FolioBeyond Fixed Income Commentary For May 2020
Performance Summary
FolioBeyond’s core Fixed Income strategy, as calculated through the S-Network FolioBeyond Optimized Fixed Income Index ("SNFBFI"), returned +0.72% (net of 30bp annual fee assumption) in May versus +0.47% for the Bloomberg Barclays U.S. Aggregate Bond Index (“AGG”). Over longer time periods, SNFBFI, which is available to investors through Separately Managed Accounts and in a substantially similar version through the AdvisorShares ETF (ticker FWDB), continues to outperform both AGG and Morningstar’s US Fund Multisector Bond category as shown below for 5-year and 10-year holding periods. Risk levels in the option markets continue to ratchet down with continued Fed intervention and slow renormalization of the economy. Consequently, the algorithm has gradually increased risk in short duration High Yield Credit and rate duration primarily in US Agency bonds.
Source: Morningstar
* SNFBFI’s returns are net of underlying ETF fees and 30 bp assumed management fee. Although the information herein is believed to be reliable, FolioBeyond makes no representation or warranty as to its accuracy, and information and opinions reflected herein are subject to change at any time without notice. The past performance information presented herein is not a guarantee of future results.
** The Rank is calculated using the total return of the S-Network FolioBeyond Optimized Fixed Income Index ("SNFBFI"), ranked against the closing total returns of the open-end mutual funds in Morningstar's US Fund Multisector Bond Category. Percentile Rank is the total-return percentile rank of SBFBFI relative to all funds in the defined Morningstar Category. The highest (or most favorable) percentile rank is 1 and the lowest (or least favorable) percentile rank is 100.
The primary driver of returns in May was short duration High Yield Corporate Bonds, while other exposures were flattish to slightly positive. Given the backdrop of some distortions caused by Fed activity combined with uncertainly of the pace of economic recovery, FolioBeyond’s Fixed Income algorithm should continue to provide an unbiased, optimized portfolio allocation based on daily updates of value and risk measures. As implied volatility levels fall closer to historical norms, the model will be able to increase allocations further to sectors that have not participated meaningfully in the initial rebound of certain credit products. Additionally, the momentum model embedded into the algorithm should capture technical aspects of price movements in the context of the overall optimization process.
Highlight: Volatility Measures
The recent experience of extreme volatility has refocused attention on proper risk management. We want to highlight two aspects of risk measurement that merit discussion. The first is the computation of the commonly used standard deviation measure. This standard deviation measure reflects the annualized return volatility of an investment over certain time periods and attempts to capture the multitude of variables that contribute to price and income volatility over time. For any liquid instruments with a well-functioning secondary market, the underlying datapoints should be captured on a daily basis. A large part of the industry, however, still uses only monthly return data to compute standard deviations. The difference here is approximately 252 daily datapoints in a year versus 12 monthly return observations. The implications are obvious since monthly data will often smooth or mask intra-month volatility and, in some cases, actually overstate the volatility if the monthly datapoints happen to exhibit bigger drawdowns.
To illustrate the stark differences based on the two approaches, we compared the standard deviations in the table below for our SNFBFI Index in comparison to the AGG ETF and the underlying AGG Index. When we look at SNFBFI, the standard deviation numbers based on daily data are actually lower than if we used monthly data. On the other hand, the relationship is reversed for the AGG ETF and the AGG Index. On the AGG ETF, the standard deviation over the past 12 months based on daily returns skyrockets to 8% versus a more typical 3-4% range using monthly data.
Source: Morningstar for underlying data on SNFBFI, AGG ETF, AGG Index.
* SNFBFI’s returns are net of underlying ETF fees and 30 bp assumed management fee. Although the information herein is believed to be reliable, FolioBeyond makes no representation or warranty as to its accuracy, and information and opinions reflected herein are subject to change at any time without notice. The past performance information presented herein is not a guarantee of future results.
The second dynamic here is the accuracy of pricing data. SNFBFI is an ETF-only portfolio where the pricing reflects actual ETF market levels. The AGG Index, however, generally utilizes model marks from pricing services that were often significantly off-market during the volatile month of March. Consequently, the AGG ETF is expected to show greater volatility versus the AGG Index. However, the magnitude of the difference in realized daily volatility is still surprising.
The main takeaways here are the importance of measuring volatility risk more accurately by using daily data and the use of true market prices in the first place to generate daily return numbers. FolioBeyond’s process updates the underlying market data daily to recompute risk levels, with triggers set for executing a rebalancing of the portfolio when necessary. This disciplined process allows for a robust risk management process which should lead to better risk-adjusted returns over time.
Please contact us to explore how our low-cost, efficiently executed algorithmic strategy can fit into your Fixed Income bucket, or how the model can be customized to suit your specific investment needs.
