WHITE PAPER A NEW WAY TO INVEST IN STOCKS By Koen Van de Maele, CFA and Sébastien Jallet
TABLE OF CONTENTS INTRODUCTION 2 STANDARD EQUITY INDICES 3 LOW-RISK INVESTING 4 QUALITY SCREENING 6 COMBINING LOW-RISK INVESTING WITH QUALITY SCREENING 9 CONCLUSION 2 ABOUT CANDRIAM 3 REFERENCES 4 INTRODUCTION The current low-yield environment in Europe is focusing the investor s attention on equities. Due to the European Central Bank s quantitative easing programme, rates are expected to first stabilise at low levels before starting to gradually rise. The expected return on bonds (government or corporate) is therefore fairly low. However, notwithstanding the higher expected return on the equity markets, the risk profile of these markets is obviously significantly different to that of the bond markets. As equity-market volatility is much higher than volatility in most of the credit markets, this paper presents a new approach to investing in stocks, with the focus on reducing the risk, especially when compared to standard equity investments. The demand for defensive equity strategies also is, significantly, still spurred by the negative performance of the equity markets following the 2008 financial crisis. The approach presented here combines the so-called low-risk anomaly with a quality approach. While the first concept is based mainly on historical market data, the quality approach uses forward-looking, fundamental company analysis. The purpose of the combined methodology is to create a portfolio with a lower beta than the broad equity market while matching its return via the creation of a positive alpha. As such, the portfolio will significantly deviate from the commonly used cap-weighted stock indices. The rest of this paper is organised as follows. Section assesses some undesirable characteristics of standard equity indices. Section 2 describes the low-risk anomaly. Section 3 decomposes the quality approach and section 4 reports the combination of both methodologies. Section 5 concludes. February 205 2
STANDARD EQUITY INDICES Frequently used equity indices, in which the stocks are weighted according to their (free-float adjusted) market capitalisation, bear some drawbacks and appear less efficient than is generally believed (Haugen and Baker [99, Grinold [992]). Firstly, due to an excessive concentration in the largest market-cap stocks, they do not efficiently diversify unrewarded specific risks. The effective number of stocks 2 for a European cap-weighted (CW) index is currently only 30 compared to a nominal number of around 430 stocks. Additionally, they provide limited access to other rewarded risks (such as size, momentum and value). Figures and 2 show that the CW indices have implicit biases towards expensive and large-cap stocks, thereby failing to capture value and small-size risk premiums (Fama and French [993]). The fact that CW indices are suboptimal is academically attributed to the disagreement among investors about risk and expected returns, the restrictions on short selling, the impact of taxes and the non-presence of some investment alternatives (such as human capital) in the target index. These weaknesses are not merely theoretical, but translate into poor risk-adjusted performance of those CW indices. The investment approach presented in this paper aims to overcome some of the flaws in the CW indices. FIGURE : Cap-Weighted Index tilt toward Expensive Stocks PRICE/BOOK QUINTILES AVERAGE WEIGHT IN CAP-WEIGHTED INDEX 25% 20% 5% This graph illustrates the overrepresentation of expensive stocks in CW indices. The European universe is split into 5 equal buckets according to companies Price-to-Book ratio. The average weight over time of each bucket in the cap-weighted index is shown. The figures (on the European universe, with quarterly updates) date from 992 and end in Q4 204. 0% 5% = High Price/Book 2 3 4 5 = Low Price/Book FIGURE 2: Cap-Weighted Index Concentration on Large-Cap Stocks MARKET CAPITALIZATION QUINTILES AVERAGE WEIGHT IN CAP-WEIGHTED INDEX 80% 60% 40% The graph illustrates the bias of CW indices towards companies with large capitalisations. The European universe is split into 5 equal buckets according to the companies market capitalisation. The average weight over time in the cap-weighted index is calculated for each quintile. The figures (on the European universe, with quarterly updates) date from 992 and end in Q4 204. 20% 0% = Large-Cap 2 3 4 5 = Small-Cap For the sake of simplicity, all minor corrections made by certain index providers to construct a more representative index in terms of sectors and countries are ignored in this paper since the conclusions remain unchanged. 2 Effective number of stocks is defined as the reciprocal of the Herfindahl Index, which, in turn, is defined as the sum of the squared weights of all portfolio constituents. February 205 3
LOW-RISK INVESTING In an efficient market, investors earn higher returns only if they are willing to bear higher risk. Despite the intuitive appeal of a positive risk-return relationship, this pattern has been surprisingly hard to find in historical data. For example, sorting European stocks by using measures of market beta or volatility shows just the opposite. Figure 3 shows that from 992 through 204 in the European equity market, portfolios of low-risk stocks had surprisingly higher average returns. A similar inverse relationship between risk and returns can be seen in the international developed equity markets and even in Treasury, credit, commodity and foreign exchange markets (Frazzini and Petersen [204]). This low-risk anomaly suggests a very basic form of market inefficiency. FIGURE 3: Outperformance of Low Risk RISK QUINTILES ANNUAL EXCESS VS CAP-WEIGHTED INDEX 3% 2% % 0% Low-risk equity portfolios outperform high-risk portfolios. The universe is split into 5 equal buckets according to stocks realized volatility over the last 2 months. Equal-weighted portfolios are constructed for each quintile. Figures (on the European universe, with a quarterly rebalancing) date from 992 and end in Q4 204. -% -2% -3% = Low Risk 2 3 4 5 = High Risk FIGURE 4: Low Risk vs High Risk EVOLUTION SINCE 992,400,400 Stocks with low historical volatility tend to outperform high-risk stocks. The graph compares the lowest realized volatility quintile with the highest realized volatility quintile.,200,000 800 600 400 200 0 992 994 996 998 2000 2002 2004 2006 2008 200 202 204 Lowest Risk Quintile Highest Risk Quintile Cap-weighted index February 205 4
The origin of this low-risk anomaly may be found in behavioural finance and the characteristics of the active fund management industry. As active fund managers are paid on their out performance against a benchmark, they tend to overweight risky stocks with higher beta and volatility in order to achieve higher returns. Volatile stocks are indeed much likelier to produce higher shortterm returns. As volatile stocks are more popular, they also become relatively over-valued and therefore produce lower long-term returns and do not deliver on their promise. A further justification of the low-risk anomaly is that many investors, such as individuals, pension funds and mutual funds, are constrained in the leverage they can take. Therefore they overweight risky securities instead of using leverage. For instance, many mutual fund families offer balanced funds in which the normal fund may invest around 70% in long-term bonds and 30% in stocks, whereas the aggressive funds invests 30% in bonds and 70% in stocks. If the normal fund is efficient, then an investor could leverage it and achieve a better trade-off between risk and expected return than the aggressive portfolio with a large tilt towards stocks. This behaviour of tilting towards high-risk assets illuminates why risky assets exhibit lower riskadjusted returns than low-risk assets. It is important to highlight that low-risk investing in stock markets results in substantial sector bets. However, low-risk investing delivers positive returns both as a sector-neutral strategy and as a pure bet across sectors (Baker, Bradley and Taliaferro [204]; Asness, Frazzini and Pedersen [203]). Figure 5 shows the results of a simulation similar to the one shown in figure 3, but executed on a sector-neutral level. FIGURE 5: Outperformance of Low Risk Sector Neutral RISK QUINTILES ANNUAL EXCESS VS CAP-WEIGHTED INDEX 2%.5% % 0.5% A sector-neutral portfolio with the lowest risky stocks per sector outperforms portfolios with the more risky stocks per sector. Each sector in the universe is split into 5 equal buckets according to the stocks realized volatility. Then the stocks of each quintile of each sector are combined on an equally weighted basis to build sector-neutral volatility quintiles. The figures (on the European universe, with a quarterly rebalancing) date from 992 and end in Q4 204. 0% -0.5% = Low Risk 2 3 4 5 = High Risk February 205 5
Figure 6 illustrates the recent evolution of the equity-style exposures of a low-risk equity portfolio based on Barra. Obviously, the portfolio is exposed to the low volatility factor. Additionally, by construction, each alternative weighting scheme (different from CW) has a small-size bias. With the exception of the short-value style, all other style exposures are close to neutral compared to the CW index. FIGURE 6: Low Risk Strategy Style Exposures Evolution vs CW Barra.2 0.8 0.6 0.4 0.2 0-0.2-0.4-0.6-0.8 2009 200 20 202 203 204 Evolution of Barra risk-index exposures calculated from the first quintile equally-weighted low risk portfolio. Low Volatility Small Size Momentum Quality (Low Financial Leverage) Growth Value, Barra Factor Indices Notwithstanding the fact that low-risk investing, when independently implemented, has entirely gained its credits, the approach is expected to become even more robust when combined with a fundamental quality screening process. After all, low-risk investing is mainly backward-looking, since historical risk characteristics will serve as the basis of expected risk. A fundamental quality screening process adds a forward-looking dimension into the investment process. QUALITY SCREENING Extending low-risk investing with quality screening is a matter of course. Risk has many dimensions, of which the quality of a company is obviously an important one. Companies that are profitable, growing, well-managed and exhibit low financial leverage are intuitively expected to be less risky. Although ranking stocks on the basis of a quality score may not be new, the practice gained in popularity in the early 2000s with the collapse of firms like Enron, whose market caps far exceeded their fundamental valuations. Joseph Piotroski s F-score, introduced in 2002, and Joel GreenBlatt s magic formula investing, which debuted in 2005, became popular tools for analysing companies financial health. Nevertheless, the definition of quality differs among asset managers and research firms. In this paper, quality is simulated using 3 dimensions: profitability, cash flow generation and financial leverage. Profitability is expressed in the return on equity (ROE) for financial companies and return on capital employed (ROCE) for non-financial companies. Both the level and the variation are taken into account. Cash Flow generation is based on the average change in operational cash flow (OCF) over 7 years. Financial leverage is based on a combination of the following factors: Net Debt/EBITDA, Net Debt/Assets, Equity/Assets and Tier ratio (for financial companies). February 205 6
Figures 7 and 8 show the longer-term returns from quality investing as defined in this paper. It turns out that high-quality stocks significantly outperform low-quality stocks. Literature shows that a strategy that goes long high-quality stocks and shorts low-quality stocks earns significantly higher risk-adjusted returns across many countries globally (Asness, Frazzini, Pedersen [203]). FIGURE 7: Outperformance of Quality QUALITY QUINTILES ANNUAL EXCESS VS CAP-WEIGHTED INDEX 4% 3% 2% High-quality stocks outperform low-quality stocks. The European universe is split into 5 equal buckets according to companies quality score. Equal-weighted portfolios are constructed for each quintile. The figures (on the European universe, with a quarterly rebalancing) date from 992 and end in Q4 204. % 0% -% -2% = High Quality 2 3 4 5 = Low Quality FIGURE 8: High Quality vs Low Quality EVOLUTION SINCE 992,400 The highest quality quintile outperforms the lowest quality quintile.,400,200,000 800 600 400 200 0 992 994 996 998 2000 2002 2004 2006 2008 200 202 204 Highest Quality Quintile Lowest Quality Quintile Cap-weighted index February 205 7
Figure 9 illustrates the recent evolution of the equity-style exposures of a high quality portfolio based on Barra. Unsurprisingly, the portfolio is exposed to the quality factor. High quality stocks also tend to be on average more expensive than the stock universe. This undesirable characteristic does not seem to prevent quality stocks from performing and shows that investors are willing to pay a premium to get higher quality. FIGURE 9: High Quality strategy Style Exposures Evolution vs CW Barra 0.8 0.6 0.4 0.2 0-0.2-0.4-0.6-0.8 2009 200 20 202 203 204 Evolution of Barra risk index exposures of the first quintile equally weighted quality portfolio. Low Volatility Small Size Momentum Quality (Low Financial Leverage) Growth Value, Barra Factor Indices It is important to highlight that, similar to the low-risk strategy, quality investing results in strong sector deviations compared to a CW index. However, the quality-effect remains intact in a context where sector strategies are neutralised, as illustrated in figure 0. FIGURE 0: Outperformance of Quality Sector Neutral QUALITY QUINTILES ANNUAL EXCESS VS CAP-WEIGHTED INDEX 3.5% 3% 2.5% 2%.5% % 0.5% 0% -0.5% -% -.5% -2% = High Quality 2 3 4 5 = Low Quality A sector-neutral portfolio with the highest quality stocks per sector outperforms portfolios with low-quality stocks per sector. Each sector in the universe is split into 5 equal buckets according to the stocks quality. Then the stocks of each quintile of each sector are combined on an equally weighted basis to build sector neutral quintiles. The figures (on the European universe, with a quarterly rebalancing) date from 992 and end in Q4 204. Please note that a quality strategy is very different from a standard value strategy. In essence, quality investors buy stocks based on quality characteristics irrespective of stock prices, while value investors buy irrespective of quality. February 205 8
COMBINING LOW-RISK INVESTING WITH QUALITY SCREENING Since both low-risk and quality investing yield positive returns, the combination of both approaches is expected to result in an even higher risk-adjusted performance. In fact, even if both approaches are positively rewarded in the long term, there is extensive evidence that they may each encounter prolonged periods of underperformance. More generally, the reward for exposure to low risk and quality has been shown to vary over time. But if this time variation in returns is not completely synchronized for the 2 approaches, combining them allows investors to diversify the sources of their outperformance and smooth their performance across market conditions. The approach presented in this paper is to filter the eligible European investment universe first for quality companies by eliminating 50% of the companies with the lowest quality score. Secondly, a low-risk portfolio is constructed by buying quality stocks that exhibit the lowest realized volatility over the last 2 months. The low-risk portfolio includes one-third of the eligible quality stocks. Hence, the final portfolio holds around 6.7% of the companies of the initial universe. In order to limit the effect of transactions costs, the portfolio is rebalanced only on a quarterly basis. Figures and 2 and Table show the longer-term characteristics of the combined portfolio. This portfolio produces a higher return and a lower risk than the CW index. Hence, it increases the Sharpe and Sortino Ratios. These ratios are also higher than both quality alone or low-risk alone. Also, the downside volatility, CVaR and maximum drawdown are reduced to a greater extent than in the CW index. FIGURE : Combination vs Equally-weighted and Cap-weighted FIGURE 2: Risk / Return characteristics EVOLUTION SINCE 992,600,400,200,000 800 600 400 200 0 992 994 996 998 2000 2002 2004 2006 2008 200 202 204 High Quality Low Risk Portfolio Equally-weighted index Cap-weighted index Annual Return 2.5% 2%.5% 0.5% 0% 9.5% 9% 8.5% Combination Low Risk Quality Cap-Weighted Equally-Weighted 8.5% 0.5% 2.5% 4.5% 6.5% 8.5% 20.5% 22.5% 24.5% Volatility The high quality-low risk portfolio is compared to a CW and equally weighted European equity index. Graphical illustration of the risk-return characteristics of the different portfolios. February 205 9
TABLE Low Risk (st quintile portfolio) Quality (st quintile portfolio) Combination of Quality and Low Risk (st tercile portfolio within 50% quality stocks) Cap-Weighted European stocks Equally Weighted European stocks Excess Return 3.0% 3.% 3.8% - 0.9% Annual Return.6%.7% 2.5% 8.7% 9.6% Volatility.4% 6.6%.5% 5.5% 7.3% Downside Volatility 8.9% 2.6% 8.7%.5% 3.% Beta 0.64.0 0.66.0.06 Tracking Error 7.9% 5.% 7.3% - 5.6% Active Share 74.9% 74.5% 73.5% - 52.0% CVaR -7.6% -.0% -7.5% -0.4% -2.0% Maximum Drawdown -43.4% -57.5% -42.0% -53.6% -60.9% Sharpe Ratio 0.69 0.49 0.77 0.32 0.34 Sortino Ratio 0.89 0.64.0 0.43 0.45 Information Ratio 0.37 0.60 0.52-0.7 Monthly return and risk characteristics of the different portfolios between 992 and 204 are shown on this table. Excess return, beta, tracking error, active share and information ratio are relative to the CW European equity index. February 205 0
Figure 3 illustrates the style exposures of the combined portfolio. Predictably, the combined portfolio is exposed to the Low Volatility and Quality style. However, there appears to be significant exposure to small-cap stocks too. In order to know whether the extra return from the combined portfolio can be attributed entirely to this small-cap effect, an equally weighted European universe, with similar small-cap exposure, is added to Table and to Figures and 2. The combined high quality low risk portfolio is then seen to unmistakably outperform the equally weighted universe, both in terms of return and of risk. This indicates that the excellent risk-return characteristics of the combined portfolio cannot be solely attributed to a small-cap bias. The combined high quality-low risk portfolio also exhibits a short-value bias. Hence, it appears that relatively expensive stocks are retained. However, although these stocks seem expensive when compared with the entire European stock universe, they appear less expensive when taking into account their intrinsic nature of showing high-quality company fundamentals and behaving in less volatile fashion. This defensive nature reduces the discount rate at which equity analysts derive a target stock price and hence justifies higher valuations. Furthermore, defensive stocks will have a steadier cash flow profile, and this, in severe economic downturns, reduces the need to raise extra capital. This element also validates a higher valuation than regular stocks. FIGURE 3: Combination strategy Style Exposures Evolution vs CW - Barra 0.8 0.6 0.4 0.2 0-0.2-0.4-0.6-0.8 2009 200 20 202 203 204 Evolution of Barra risk index exposures of the equally weighted high quality low-risk portfolio. Low Volatility Small Size Momentum Quality (Low Financial Leverage) Growth Value, Barra Factor Indices Despite the evidence that such a combination of low risk and quality has delivered excellent results in the past, the question may be asked as to whether similar returns can be repeated in the future. To study this, it is interesting to consider how the price of low risk and quality varies over time. To address this question, we run a cross-sectional rank correlation of stocks price-to-book ratio with their overall low-risk ranking. A similar cross-sectional rank correlation is run with the quality score. This rank correlation tests the extent to which low risk (or high quality) is associated with high prices in the cross section. Figure 4 shows the time series of the price of low risk and the price of quality respectively, which is simply the time series of the rank correlation coefficients. Asness, Frazzini and Pedersen [203] have shown with a cross-sectional regression that a low price in a certain factor predicts a high return of the factor in the subsequent periods. February 205
The rank correlations in Figure 4 indicate that quality stocks are relatively expensive over the entire period, leading to the conclusion that investors are willing to pay a premium to own quality. But higher prices for quality stocks do not affect their longterm performance. The relation between low risk and price-to-book is less stable. Unsurprisingly, low-risk stocks are most expensive in bear markets, when less risky companies are most attractive to investors. Low-risk stocks currently appear to be slightly above their long-term average price. FIGURE 4: Low Risk and High Quality vs High Price/Book RANK CORRELATIONS OVER 20 YEARS 0.5 0.4 0.3 0.2 0. Rank correlation between Price-to-Book ratio and volatility and quality over 22 years for the European universe. A high Low Risk (resp. High Quality) rank correlation means that the Low Risk (resp. High Quality) ranking is reflected in a high Price-to-Book ratio. Hence, the Low Risk (resp. High Quality) style can be said to be expensive. 0-0. -0.2-0.3 99 993 995 997 999 200 2003 2005 2007 2009 20 203 204 Quality Low risk Quality Average Low risk Average CONCLUSION An equity portfolio based on combining low-risk investing with a quality filter enables investors to benefit from a lower absolute risk while retaining the potential to match the return of the broad equity market. In essence, the combined portfolio is expected to have a higher Sharpe or Sortino Ratio than other standard equity investments. Such a portfolio greatly differs from a pure market capitalisation weighted index. Hence, the disadvantages of CW indices are mostly avoided. This concept is interesting for investors that care most about the absolute risk-return characteristics of the portfolio irrespective of the market indices. Therefore, most interest comes from pension funds, insurance companies and retail and private investors. The approach illustrated in this paper can be further refined by adding a classical fundamental company analysis to the quality screening. Risk can be further reduced by including elements such as quality of the management, corporate governance and competitive positioning in the analysis. This makes the final portfolio even more robust and forward-looking. However, it should be clear that the suggested approach might underperform the broad CW equity markets for longer periods. Especially in periods when large-cap or low-quality stocks outperform, the combined approach will underperform. Also, through holding a lower beta (market exposure), the combined approach might underperform during a strong bull market. February 205 2
ABOUT CANDRIAM Candriam Investors Group has been investing in European and global equity markets on behalf of its clients for more than 25 years. Today, Candriam manages over EUR 3 billion in stocks, which is around 6% of its total assets under management of around 80 billion EUR (as at the end of December 204). Candriam offers a large choice of different investment approaches in equity management: fundamental European equity management, global quantitative equity management, global thematic equity strategies, Emerging market equity management, Sustainable and Responsible Investment equity management and indexed management. A strategy aiming for lower risk and higher quality Candriam Investors Group recently launched a new strategy based on the concept described in this paper. This strategy combines quality screening with low-risk implementation with the aim of reducing volatility and achieving a strong performance over the market cycle. The quality screening starts with scoring European companies on 5 criteria: financial leverage, profitability, underlying market dynamics, competitive advantage and quality of the management (including corporate governance). Next, a valuation and liquidity assessment will determine whether a stock meets the requirements of a quality stock. The final portfolio is constructed by seeking the optimal combination of quality stocks so that the overall volatility of the portfolio is minimised. A well-diversified portfolio is obtained by evenly spreading the risk over the portfolio holdings and preventing any important idiosyncratic risks. This new strategy is managed by a dedicated Fundamental European Equity investment team of specialists with 5 years of experience on average, working alongside an Investment Engineering team of 8 highly-qualified quantitative experts. February 205 3
REFERENCES Asness, Frazzini, Pedersen. 203. Quality Minus Junk, Working paper Asness, Frazzini, Pedersen. 203. Low-Risk Investing Without Industry Bets, Working paper Baker, Bradley, Taliaferro. 204. The Low-Risk Anomaly: A Decomposition into Micro an Macro Effects. Financial Analysits Journal 70(2) Frazzini, Pedersen. 204. Betting Against Beta. Journal of Financial Economics () Grinold. 992. Are Benchmark Porfolio Efficient? Journal of Portfolio Management 9() Harvey, Liu, Zhu. 203. And the Cross-Section of Expected Returns. Working paper, Duke University Haugen, Baker. 99. The efficient Market Inefficiencies of Capitalisation-Weighted Stock Portfolios. Journal of Portfolio Management 7(3) Hong, Sraer. 202. Speculative Betas, Working paper, Princeton University February 205 4
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