk. As such, the estimation of the CAPM-defined beta is the focus of this report. AIFMRM s Equity Risk Service AIFMRM s Equity Risk Service is a continuation of the same service previously offered by BNP Paribas Securities South Africa (Pty) Ltd (BNP). This service is aimed at providing up-to-date risk measures and associated statistics for the stocks and indices that are listed on the Johannesburg Stock Exchange (JSE). The Equity Risk Service is based not only on the American and the British experience but also on an ongoing research programme at the University of Cape Town (UCT). The estimates are based on a price and volume series database sourced from the JSE Ltd, Bloomberg L.P. and INET BFA - IRESS. Unique Estimation Features This service differs from others in that the estimation procedure is refined and enhanced to improve the accuracy of the risk estimates. Two adjustments have been effected to the standard estimation procedure - the first improves the sampling-theory beta estimate by reducing sampling error, while the second further improves this estimate by reducing estimation error. Firstly, a thin-trading correction procedure, known as the trade-to-trade procedure, is implemented to preclude biases in sample beta estimates caused by thin-trading. Secondly, a Bayesian adjustment is implemented which reduces estimation error by incorporating prior distributional information on sample beta estimates. Prior research by BNP has shown that this adjustment improves the predictability of betas by approximately 20%. Due to the increased emphasis on professionalism, most investment managers no longer doubt the usefulness and the scientific merit of the tools of Modern Portfolio Theory. However, many investors have been daunted by the myths that the level of mathematics needed is unmanageable. Therefore some explanatory material is also included in Section 2, which shows that the crucial ideas are simple ones and are free from mathematical complexity. In Section 3, basic risk management ideas are expanded upon and suggestions for how the Equity Risk Service may be used are offered. In Section, 4 the risk and associated statistics for JSE-listed stocks and indices are tabled. In Section 5, some pertinent questions are answered; in Section 6, a worked example is included; and in Section 7, some references to pertinent literature on the subject of systematic risk measurement is provided. Equity Risk Service Q1/2018 4

5 2 The Basics of Risk Management The major challenge facing investors has always been the maximisation of their wealth in a world of uncertainty. In the world of finance, the level of uncertainty, or risk, of a share has become associated with the degree to which the share price fluctuates over a specific horizon. The more variable the company s share price, the more risky the share. To get a better picture of the notion of risk, consider Figure 1, which demonstrates the price variability of Gold Fields, a high-risk gold share, and Woolworths, a low-risk general retail share since FIGURE 1: Gold Fields (GFI) and Woolworths (WHL) share prices since October It is also possible to attach a quantitative measure to the risk of a share by measuring its variability. This is done by computing the standard deviation of the percentage price changes (percentage returns). Standard deviation or volatility is a widely accepted statistical measure of a share s total risk. The higher the standard deviation, the riskier the share. (The annualised standard deviation or volatility estimates for each of the JSE-listed shares and indices may be found in tables in the accompanying Excel workbook.) As expected, Gold Fields had an annualised standard deviation of 49.19% over the entire period, while Woolworths has an annualised standard deviation of only 35.15% over the same period. It is important to note that a company s total risk can be split into two parts, namely, market risk and unique risk. Market/systematic risk reflects the fluctuations which are linked to factors which affect the market as a whole (e.g. political events, interest rate changes, etc.). Unique/non-systematic risk reflects the fluctuations which are linked to events which are unique to the company (e.g. bad management, worker strikes, operational issues, etc.). Equity Risk Service Q1/2018 5

6 Market/Systematic Risk All share prices are driven to some extent by market forces, some more than others. Beta measures the sensitivity of a share price to movements in the market as a whole. A share with a beta of 1.5 will move, on average, 15% for each 10% move in the market. Generally, such a share would prove aggressive, performing well in bull markets and poorly in bear markets. On the other hand, a share with a beta of 0.5 will move, on average, only 5% for every 10% move in the market. Generally, such a share would prove to be defensive, underperforming the market index in bull markets, but would do well, relative to other shares, in bear markets. Finally, a share with a beta of 1 will, on average, move in line with the market. We can determine what proportion of a share s total risk is attributable to market risk by using the R 2 statistic. R 2 indicates the proportion of a share s total risk that is attributable to market movements. Unique/Specific Risk Unexpected price movements (those which are not market driven) are a result of a share s unique risk. It is also possible to determine what proportion of a share s total risk is unique risk by using the R 2 statistic. While R 2 indicates the proportion of a share s total risk that is attributable to market movements, 1 R 2 indicates the proportion of a share s total risk that is not attributable to market movements but rather to factors that are unique to the company, since 0 R 2 1. Systematic versus Specific Risk Firstly, the CAPM advocates that investors should not expect to be compensated (expect more profit) for taking on unique risk, but they can expect to receive higher returns for taking on market risk. This makes sense as most investors are concerned with holding a portfolio of investments rather than one individual share. The major reason for holding portfolios is an intuitive one - we don?t want to put all our eggs in one basket. Put simply, if portfolios are diversified, the unique risks of individual shares tend to cancel each other out. For example, while Gold Fields shares had periods of decline since 2000, Woolworths shares increased in value over the same period. If an investor had been holding a portfolio of both shares, the bad news of Gold Fields would largely have been cancelled by the good news of Woolworths? success and so it would have diversified some of the unique risk of Gold Fields away. This reduction in unique risk is precisely what diversification is all about. In fact, a portfolio consisting of an investment divided equally between almost any 10 listed companies will have eliminated over 80% of the unique risk of the portfolio. And so empirical evidence is consistent with intuition - if one is not forced to take on unique risk (since it can be eliminated by diversification), then why should one be rewarded for it? However, no matter how Equity Risk Service Q1/2018 6

7 much one diversifies, one cannot eliminate market risk. One cannot escape the economy-wide perils that affect the entire market - each share in a portfolio will respond to the news affecting the economy as a whole. The market risk of any portfolio can be determined by computing the weighted average of the betas of the constituent shares of the portfolio. This is precisely why beta is such an important tool to professional investment management. Unique risk can easily be diversified away; leaving the beta of a welldiversified portfolio as the key risk measure which reveals all there is to know about the portfolio?s risk. The second reason why it is important to know the difference between market risk and unique risk concerns the way people approach investment analysis. Some investors are skilled at predicting which way the market will be moving, others attempt to identify which sectors they should be in, and perhaps analyse particular shares. One s skills in these areas are inextricably linked to the two components of total risk and have important implications for the composition of the portfolios you should hold. To capitalise on any skills one may have in forecasting the market, one will need to be concerned with shifting the beta of a portfolio - increasing it when a market is predicted/expected to rise and vice versa. That is, one will be altering the market risk exposure of a portfolio. On the other hand, if one is skilled at selecting sectors and shares, or have some information that the rest of the market does not, one may need to take on some unique risk. In summary, it is worth mentioning that, measured over long periods of time, high beta shares have given the highest returns. However, it is important to emphasise over long periods of time. Clearly, during bear markets, high beta shares are the worst performers. While many investors may be seeking high returns from high beta shares, there is absolutely no guarantee that this will be achieved. That is why beta is referred to as a measure of risk - high beta shares are genuinely more risky than low beta shares. Some useful references to academic literature are the following: [BB93b], [BB93a], [BB98], [BB97], [Bra91], [AB93], [Bra89], [BB89], [FF77], [Ber94], [GM96], [Bla93], [Gri93], [Dre92] and [Ber92]. Equity Risk Service Q1/2018 7

8 3 Use of the Service The ERS is not about advising one on what shares he/she should buy; its aim is to supply back-up information to the astute investor or portfolio manager so that they may make sound, professional investment decisions. Below, some ideas are offered on how risk measures can be used. These ideas by no means cover all the uses as there exists a multitude of specific financial models which require these parameters. Monitoring a Portfolio s Risk Level The biggest concern for investment managers is that a so-called balanced portfolio realises a substantial loss during a market recession, or a so-called growth fund returns only 10% when the market goes up 20%. Whether one is a private investor or a professional portfolio manager, one needs to know how much risk a portfolio is exposed to and how to monitor the risk over time. Calculating a Portfolio s Risk By now it should be clear that, for portfolios, beta is the most important component of risk (since market risk is the dominant risk component for diversified portfolios). The beta of a portfolio tells us how sensitive it is to market movements. Calculating a portfolio s beta is straightforward: simply look up the individual betas of the constituent shares and weight each one by the proportion of the fund that is invested in that share. The sum of these weighted values will yield your portfolio s beta (to calculate the actual amount of market risk that a portfolio has, refer to the worked example in Section 6). The measurement of the unique risk of a portfolio is slightly more intricate. Naturally, if a portfolio is the market index or something very close to it, the unique risk of the portfolio will be virtually zero. However, if a portfolio is not diversified, one may need some data on the recent history of the portfolio?s performance. Alternatively, if a portfolio is reasonably diversified, one may calculate the portfolio?s unique risk from the unique risk figures of the constituent shares, analogous to the method employed in the worked example in Section 6. If a portfolio is fairly well diversified, this approach gives a good estimate of its unique risk. However, if many of the shares in a portfolio are clustered in one industry, then the true unique risk will be slightly higher than calculated. Once the two risk components of a portfolio have been quantified, these may be compared, for instance, your target levels. If either of the risk components is off target, the remedy is clear. Traditionally, the approaches to portfolio management have included: restricting selection choices to an eligible list of large companies; specifying a minimum yield level; restricting the proportion in a single share or sector; and even authorising every deal that is made. This can impose unnecessary constraints that fail to control the risks. The modern way to manage portfolios is to measure beta and unique risk on an ongoing basis and to track actual levels to target levels. Equity Risk Service Q1/2018 8

9 Measuring a Portfolio s Performance In the past, many managers have compared their funds on the basis of returns alone. Performance figures that are unadjusted for risk indicate how much money the portfolio has earned, but provide no information about the risks that were taken. Managers may argue that it is profit that clients are concerned with and find it difficult to convince clients that their portfolios yield lower returns than their competitors simply because they are exposed to lower risks. But managers who take on unnecessarily high-risk portfolios in an attempt to gain a competitive edge on return are foolish and will be managers no more when the market turns bearish. Merit should always go to the managers who have achieved the highest risk-adjusted returns even if their unadjusted returns are lower. Adjusting for Risk A lot has been written about adjusting for risk. Some measures have been designed to compare portfolios on a one-off basis, and others have been designed to continually monitor the risk-adjusted performance of a portfolio. For example, to compare risk-adjusted performances of various portfolios at the year-end, one could simply divide the annual return on each portfolio by its beta (Treynor s measure). Clearly, the portfolio having the largest measure would be the best risk-adjusted performer. If however, the portfolios are not fully diversified, one should perhaps divide by their standard deviation (i.e. total risk) instead of beta and compare them on this basis (Sharpe s measure). Alternatively, one may want to monitor how well an individual portfolio is performing. In this case, one could compute the portfolio s abnormal return on an ongoing basis. The term abnormal return embodies the idea of having returns over and above (or below) what is expected, given the risk of a portfolio. The idea is to compute the return for a portfolio over and above the return you would expect for a portfolio having the same beta. For example, consider a portfolio having a beta of 1. Since this is the same beta as that of the market index, one would expect it to perform just as well as the market. What about an investment with a beta of zero? Zero beta means zero market risk. If one were to invest all their money in a fixed interest instrument, one would receive the interest rate as a return but the beta would be zero. So the interest rate can be used as a benchmark return for a portfolio with a beta equal to zero. Now suppose that a portfolio has a beta of 0.7. This can be viewed as having the same beta as a portfolio with 70% invested in the market index and 30% invested at a fixed interest rate. So to compare like-with-like, the benchmark return on this portfolio can be computed as 0.7 multiplied by the return of the market index (over the same period) plus 0.3 multiplied by the interest rate. Having obtained this benchmark return, subtract it from the portfolio s actual return realised over the same period. This is the portfolio s abnormal return. If the abnormal return is positive, the portfolio is doing well. If it is negative, the portfolio is underperforming the benchmark. Equity Risk Service Q1/2018 9

10 Selection and Timing In this section, two issues that are of utmost importance to all portfolio managers are considered. Firstly, which shares to choose (selection), and secondly when to trade (timing). Considering selection, one should look for shares with high abnormal returns. By contrast, shares that consistently produce negative abnormal returns are the ones to sell. The abnormal return for a share can be calculated in the same way as that for a portfolio described above (see also Section 6 for a worked example). Abnormal returns can even be computed on a daily or weekly basis to closely monitor opportunities to trade in shares. Calculating abnormal returns of shares is well and good, but there are hundreds of shares. Which shares should one look for? Obviously, one should focus their efforts on the sectors and shares which one knows best. One may also want to focus on sectors where he/she hold much less than market proportions. But the shares which are most likely to yield significant abnormal returns are the ones having high unique risk. If a share had no unique risk, there would be no purpose in analysing its abnormal returns as its price movements would be determined entirely by its beta. Considering market timing, this depends very much on one s ability to forecast which way the market is moving. If one thinks the market is about to go up, more wealth should be moved into high-beta shares. On the other hand, if one thinks the market is about to fall, then it would be better to move into lowbeta shares or into liquid assets. Of the two, going liquid is easier and may be less costly, but one may be constrained to remain invested in liquid assets (e.g. fixed-term deposits). Whichever way one chooses, however, it will be better to sell the highest beta shares first. Confidence in forecasts should also impact the degree to which a portfolio is shifted. Clearly, the less confident one is about their forecasts, the more moderate should the shift be in the beta of their portfolio. The ability to make accurate forecasts and the ability to pick winners are clearly going to influence an investment strategy. Assuming that one is a fairly good analyst that is correct 6 times out of 10. Even with these moderate levels of forecasting skills one can produce useful profits. One may thus want to take on a slight amount of unique risk, although it would be wise to limit unique risk to a maximum of about 10% of a portfolio s total risk. If, however, one does not claim to be able to pick winners or if one has no particular forecasting prowess, then one should hold as diversified a portfolio as possible. Equity Risk Service Q1/

11 4 Beta Tables Market Proxies The JSE is unique in the sense that it is composed of three distinctly different types of shares: resources, financial and industrial shares. Investors are often concerned with the behaviour of shares in these markets relative to an index which characterises these markets separately, rather than relative to an overall market index. For example, many investors prefer to measure the performance of an industrial share relative to an industrial market index and a gold share relative to a mining index. To accommodate these preferences for each listed share, risk statistics relative to each share s characteristic market index as well as relative to the overall market index have been included. Proxies for the three main sectors as well as the overall market are considered to be the following: FTSE/JSE All Share Index (J203); FTSE/JSE Top 40 Index (J200); FTSE/JSE Financials and Industrials Index (J250); FTSE/JSE Industrials Index (J257); and the FTSE/JSE Resources Index (J258). Therefore, the tables which follow provide risk statistics (primarily betas in this report, but other statistics are provided in the accompanying Excel workbook) for all JSE-listed shares and indices relative to the aforementioned market and sector proxies. 4.1 FTSE/JSE Africa Index Series About the Index Statistics Tables For the FTSE/JSE Africa Index Series, the tables are decomposed or split by index type with each column defined as follows: Code: The index code which identifies the respective FTSE/JSE index. Name: The name of the respective FTSE/JSE index. # Points: The number of months that the index has been in existence over the past 5-year period. Beta(x): This is the sensitivity of the respective index level to changes in the market proxy x, where x = J203, J200, J250, J257 or J258. A beta of 1 means that the index will, on average, move in line with the market proxy. A beta greater than 1 implies that the index will tend to move more in percentage terms than the market proxy and vice versa. If the value is depicted in: (i) red, then the beta value is negative; (ii) green, then the beta value is statistically significant with 95% confidence; or (iii) blue, then the beta value is statistically significant with 99% confidence. Equity Risk Service Q1/

12 TABLE 1: All Africa Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) JA00 All Africa JA0R ZAR All Africa TABLE 2: Dividend Forecast Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J259 Dividend Plus TABLE 3: Headline Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J200 Top J201 Mid Cap J202 Small Cap J203 All Share J204 Fledgling J205 Large Cap J206 Large and Mid Cap TABLE 4: Headline Index Variants Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J2EQ Equally Weighted Top J300 Capped Top J303 Capped All Share JN00 Top 40 Net TRI JN23 All Share Net TRI TABLE 5: ICB Industry Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J500 Oil & Gas J510 Basic Materials J520 Industrials J530 Consumer Goods J540 Health Care J550 Consumer Services J560 Telecommunication J580 Financials J590 Technology Equity Risk Service Q1/

13 TABLE 6: ICB Sector Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J055 Oil & Gas Producers J135 Chemicals J173 Forestry & Paper J175 Industrial Metals & Mining J177 Mining J235 Construction & Materials J272 General Industrials J273 Electronic & Electrical Equipment J275 Industrial Engineering J277 Industrial Transportation J279 Support Services J335 Automobiles & Parts J353 Beverages J357 Food Producers J372 Household Goods & Home Construction J376 Personal Goods J378 Tobacco J453 Health Care Equipment & Services J457 Pharmaceuticals & Biotechnology J533 Food & Drug Retailers J537 General Retailers J555 Media J575 Travel & Leisure J653 Fixed Line Telecommunications J657 Mobile Telecommunications J835 Banks J853 Non-life Insurance J857 Life Insurance J863 Real Estate Investment & Services J867 Real Estate Investment Trusts J877 General Financial J898 Equity Investment Instruments J953 Software & Computer Services J957 Technology Hardware & Equipment Equity Risk Service Q1/

14 TABLE 7: ICB Sub-Sector Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J150 Gold Mining J151 Coal Mining J153 Platinum & Precious Metals J154 General Mining TABLE 8: Preference Share Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J251 Preference Share JN51 Preference Share Net TRI TABLE 9: RAFI Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J260 RAFI J263 RAFI All Share J283 Capped RAFI All Share JNR4 RAFI 40 Net TRI TABLE 10: Secondary Market Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J230 Development Capital J231 Venture Capital J232 Alternative Exchange J233 Alternative Exchange TABLE 11: Shareholder Weighted (SWIX) Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J400 SWIX Top J403 SWIX All Share JN43 SWIX All Share Net TRI JNX4 SWIX Top 40 Net TRI TABLE 12: Shariah Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J140 Shariah Top J141 Capped Shariah Top J143 Shariah All Share JNS4 Shariah 40 Net TRI Equity Risk Service Q1/

15 TABLE 13: Specialist Indices Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J250 Financials and Industrials J257 Industrials J258 Resources TABLE 14: Specialist Property Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J253 SA Listed Property J254 Capped Property J255 Property Unit Trust J256 Property Loan Stock TABLE 15: Style (Value and Growth) Index Series Code Name # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) J330 Value J331 Growth Equity Risk Service Q1/

16 4.2 JSE-Listed Shares About the Share Statistics Tables For the FTSE/JSE Africa Index Series, the tables are decomposed or split by index type with each column defined as follows: Code: The share code which identifies the respective JSE-listed share. Cap: The market capitalisation segment to which the respective share belongs: L - for large capitlalisation shares; M - for mid capitalisation shares; S - for small capitalisation shares; F - for fledgling capitalisation shares; A - for AltX-lised shares; and - for unclassified shares. First Trade: The first day on which the share traded over the past 5-year period. Last Trade: The last day on which the share traded over the past 5-year period. % Traded: This quantity is the percentage of the business days traded between the first and last trade date. This provides an indication of the extent to which the security is thinly traded. # Points: The number of months that the share traded over the past 5-year period. Beta(x): This is the sensitivity of the respective share price to changes in the market proxy x, where x = J203, J200, J250, J257 or J258. A beta of 1 means that the share will, on average, move in line with the market proxy. A beta greater than 1 implies that the share will tend to move more in percentage terms than the market proxy and vice versa. If the value is depicted in: (i) red, then the beta value is negative; (ii) green, then the beta value is statistically significant with 95% confidence; or (iii) blue, then the beta value is statistically significant with 99% confidence. TABLE 16: Oil & Gas (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) EEL 28-Mar Mar-18 92% ERN 28-Feb Mar-18 22% MNK S 31-Dec Mar-18 90% OAO 28-Mar Oct-17 59% REN A 30-Jun Mar-18 69% Equity Risk Service Q1/

17 TABLE 17: Basic Materials (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) ACL S 28-Mar Mar % AFE M 28-Mar Mar % AFX S 28-Mar Mar-18 99% AGL L 28-Mar Mar % AMS L 28-Mar Mar % ANG L 28-Mar Mar % ARI M 28-Mar Mar % ASR M 28-Mar Mar % ATL 28-Mar Mar-18 72% BAU F 27-Mar Mar-18 70% BIL L 28-Mar Mar % BSS 28-Mar Feb-18 81% BUC A 7-Mar Mar-18 7% CMO 20-Mar Mar-18 62% CRD 27-Mar May-17 53% DRD F 28-Mar Mar % DTA F 26-Mar Mar-18 61% EPS 28-Mar Mar-18 64% EXX M 28-Mar Mar % FCR F 14-Mar Mar-18 52% FSE 12-Mar Mar-15 33% GFI M 28-Mar Mar % GLN L 29-Nov Mar % HAR M 28-Mar Mar % HLM F 28-Mar Mar % HWA F 19-Mar Mar-18 13% IMP M 28-Mar Mar % ISB F 28-Mar Mar-18 63% JBL A 28-Mar Mar-18 99% KBO A 20-Mar Mar-18 46% KIO L 28-Mar Mar % LON S 28-Mar Mar % MCZ F 28-Mar Mar-18 99% MED 27-Mar-13 2-Dec-16 42% MMH 27-Mar Sep-14 27% Equity Risk Service Q1/

18 TABLE 18: Basic Materials (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) MND L 28-Mar Mar % MNP L 28-Mar Mar % MRF F 28-Mar Mar % NHM M 28-Mar Mar % OMN M 28-Mar Mar % ORN F 28-Sep Mar-18 95% PAN S 28-Mar Mar % RBP S 28-Mar Mar % RDI 13-Mar Mar-17 24% RLF F 28-Mar Mar % RNG F 28-Mar Mar-18 58% RSG F 28-Mar Mar-18 18% S32 L 29-May Mar % SAP L 28-Mar Mar % SGL M 28-Mar Mar % SOL L 28-Mar Mar % SPA F 28-Mar Mar-18 16% TAW 19-Mar Mar-18 68% THA 30-Apr Mar-18 70% TSX F 28-Mar Mar-18 96% UCP F 28-Mar Mar-18 95% WCC 31-Oct Mar-15 27% WEZ F 28-Mar Mar % WSL F 28-Mar Mar % YRK F 28-Mar Mar-18 88% ZCI 14-Mar Jul-15 9% Equity Risk Service Q1/

19 TABLE 19: Industrials (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) ACE A 28-Mar Mar-18 53% ADR F 28-Mar Mar % AEG F 28-Mar Mar % AFT S 28-Mar Mar % ANS A 28-Mar Mar-18 94% ARH F 26-Mar Mar-18 76% ART F 28-Mar Mar-18 95% BAW M 28-Mar Mar % BCF F 28-Mar Mar-18 91% BEL F 28-Mar Mar-18 85% BIK 27-Mar Jul-13 3% BSR F 28-Mar Mar-18 97% BVT L 28-Mar Mar % CAC F 29-Jul Feb-18 2% CGR F 28-Mar Mar-18 99% CIL F 28-Mar Mar % CRG F 28-Mar Mar-18 29% CSG F 27-Mar Mar-18 90% CTK S 30-Dec Mar-18 79% DAW F 28-Mar Mar-18 92% ELI F 28-Mar Mar % ELR F 28-Mar Mar-18 69% ENX F 28-Mar Mar-18 86% ESR F 28-Mar Mar-18 96% EXG F 28-Mar Mar-18 99% GND S 28-Mar Mar % GRF F 28-Mar Mar % HDC S 28-Mar Mar % HWN F 28-Mar Mar-18 87% IPL M 28-Mar Mar % IVT S 28-Mar Mar % IWE F 28-Mar Mar-18 88% KAP M 28-Mar Mar % KDV 27-Mar Mar-18 14% MAP A 31-May Mar-18 79% Equity Risk Service Q1/

20 TABLE 20: Industrials (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) MDI F 28-Mar Mar-18 93% MFL F 28-Mar Mar-18 99% MIX F 28-Mar Mar-18 92% MMG F 28-Mar Mar-18 85% MMP F 22-Mar Mar-18 35% MPT S 28-Mar Mar % MRI 28-Mar Jul-16 43% MUR S 28-Mar Mar % MZR F 28-Mar Mar-18 36% NPK M 28-Mar Mar % NT1 S 28-Mar Mar-18 76% NVS F 31-Mar Mar-18 94% OLG F 28-Mar Mar-18 98% PMV F 28-Mar Mar-18 20% PPC S 28-Mar Mar % PSV A 28-Mar Mar-18 50% RBX S 28-Mar Mar % REM L 28-Mar Mar % RLO M 28-Mar Mar % SEP F 28-Mar Mar-18 98% SNV F 28-Mar Mar % SOH F 28-Mar Mar-18 55% SPG M 28-Mar Mar % SSK F 28-Mar Mar-18 97% TOR 28-Mar Mar-18 96% TPC F 28-Mar Mar-18 68% TRE S 28-Mar Mar % TRL F 30-Oct Mar-18 78% VLE F 28-Mar Mar-18 88% WBO S 28-Mar Mar % WEA A 27-Mar Mar-18 63% WKF 26-Mar Mar-18 28% Equity Risk Service Q1/

21 TABLE 21: Consumer Goods (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) AHL 23-Apr Mar-18 15% ANH 29-Jan Mar % ARL S 28-Mar Mar % AVI M 28-Mar Mar % AWT 27-Mar Jan-15 20% BTI L 28-Mar Mar % CFR L 28-Mar Mar % CKS F 28-Mar Mar-18 54% CLR S 28-Mar Mar % CVH S 28-Mar Mar % DST M 28-Mar Mar % ILE A 28-Mar Mar-18 44% MTA S 28-Mar Mar % NWL F 28-Mar Mar-18 58% OCE M 28-Mar Mar % PFB F 31-Mar Mar-18 80% PFG M 28-Mar Mar % QFH F 31-Oct Mar-18 99% RBA 28-Mar-13 9-Feb-16 50% RCL M 28-Mar Mar % RFG S 31-Oct Mar % SHG F 31-Mar Mar-18 90% SNH M 28-Mar Mar % TBS L 28-Mar Mar % TON M 28-Mar Mar % TABLE 22: Health Care (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) ACT S 28-Mar Mar-18 99% AIP S 28-Mar Mar % APN L 28-Mar Mar % ASC S 29-Nov Mar % AVL A 30-Apr Mar-18 99% GLI A 30-Nov Mar-18 25% LHC M 28-Mar Mar % MEI L 29-Feb Mar % NTC M 28-Mar Mar % NUT A 28-Mar Mar-18 84% Equity Risk Service Q1/

22 TABLE 23: Consumer Services (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) ADH S 28-Mar Mar % AET 28-Mar-13 3-Jul-14 16% AME F 12-Mar Mar-18 32% AON 26-Mar Mar-18 5% AOO 6-Mar Aug-17 1% BID L 31-May Mar % CAT S 28-Mar Mar-18 92% CHP F 29-May Mar % CLH S 28-Mar Mar % CLS M 28-Mar Mar % CMH F 27-Mar Mar-18 85% COH M 28-Mar Mar % COM F 28-Mar Mar-18 98% CSB S 28-Mar Mar % CUL 18-Mar Mar-18 33% DCP M 30-Nov Mar % EMH F 15-Mar Mar-18 50% EMN 8-Mar Mar-18 48% FBR M 28-Mar Mar % GBI A 29-Feb Mar-18 55% HIL 22-Dec Mar-18 34% ITE M 28-Mar Mar-18 91% KAL F 30-Jun Mar-18 94% LEW S 28-Mar Mar % MRP L 28-Mar Mar % MSM M 28-Mar Mar % NCS F 28-Mar Mar-18 19% NPN L 28-Mar Mar % PEM A 31-Mar Mar-18 96% PHM F 28-Mar Mar-18 92% PIK M 28-Mar Mar % RTN F 6-Mar Oct-17 8% RTO F 6-Mar Jan-18 4% SDO S 31-Oct Mar % SHP L 28-Mar Mar % Equity Risk Service Q1/

23 TABLE 24: Consumer Services (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) SPP M 28-Mar Mar % SRR 29-Sep Mar % SUI S 28-Mar Mar % SUR S 28-Mar Mar % TAS F 28-Mar Mar-18 99% TBG F 27-Mar Mar-18 82% TFG M 28-Mar Mar % TRU M 28-Mar Mar % TSH M 28-Mar Mar % VMK F 28-Mar Mar-18 63% WHL L 28-Mar Mar % WIL F 22-Mar Mar-18 17% TABLE 25: Telecommunications (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) BLU M 28-Mar Mar % HUG F 20-Mar Mar-18 76% MTN L 28-Mar Mar % TKG M 28-Mar Mar % TLM 6-Mar Mar-18 17% VOD L 28-Mar Mar % TABLE 26: Utilities (ICB Industry) Code Cap First Trade Last Trade % Traded # Points Beta(J203) Beta(J200) Beta(J250) Beta(J257) Beta(J258) IPS 22-Mar Sep-15 18% Equity Risk Service Q1/