Indexology^® Blog

Mexico’s Finance Minister, Carlos Urzúa, abruptly resigned on July 9, 2019 over policy disagreements with the current government.[1] Market participants reacted negatively, generating volatility in the Mexican peso’s exchange rate, which fell 2.3% right after the announcement,[2] and the country’s equity market, and bringing a trial by fire for the S&P/BMV IPC Risk Weighted Index (MXN).

The S&P/BMV IPC Risk Weighted Indices aim to offer upside participation and downside protection, which was evident in their performance after the recent events in Mexico. While the total return of the flagship S&P/BMV IPC lost 1.77% in a single day, the low volatility strategy for Mexico, represented by the S&P/BMV IPC Risk Weighted Index (MXN), fell just 1%. The S&P/BMV IPC Risk Weighted Index (MXN)’s one-day outperformance of 77 bps showed a reversal in the indices’ month-to-date performance and reduced the difference in year-to-date returns (see Exhibit 1).

The bad news of this event is that market sentiment perceives instability in Mexico.

The good news is that this event proved the advantages a low volatility strategy can provide.

[1] NYTimes.com: Mexico’s Finance Minister Resigns, Rebuking the President’s Policies

[2] Mexico Peso Falls as Investors Fret Over Finance Minister’s Exit – Bloomberg

Much has been made during recent months of the renewed interest investors have shown in gold on the back of growing financial market turbulence, a plethora of geopolitical flashpoints, and a string of economic releases that have fallen short of expectations. But gold has not been the shiniest precious metal this year; back in January, palladium became the most expensive precious metal for the first time since 2002, and by July 8, 2019, it had reached USD 1,542 per troy ounce, a premium of almost USD 150 to gold. When compared to its sister metal, platinum, the performance of palladium has also been impressive, with the platinum/palladium ratio more than halving since the beginning of 2017.

Drivers of performance in the palladium market were a mix of broader, macro demand trends and commodity-specific supply constraints.

Approximately 80%  of palladium demand comes from the automotive industry. Its other uses include electronics, dentistry, and jewelry. As regulations on emissions have tightened, demand for palladium to be used in the catalytic converters of gasoline-powered vehicles has risen. Gasoline vehicles have also become more popular in the wake of a number well-publicized diesel emissions scandals (diesel-powered cars use platinum in their catalytic converters).

Palladium’s outperformance relative to platinum has rightly raised the question of substitution. While it may be theoretically possible for carmakers to switch from palladium to platinum, further technical advances and a reasonable lead time are likely required. It is also worth noting that even with a rally in price, the cost contribution of palladium in car manufacturing is low and may not warrant significant research and development resources, especially when the future of passenger travel may not revolve around the combustion engine.

Electric vehicles do not burn fuel and hence do not require catalytic converters; however, it is not clear how quickly the mass adoption of electric vehicle technology will occur and hence how long it will take for palladium’s largest end market to shrink and eventually disappear.

Palladium is a by-product of platinum and nickel mining and is primarily mined in South Africa and Russia, and both countries face a myriad of investment and production challenges. Palladium prices spiked in March 2019 when Russia’s Ministry of Industry and Trade announced it was considering a temporary ban on the export of precious metal scrap and tailings, while in South Africa, the world’s largest platinum miners are about to embark on a series of wage negotiations with unions, which in the past have led to lengthy mine strikes.

As a by-product, it is not just the prospects of the palladium price that are taken into consideration when a miner makes a short-term capital expenditure or longer-term investment decision.

One area where supply is expected to increase over the coming years is the secondary scrap market. As more and more end-of-life vehicles contain a significant amount of palladium in their auto catalysts, the level and sophistication of recycling will likely increase.

The S&P GSCI Palladium is up 32.8% YTD, making it one of the best-performing individual commodities during the first half of 2019. Its performance in the second half of the year and beyond will depend equally on the ongoing push for lower car emissions and the supply challenges of not being the primary product mined.

For corporate bond managers, credit analysis is a key step in the investment process, one that lays the foundation for their credit outlook and investment strategies. Credit analysis assesses bond issuers’ creditworthiness and evaluates their ability to make timely interest and principal payments. Credit analysis is critical in helping bond managers assess the state of the credit cycle we are in, select bonds of healthy credit quality, and avoid names that could potentially suffer large principal loss. The analysis includes qualitative and quantitative components. On the quantitative side, credit ratios that measure leverage, interest coverage, liquidity, and profitability allow for the evaluation of a bond issuer’s financial risk profile.

Can we incorporate credit analysis into a rules-based methodology to capture some of the alpha and/or mitigate credit risk generated by active portfolio management? Credit ratio analysis, being quantitative, offers such an opportunity. To do so, we propose a rules-based model that uses credit ratios to screen out the least creditworthy issuers, thereby constructing a corporate bond portfolio with strong credit quality. We acknowledge that ratio analysis is only one part of credit analysis, and though necessary, it is not sufficient to assess an issuer’s creditworthiness. Therefore, instead of actively selecting companies with healthy ratios, we seek to use credit ratios to screen out companies at the bottom of the ranks, indicating financial stress.

The methodology involves two steps. First, we construct a representative investable universe from the broad investment-grade and high-yield corporate bond universe, respectively, by applying criteria on bond size (minimum size of USD 750 million for investment grade and USD 400 million for high yield), maturity (2 years-10.5 years), and spread duration (greater than or equal to 1). In line with the objective of constructing a quality credit portfolio for the high-yield universe, we exclude bonds with a credit rating below “B-.” Second, we group bond issuers by sector (banks, non-bank financials, and non-financial corporates), and use a set of sector-specific credit ratios on leverage, interest coverage, liquidity, and profitability to rank issuers within sectors (see Exhibit 1).

For the applicable ratio within each sector, we calculate a trimmed z-score for every issuer and then standardize the scores within the sector. We rank issuers by the average of the standardized z-scores within their respective sector. The bottom 20% of issuers in each sector are then screened out. The remaining issuers are equally weighted, and bonds issued by the same issuer are equally weighted in the portfolio. The portfolio rebalances quarterly at the end of February, May, August, and November each year.

Exhibits 2 and 3 compare the back-tested performance of the credit strength portfolios with the underlying broad market indices. For investment-grade and high-yield bonds, credit strength portfolios reduced return volatility and improved risk-adjusted returns. The maximum drawdown was lower than the underlying universes during market downturns.

The back test shows that incorporating credit ratio analysis in a rules-based portfolio construction process resulted in potentially more desirable risk/return characteristics for investment-grade and high-yield corporate bonds. In our next blog, we will further explore the benefits of downside protection and sector diversification that the credit strength strategy offers.

The performance of U.S. equity factors during Q2 was lackluster, with most underperforming the S&P 500, as seen in Exhibit 1.  While Minimum Volatility and Low Volatility were notable exceptions, Value, Quality, High Beta, and Momentum all lagged the benchmark – in large part because of their tilt toward smaller companies.  Since most factor indices are not cap-weighted, their out- or under-performance tends to parallel that of the equal-weighted 500.

  Equal Weight is a particularly good illustration of the small-size effect, since it holds the same stocks as the cap-weighted S&P 500.  Exhibit 2’s factor exposure chart makes Equal Weight’s small cap tilt clear.  Given the outperformance of larger-cap stocks during the quarter, Equal Weight performance was understandably disadvantaged.

Exhibit 3 demonstrates that larger-cap stocks dominated within most sectors of the S&P 500, with a particularly noticeable effect in the Consumer Discretionary and Info Tech sectors.  Seven out of eleven equal weight sectors underperformed their cap-weighted counterparts.

The S&P 500 Pure Value Index provides a less direct example of the impact of large-cap performance. The key driver of Pure Value’s underperformance last quarter was stock selection, again primarily in the Info Tech and Consumer Discretionary sectors.

Active managers are not immune to these effects.  Our SPIVA database shows that active management tends to be particularly challenged in periods when the largest stocks outperform, and when Low Volatility outperforms.  If I were a betting woman, I would not bet on active manager outperformance when our next SPIVA report appears.

“Sustainable investing must go mainstream. Fortunately, the momentum is growing.” – Mark Carney

Mark Carney’s statement underpins the sentiment of the investment community, where environmental, social, and governance (ESG) considerations have entered the forefront of investors’ priorities.

Whether factor indices have ESG principles integrated or not, understanding a factor’s influence on ESG characteristics, such as the benefits of quality, can be advantageous.

Exhibit 1 presents a heat map visualizing the S&P Factor Indices’ ESG exposures. This can be interpreted as red representing poor performance, yellow as middle, and green as strong, relative to the other indices.

Using S&P DJI ESG Scores, ESG improvements from the parent index were calculated as a percentage of possible ESG improvement if the total index weight were to be placed in the highest ESG-scoring stock (hence, there is no data for the S&P 500^® as it is the benchmark for the factor indices analyzed). The S&P DJI ESG Scores are based on underlying data from SAM’s award-winning Corporate Sustainability Assessment combined with an S&P DJI methodology of how underlying data is treated and aggregated.[1]

The “ESG,” “E,” “S,” and “G” columns in Exhibit 1 refer to the improvement in the ESG, environmental, social, and governance levels of the S&P DJI ESG Scores, while the other columns refer to various Trucost carbon footprinting methods.

Exhibit 1 illustrates that factor exposures can have a strong influence on ESG scores.

Quality topped all factors, showing strong ESG performance, fossil fuel reserves aside. Fossil fuel reserves, however, are driven largely by one company, Occidental Petroleum—only four companies included in the S&P 500 have any fossil fuel reserves. The nature of this metric, with only 16 S&P 500 constituents having fossil fuel reserves recorded by Trucost (see Exhibit 2), means the figure is skewed by including just one high fossil fuel reserve company. On the S&P DJI ESG Score front, quality performs well, seeing an ESG performance increase almost as high as the S&P 500 ESG Index, albeit with a stock count of less than a third.

Alternately, the S&P 500 Low Volatility Index shows the poorest ESG characteristics, with a 29% ESG reduction, largely driven by a 41% environmental reduction, while also falling short on social and governance factors. These S&P DJI ESG Scores[2] are backed by poor carbon to value, carbon to revenue, and weighted average carbon intensity figures, which were the worst performers of the factor indices analyzed. The low carbon scores are unsurprising, considering the large number of Utilities companies with low volatility characteristics. Exhibit 3 illustrates the distribution of carbon intensities, heavily skewed as with the fossil fuel reserves.

A responsible investor may consider implementing a carbon reduction strategy for a low volatility index or combining ESG with low volatility to gain stronger ESG exposures while still capturing the low volatility risk premia. In the next blog, we’ll see what drives these scores, as well as how constant the sector allocations within factor indices are over time.

[1]  “S&P DJI ESG Scores FAQ,” S&P Dow Jones Indices.

[2]  “Index ESG Characteristics Explained,” S&P Dow Jones Indices.