Indexology^® Blog

Despite three interest rate hikes, record issuance, and a total market size climbing toward the USD 1 trillion mark, the dominating theme for loans in 2017 has been the massive amount of repricings that have occurred throughout the year. As detailed in an earlier blog post, leveraged loans pay a two-part coupon—a market-driven base rate plus a contractual credit spread. The accommodative market conditions (i.e., high demand and limited supply) have allowed issuers of bank loans to renegotiate the fixed component of their interest payment via repricing.

Generally speaking, the appeal of leveraged loans in a rising rate environment is the floating nature of coupons—as interest rates increase, the base rate (typically 30-90 day LIBOR) also increases, providing market participants with a way to minimize interest rate risk while also generating extra income. As expected, both one-month and three-month LIBOR have increased 60-70 bps throughout the year and sat at 1.47% and 1.59%, respectively, as of Nov. 30, 2017 (see Exhibit 1).

As mentioned, market conditions have allowed issuers to reprice a staggering amount of loans throughout the year (see Exhibit 2). As of Nov. 30, 2017, a total of USD 520 billion of leveraged loans had been repriced in 2017. Perhaps more surprising is that this figure includes nearly USD 130 billion of facilities that have been repriced twice this year. Given the size of the market at the start of the year (USD 850 billion as per the S&P/LSTA Leveraged Loan Index), that translates to over 45% of the total market. The month of November saw USD 79 billion in repricings—the second highest ever, following the unprecedented USD 100 billion of repricings that occurred in January.

Of course, the repricing impact has taken a toll on yields. Typically, repricings result in a reduction of credit spreads from 50 to 150 bps. For the first 10 months of 2017, repricing activity averaged 85 bps. That number fell to 64 bps for November, as there was less opportunity for spread savings, given the large repricings that had already occurred earlier in the year. Over the course of 2017, repricings have dropped the average weighted credit spreads of the S&P/LSTA Leveraged Loan Index from L+460 to L+382 (see Exhibits 3 and 4).

So, have repricings run their course in the loan market and, if not, how much more room is there for deals to reprice further? That all depends on market technicals (i.e., supply-demand imbalance).

The current expectation is for continued interest rate hikes through 2018, which should keep demand up for floating-rate assets. Additionally, as long as institutional investors are sitting on cash, loan issuers should benefit. Given the choice between cash returns or a repricing, institutional investors will continue to be accommodative to loan issuers.

As of Dec. 15, 2007, the S&P/LSTA U.S. Leveraged Loan 100 Index was up 3.07% YTD, while the broader S&P/LSTA Leveraged Loan Index had returned 3.88% (see Exhibit 5).

The discussions on the merits of carbon awareness investing are evolving and highlighting a desire to shift from the current data-driven carbon emission framework to a more sophisticated and risk analysis-driven, 2 degree pathway paradigm. The shift has been spurred largely by the Financial Stability Board (FSB) and recommendations from its Task Force on Climate-related Financial Disclosures (TCFD). At the Responsible Investor Americas Conference in early December, the implementation and the impact of the TCFD recommendations were a heavily discussed topic, as they could prove to be a game-changer in carbon awareness investing.

The TCFD was created in 2015 by the G20 Finance Ministers and the Central Bank Governors, headed by Michael Bloomberg and given the goal to create a climate-related disclosure framework that will aid the three major groups who are involved in understanding climate-related financial risks—corporations, investors, and regulators. Its report was released in June 2017 and is expected to prove instrumental in laying the groundwork and framework to help market participants understand the risks and opportunities posed by the transition to a low-carbon economy.

TCFD Recommendations

The TCFD recommendations call for greater climate-related disclosure and information in a corporation’s governance, strategy, and risk management processes and the implementation of standardized metrics plus targets. The materiality principle assessment is encouraged in the strategy and metrics and targets sections. The TCFD’s overarching goal was to focus on the financial impact of climate-related risks and opportunities on an organization rather than the impact of an organization on the environment (Exhibit 1). This latter effort is synonymous with impact measurement, which is an outward assessment aiming to quantify the impact of a company in regard to its environmental consequences. As discussed in my previous blog, the endeavor itself is honorable but presents a host of considerable challenges. Global companies have a multitude of businesses and can find themselves making numerous products—some that are deemed positive and others deemed negative. An attempt to arrive at an overall score that quantifies such an impact can be difficult, problematic, and unreliable at best.

TCFD Key Takeaways

As the TCFD’s report encourages companies to make disclosures, it is hoped that these disclosures will end up in mainstream integrated financial reports rather than in sustainability reports, whereby audit committees and senior executives will then get involved (Exhibit 2). In addition, the task force encouraged the use of scenario analysis, whereby corporations can use forward-looking information, with the 2 degree scenario as the common reference point. The TCFD realizes that widespread adoption of its recommendations is critical and the momentum is there, as evidenced by the recent EU High Level Expert Group on Sustainable Finance announcement that the TCFD recommendations will be integrated into EU policy. More than 100 companies with a combined market cap of approximately USD 3.5 trillion and financial institutions responsible for USD 25 trillion in assets have stated their support for the TCFD recommendations following their launch in July 2017.[1]

The TCFD says that asset owners have a crucial role to play in influencing better disclosure, as they hold the power of mobilizing assets in the investment value chain. The TCFD is sanguine that its recommendations implementation path will gain further momentum and envision the following milestones over the next five years, as climate-related risks and opportunities reporting takes hold.

[1]   TCFD Press Release, June 29, 2017, Final Recommendations of the Task Force on Climate-related Financial Disclosures (TCFD) Help Companies Disclose Climate-related Risks and Opportunities Efficiently and Effectively, available at https://www.fsb-tcfd.org/wp-content/uploads/2017/06/Press-Release-Final-TCFD-Recommendations-Report-Release-29-June-2017-FINAL-IMMEDIATE-RELEASE-UPDATED-SUPPORTERS-LINK.pdf.

In a recent study published in the Financial Analysts Journal, Ang, Madhavan, and Sobczyk (2017)[1] highlighted that using regression-based factor loadings to measure managers’ factor exposures, even when conducted on a rolling basis, can be misleading due to excessively smoothed coefficients, given that active managers adjust their exposures dynamically. The authors argued that holdings-based attribution offers more insight, and they used the actual holdings of mutual funds to analyze the contribution to active returns by factors.

With that in mind, in a previous post, we performed a holdings-based performance attribution to highlight that the improvement in relative performance of actively managed large-cap mutual funds stemmed entirely from the stock-selection effect (excess returns of 1.75%), demonstrating their abilities to pick winning stocks. In this follow-up post, we use the same holdings of the managers to take a closer look at their active factor exposures relative to the S&P 500 and their contribution to active return.[2] This risk-based attribution analysis complements our prior Brinson performance attribution analysis, providing a holistic picture on the sources of excess returns.[3]

During the 12-month trailing period ending June 30, 2017, large-cap active managers, on average, were underweight to value, dividend yield, and size factors, and they had higher positive tilt to beta, momentum, and leverage factors (see Exhibit 1). In addition, we observed lower negative tilt to price volatility and earnings variability factors within the same group of managers.

Earnings variability and leverage are measures often used as proxies for the quality factor. Taken together, large-cap managers were, therefore, more exposed to lower quality and higher volatility during the 12-month period studied. This generalized behavior comes as no surprise, as the equity market during the same period has been ripe for risk taking, with lower quality outperforming higher quality in the large-cap space.[4]

In terms of the impact of managers’ factor bets on their average active return, we can approximate that not all exposures have been handsomely compensated. For example, dividend yield, revenue/price, and size had negative monthly average returns to the factor, which has resulted in an overall positive factor impact (see Exhibit 3).[5] Beta[6] has also been a rewarding factor for those managers seeking broad market exposure. It has been the best-performing factor, and large-cap managers on average have been overweighting their beta exposure.

However, the average monthly return to other value factors such as book/price and earnings/price have been positive and the underweight to those factors have detracted from the average portfolio active return. Higher average exposure to volatility, momentum, and to earnings variability factors have also detracted from excess returns.

Taken all together, we now have a fuller picture and insight on what has worked for actively managed large-cap mutual funds and what hasn’t over the 12-month period studied. Judging by the longer-term historical SPIVA^® results, large-cap U.S equity remains a challenging asset class for an average active manager to beat and to do so consistently. It remains to be seen if the results we witnessed can be repeated in the future.

[1]   Ang, Andrew, A. Madhavan, and A. Sobczyk, “Estimating Time-Varying Factor Exposures,” Financial Analysts Journal. Volume 73 Number 4.

[2]   We use the Northfield US Fundamental Risk Model to estimate the managers’ factor exposures.

[3]   Brinson attribution and risk-based attribution are mathematically and conceptually different enough that they can potentially produce different results. For more illustrative examples, refer to https://insight.factset.com/brinson-and-risk-based-performance-attribution-disagree

[4]   Based on the total returns of the S&P 500 Quality Index (14.79%) versus the S&P 500 Quality – Lowest Quintile Index (17.44%) from June 30, 2016, to June 30, 2017.

[5]   Factor impact is reported as the geometrically compounded return of monthly average active exposure * monthly factor return.

[6]   Beta here refers to the CAPM beta, where the market is represented by the general Northfield universe comprising approximately 5,000 U.S. companies.

As we rapidly approach Christmas and New Year (not to mention year-end evaluations) it seems fitting to reflect on what happened in 2017.  We will publish our regional month-end dashboards on December 29 (interested parties may sign up here).  Before then, here are a few highlights using data as of December 15.

Volatility was historically low this year.

There was unusually low volatility in U.S. equities, with daily volatility in the S&P 500® in 2017 being lower than in any other year since 1965.  Meanwhile, the volatility in the S&P Europe 350 was lower than in any full year since the index launched in 1998.

Underlying the dampened risk environment was a series of political risks that failed to manifest: victories for the favorite candidates in Dutch, French, German and Japanese elections contrasted with the surprises of 2016.  Diversification also played a part, as ultra-low stock-to-stock correlations meant winners and losers among individual stocks balanced each other out in the benchmark’s performance.  Market participants also seemed intensely relaxed about the expected impact of anticipated news-flow on S&P 500 constituents, as seen in VIX®.  So far this year, VIX recorded 48 of the lowest ever 57 closing VIX levels, as well as two new all-time low closing levels.  This environment helped the S&P 500 VIX Short Term Futures Inverse Daily Index to a 187.10% year-to-date total return.

Exhibit 1: Average 30-day realized volatility in the S&P 500 for each year since 1957.

Source: S&P Dow Jones Indices. Data from March 4, 1957 to December 15, 2017.  Past performance is no guarantee of future results.  Chart is provided for illustrative purposes.

The U.K. equity market was impacted by the relative value of the Pound Sterling.

The U.K.’s currency acted as a barometer for investors’ confidence in the ability of the U.K. Government to negotiate its exit from the European Union.  The weak and wobbly value of the pound that followed Prime Minister Theresa May’s surprise decision to call a General Election, and the subsequent loss of her party’s majority, helped the S&P United Kingdom BMI (denominated in pounds) to outperform its euro denominated counterpart.  There was a turnaround towards the end of the year, though, when the Bank of England’s decision to raise interest rates for the first time in a decade lifted sterling against the euro.

Exhibit 2: cumulative total returns to the S&P United Kingdom in euros and pounds.

Source: S&P Dow Jones Indices.  Data from December 30, 2016 to December 15, 2017.  Index levels rebased at December 30, 2016.  Past performance is no guarantee of future results.  Chart is provided for illustrative purposes.

Momentum managed to identify the trends.

With the low volatility environment and an upward trend in U.S. equities, the S&P 500 Momentum Index was the best returning S&P 500 “smart beta” strategy in 2017; as of December 15 it had gained 28.68%.  Elsewhere, there was a 47.04% year-to-date rise in the S&P BSE Momentum Index while momentum even performed well in Japanese equities; the S&P Momentum Japan LargeMidCap increased 22.85% since the end of 2016.

And finally…

2017 was quite the year for the S&P/NZX Farmer’s Weekly Agriculture Equity Investable Index; it rose 72.06% to make it the best performing index we report on in any of our regional dashboards.  Among the worst performers on our regional dashboards is the S&P/TSX Capped Energy (in US dollars) which declined 16.85% year-to-date.

The Fed raised rates for the third time this year, bringing the benchmark Fed Fund Target Rate to 1.25%-1.50%, as expected. U.S. Treasuries outperformed Japanese sovereign bonds in 2017, while U.S. Treasuries’ volatility also came down to one-half of the previous level. As of Dec. 13, 2017, the S&P U.S. Treasury Bond Current 10-Year Index rose 2.70% YTD, outperforming the S&P Current 10-Year Japan Sovereign Bond Index by 233 bps.

The outperformance of U.S. Treasuries this year reversed the previous trend, wherein Japanese sovereign bonds delivered higher risk-adjusted return in three- and five-year timeframes due to the better returns and lower volatility (see Exhibit 1).

While the yield of the S&P Current 10-Year Japan Sovereign Bond Index continued to hover around zero, the yields of U.S. Treasuries were trending higher this quarter on the back of the rising-interest-rate environment. The historical yield spread of the two indices widened from 1.72% to 2.32% over the past three years (see Exhibit 2).

The yield pick-up offered Japanese investors an incentive to buy U.S. Treasury bonds, which is in addition to the portfolio diversification benefit. Subject to their investment view on the currency, market participants could decide to hedge the currency exposure or not. Either way, they may be exposed to extra returns or a reduction in returns that can result from hedging or the performance of the foreign currency.

As an example, the S&P U.S. Treasury Bond 7-10 Year Index is designed to measure the performance of the intermediate-term U.S. Treasury bonds. The S&P U.S. Treasury Bond 7-10 Year Index (TTM JPY) and the S&P U.S. Treasury Bond 7-10 Year Index (TTM JPY Hedged) both track the same bonds with returns represented in Japanese yen, while the latter is hedged in an effort to eliminate currency exposure through a one-month forward currency contract. As demonstrated in Exhibit 3, the hedged index closely tracked the underlying index, while the unhedged version was subject to currency exposure volatility.

Hence, aside from the portfolio diversification benefit and currency exposure, allocating to U.S. Treasuries this year offered better yields and total returns than Japanese sovereign bonds.

Exhibit 1: Risk/Return Comparison of the S&P Current 10-Year Japan Sovereign Bond Index and the S&P U.S. Treasury Bond Current 10-Year Index

Exhibit 2: Yield-to-Maturity Comparison of the S&P Current 10-Year Japan Sovereign Bond Index and the S&P U.S. Treasury Bond Current 10-Year Index

Exhibit 3: Comparison of S&P U.S. Treasury Bond 7-10 Year Indices’ Returns