Indexology^® Blog

Although there seems to be more research on economic forecast and market analysis than manager selection, selecting investment managers is just as challenging as direct investing and requires considerable experience and expertise. In this blog, we investigate the return distribution of fixed income and equity funds to highlight the challenge of successfully selecting outperforming active managers. Specifically, we incorporate risk-reward analysis by calculating the ratio of the median of positive excess returns over the median of negative excess returns. This ratio provides an intuitive comparison between the magnitude of upside gains and downside losses from selecting outperforming versus underperforming active managers. All else equal, the common belief is that a ratio greater than or equal to two is desirable.

The underlying data for the analysis comes from our headline SPIVA^® U.S. Scorecard, which uses the University of Chicago’s Center for Research and Security Prices (CRSP) Survivor-Bias-Free US Mutual Fund Database, the only complete database of both active and liquidated or merged mutual funds. The analysis focused on funds’ return distribution, and therefore eliminated funds that had missing returns for the entire reporting period.

Exhibits 1 and 2 show 5-year and 10-year net excess return distributions for equity and fixed income managers by category. We calculated excess return as the net-of-fees return of each fund subtracting its relevant benchmark index return.

The following are the key highlights from the exhibits.

1. Equity managers had a harder time beating the benchmark than bond managers. The blue shade indicates the quartiles with positive net excess returns. For fixed income categories such as investment grade short and intermediate funds, global income funds, and municipal bond funds, more than half of the managers delivered positive excess returns. On the other hand, in most of the equity fund categories, except large-cap value funds, even top quartile managers were unable to outperform the benchmarks in both 5- and 10-year periods.
2. No equity funds were able to demonstrate a risk-reward ratio greater than one. The yellow shade highlights risk-reward ratios greater than two. California municipal debt funds stood out as the only fund category for which the median upside of selecting outperforming managers doubled the median downside of selecting underperforming managers for both 5- and 10-year returns. In contrast, no equity manager had a ratio greater than one.
3. Fixed income funds returns were more tightly clustered than equity funds. The interquartile range is a measure of statistical dispersion, calculated as the difference between the first and third quartile. In our case, it measures the range of alpha generated by the middle half of the managers within each fund category. The pink shades indicate that the interquartile range greater than 2% were more common for equity funds.

Consistent with prior research by S&P Dow Jones Indices,[1] we found that on a net-of-fees return basis, average managers did not outperform the benchmark over mid- to long-term horizons across all equity fund categories and many bond fund categories. Furthermore, the risk-reward analysis for picking outperforming managers versus underperforming ones demonstrated the challenge of manager selection and the expertise required to be successful in this field.

[1]   https://spindices.com/documents/spiva/spiva-us-year-end-2018.pdf

Passive investment is emerging as the more viable and favorable option over active investing. Recommendations for passive investing are supported by statistics. The S&P Dow Jones Indices SPIVA^® Around the World Report for year-end 2018 (see Exhibit 1) highlights the trend of benchmark indices beating active funds.

Exhibit 1: SPIVA Year-End 2018 Results

Source: S&P Dow Indices LLC. Data as of Dec. 31, 2018. Past performance is no guarantee of future results. Chart is provided for illustrative purposes.

In India’s significant growth economy, with lower inflation, higher GDP, and a robust financial market, there is recognition that the number of large-cap active funds underperforming their benchmark index is growing. The SPIVA India Year-End 2018 Scorecard revealed that 92% of large-cap funds underperformed the S&P BSE 100 over the 1-year period, 91% underperformed over the 3-year period, and over 50% underperformed over both the 5- and 10-year periods. The challenge for active fund managers to deliver superior returns is on the rise.

Source: S&P Dow Jones Indices LLC, Morningstar, and Association of Mutual Funds in India.  Data as of Dec. 31, 2018.  Past performance is no guarantee of future results.  Table is provided for illustrative purposes.

Investors are adopting new dynamic approaches to portfolio management in order to align themselves with targeted portfolio goals. Passive investment objectives are driven by strategy and risk/return profiles.

The passive strategies to select from are manifold: value, growth, fundamental, technical, dividend, indexing, tactical, core, satellite, etc. Asset allocation in the strategy seeks to balance risk and reward by assigning the ideal weights of assets in the portfolio to meet the requirement of the end objective. Strategies vary along a spectrum, from aggressive to conservative.

The selection made on the risk/reward spectrum determines the strategy. Reward, or return, is the change in the value of the investment measured in percentage or in absolute terms like appreciation in stock price or receipt of dividends. Risk is measured by standard deviation, which expresses the volatility of the stock or portfolio by variance from the average. If an investment portfolio has an average expected return of 12% with standard deviation at 5%, the range of returns is expected to be between 7% and 17%.

Modern Portfolio Theory (MPT) is a preferred strategic tool that uses diversification to select a group of assets that offer maximum return for a given level of risk. Harry M. Markowitz in 1952 famously explained the relationship of risk and reward through his article “Portfolio Selection” in the Journal of Finance. He became the pioneer of MPT, also referred to as the Markowitz model.

MPT assumes that investors are generally rational and risk averse. The time horizons for risk and return are the same. The views on risk measurement are identical for all investors and they control risk by diversifying their holdings. The investor prefers to either maximize his return for minimum risk or maximize his portfolio return for a given level of risk.

The theory compares two portfolios with the same return expectation, but one has lower risk than the other. MPT shows that the preference is for the portfolio with lower risk. MPT also clarifies that diversification reduces the portfolio risk. MPT supports the case for the passive investing. This style inherently provides a diverse basket of stocks, avoiding concentration risk and offering returns at the chosen risk level.

Standard deviation typically is used to help evaluate investment risk options. If an index offers a lower standard deviation than an active strategy, but with a comparable average return, the choice of preferable investment option should be easy.

A range of indices offer varied strategies across geographies, asset classes, themes, and factors. Index methodologies are transparent and rules based, staying true to style, unlike active management, which can use discretion in striving to achieve objectives. Passive investing is also generally lower in cost, owing to lower fund management fees and ease of trading, as the index constituents trade on the stock exchange like regular equity stocks.

The Indian passive investment industry is gaining strength with over USD 16 billion in assets. Core satellite strategy can facilitate the co-existence of both active and passive styles to meet portfolio goals.

S&P Dow Jones Indices produces a semi-annual report comparing the performance of active managers to their target indices or benchmarks. The report is referred to as the SPIVA Scorecard (SPIVA standing for S&P Indices Versus Active Managers).  So, what does the SPIVA Scorecard tell us about performance?  As illustrated in the table above, for any regional equity class and over any timeframe, it tells us that the overwhelming majority of Canadian active managers fail to beat their benchmarks.

This underperformance can be attributed to two main factors: 1) information efficiency and 2) high portfolio management fees. Historically, “stock pickers” were often able to beat their target indices.  This was accomplished through rigorous analysis of privileged financial information and interviews with senior management.  However, he advent of the Internet and new regulations with respect to selective disclosure has levelled the playing field.  Today critical corporate information is readily available to all investors without privilege and at little expense.  As a result, market pricing has become “efficient”.  There is very little that an active manager can determine about the investment prospects for a large cap stock that the market doesn’t already know. At the same time, active managers typically charge investment management fees in the 1% range.  Portfolio turnover is often 50% to 100% or more, in any given year, further raising investment costs and eating into overall portfolio returns.

The end result, as illustrated in the performance data, is that active management of large cap liquid equities is no longer effective.

While substantial literature exists on dividend investing in developed markets, there is little research on emerging market dividend strategies, in particular for Latin America. S&P Dow Jones Indices surveyed the emerging market dividend payers in 2014 and found that Latin America constituted about 19% of the total global dividend payers.[1] As such, Latin American markets are capable of supporting dividend-based strategies.

In countries like Brazil, Mexico, Chile, and Peru, benchmark providers already offer passive dividend indices as a way to measure performance and provide exposure to dividend-paying stocks. We now expand the investment concept to the Colombian market.

As a starting point, we analyzed the historical dividend payers using the S&P Colombia BMI as the underlying universe, based on 10 years of dividend payment history. We found that on average, 92% of the constituents paid dividends at least once during the trailing calendar year, translating to roughly 97% of the index market cap (see Exhibit 1).

When we looked at the total amount of dividends paid by GICS^® sectors (see Exhibit 2), Financials was the largest sector by absolute amount of dividends paid. This was not surprising, as the sector represented 48% of the universe’s floating market cap, on average.

The average dividend yield in Colombia over the trailing 10-year horizon was 2.84%,[2] and the top three dividend yield contributing sectors were Financials (1.4%), Energy (0.7%), and Utilities (0.2%, see Exhibit 3). While Financials had the biggest outsize contribution to yield in the past four years, its contribution actually varied when we went back further in history. Furthermore, we found that Energy used to contribute much more in previous years (2009-2014) compared with recent periods (2016-2018).

When we looked at the historical average number of dividend paying companies by sector, we saw that sectors such as Energy and Communication Services had only one security (see Exhibit 4). The Energy sector in particular raised concentration issues, as its sole constituent had high average dividend yield (5.8%).

With 16 companies paying dividends in 2018, dividend-based strategies in Colombia are possible. However, any construction of such strategies would need to consider potential single-stock and sector concentration issues.

[1]   https://spindices.com/documents/research/research-a-field-guide-to-emerging-market-dividends.pdf

[2]   The average dividend yield is calculated as a weighted average of the index weight and one-year trailing dividend yield.

The S&P/ASX Australian Index Series has played a significant role in characterizing the Australian equity market performance since its inception in April 2000. Since then, the S&P/ASX 200 has served as the foundation for benchmarks and index-based investing strategies from sectors to quality, value, and momentum factor approaches.

Following the launch of the S&P 500^® ESG Index, which uses the new S&P DJI ESG Scores,[1] S&P Dow Jones Indices is proud to announce the launch of the S&P/ASX 200 ESG Index.

In this blog, we will focus on the role the S&P/ASX 200 ESG Index provides as the ESG alternative to mainstream listed equities investing in the Australian market. Using the new S&P DJI ESG Scores and other ESG data to define the index’s eligible universe, the S&P/ASX 200 ESG Index targets 75% of the market capitalization of each GICS^® industry group in the S&P/ASX 200. The outcome is to provide an improved index ESG score relative to the benchmark, while providing similar risk and returns, allowing investors to easily put their investment beliefs into action.

The S&P ESG Indices target an improved ESG index profile by allocating to the best 75% of the GICS industry group market capitalization in each benchmark’s eligible universe by ESG score rank. The index’s eligible universe is defined as all benchmark constituents after the removal of companies:

• with specific involvement in the production or sales of tobacco or controversial weapons or low levels of compliance with the principles of the UN Global Compact or
• in the lowest 25% of S&P DJI ESG scores among their global GICS industry group peers.[2]

The remaining constituents find themselves market-capitalization-weighted in an index that has historically closely tracked its benchmark, while yielding an improved ESG profile. These indices are designed for investors wishing to integrate ESG factors into their investments without straying far from the benchmark’s risk and return profiles (see Exhibit 2), making them an appealing alternative to core domestic and global allocations.

Additional Resources on the S&P ESG Index Series

• FAQs: S&P ESG Index Series
• Steadman, Reid, and Perrone, Daniel. “The S&P 500^® ESG Index: Integrating Environmental, Social, and Governance Values into the Core.” S&P Dow Jones Indices. April 2019
• The S&P ESG Index Series Methodology

[1]   The S&P DJI ESG Scores are based on data gathered by SAM, a division of RobecoSAM, through SAM’s Corporate Sustainability Assessment (CSA).

[2]   The index also accounts for ESG controversies that arise in its companies via SAM’s Media and Stakeholder Analysis (MSA), which continually monitors media for potential ESG-related events that could challenge the ESG scores SAM had assigned to companies.