With the 1st half of 2022 now behind us, we devote this post to a mid-year review of ETFs in a variety of stock sectors. We also highlight some recent research on sectors that have historically held up well during periods of high inflation, and the benefit of time horizon when investing in stocks. We hope you find this mid-year review helpful!
Record-breaking 1st half of 2022
According to this MarketWatch article, the S&P 500 recorded its steepest 1st-half year loss in over 50 years. But, remember that the S&P 500 is a broad-based index consisting of many different companies across a variety of industries. In fact, there are 11 sectors in the S&P 500, which in order of size (and an ETF to represent them) are:
Information Technology (XLK)
Health Care (XLV)
Financials (XLF)
Consumer Discretionary (XLY)
Communication Services (XTL)
Industrials (XLI)
Consumer Staples (XLP)
Energy (XLE)
Utilities (XLU)
Real Estate (IYR)
Materials (XLB)
Mid-year review of best and worst performing sector ETFs
The chart below sorts the total return for the 11 ETFs identified above for 2022. As can be seen here, the biggest gains were among the energy sector (XLE) and the worst in consumer discretionary (XLY). Over this same period, the S&P 500 total return, measured by the iShares Core S&P 500 ETF (ticker: IVV) was -19.2%. Also, note that the energy sector was the only ETF here that saw a positive return, which is not surprising given the war in Ukraine and its impact on supply in the energy sector.
Mid-year review of returns from 11 sector-ETFs in the S&P 500 Index
Where will stocks go from here and what to do about it?
Given the current high inflation rates, Derek Horstmeyer at George Mason University recently showed the following “inflation fighters” in his June 5th Wall Street Journal Article.
Best performing sectors during periods of high inflation. Source: Derek Horstmeyer
Of course, the most prudent course of action may be to simply do nothing based on this mid-year review. Given longer investment horizons, the stock market is less likely to suffer losses. Based on Bank of America research, the chart below supports this fact.
In our last post, we introduced a new calculator to help you forecast your retirement savings. Part of this introduction showed you how the uncertainty in the markets may affect your savings forecast. So here, we summarize the differences between the two simulation options available in our new retirement savings calculator: bootstrapping and geometric Brownian motion.
Simulation of asset prices helps manage savings risks. (The vertical axis is price. The horizontal axis is time.)
Why use simulation?
Simulation, or often termed “Monte Carlo” simulation, is a scientific method to model future uncertainty using a random number generator. In the case of our savings calculator, it models the uncertainty of annual stock and bond returns. By running many simulation trials, each trial can represent one of many possible outcomes for investment returns over your planning horizon. Then, you can see what risk you may be taking in assuming a more pessimistic or optimistic account balance at retirement. For example, using default inputs to our model, a retiree can expect their future tax-deferred account balance to be likely more than $629,047, but likely not more than $1,073,058. (These values are based on default 25th and 75th percentiles. Our calculator allows these levels to be adjusted.)
Simulation provides a range of possible account values and the risk associated with achieving them.
Bootstrapping
The two most common approaches to simulation are bootstrapping and geometric Brownian motion. Bootstrapping uses historical returns of stocks and bonds, and randomly samples from them for each trial to develop simulated returns. For our model, we reconstructed annual returns for an S&P 500 ETF and aggregate bond ETF from 1989 to 2021. We used the same methodology described by DiLellio (2018). Retirees benefit from using bootstrapping since it preserves the historical distribution of stock and bond returns, as well as the correlation of their returns. In particular, extreme market shocks, like the financial crisis of 2008-2009, the dot-com bubble burst of 2001, and the Covid-19 pandemic of 2020 are all included when simulation uses bootstrapping.
One approach to simulating future returns is termed bootstrapping, where we simulate returns by random selection from a set of historical returns. In our calculator, we use annual returns from an S&P 500 and aggregate bond index ETF from 1989 to 2021. This approach has the benefit that it accurately represents the past, including the large market corrections in the financial crisis of 2008-2009, the dot-com bubble bursting in 2001, and the global pandemic in 2020. You can read more about this simulation approach in this peer-reviewed research in DiLellio (2018).
Geometric Brownian Motion
However, what if the future isn’t entirely represented by the past? In this case, we can use the geometric Brownian motion (GBM) stochastic process to simulate future stock and bond prices. Why? Using a GBM permits you to dictate return behavior using a normal distribution of asset returns. This simulation approach gives the retiree complete control over future returns. And, the retiree can select volatility and correlations of stock and bond returns. Lastly, GBM is the foundation for the famous Black-Scholes Option pricing formula. Unfortunately, GBM does not capture extreme events well. The image below from DiLellio (2018) shows how the normal distribution does a fair job, but not a perfect one, of fitting stock and bond returns.
Daily return distribution of stock (top pane) and bond market (bottom pane) indices. Two normal distributions are also shown, with volatility estimates using historical returns from 1989 to 2017. Reducing the volatility appears to provide a slightly improved fit near the center of the distribution, but worsens the fit in the distribution tails. Source: DiLellio (2018) Risk and reward of fractionally leveraged ETFs in a stock/bond portfolio, 27 Financial Services Review.
So, which simulation approach is better?
The short answer is “it depends”. Like any mathematical model, they both have their own strengths and limitations. Fortunately, you can use either of these models to develop your savings plan. In fact, we hope you consider using both, to best understand the risk of achieving your savings goals!
ETFMathGuy is a subscription-based education service for investors interested in tax-efficient investing with ETFs
For 2022, we’ve decided that the cost to produce and maintain the free and premium portfolios was simply too high. We also recognized that, while these portfolios did exceed their objective in 2020, they did not in 2021. All premium subscribers will receive a pro-rated refund of their subscription payments shortly. In the meantime, free and premium subscribers can now access the final monthly portfolios, based on data through December 31, 2021.
Coming soon
So, after receiving very positive praise on our retirement calculator, we’ve decided to make improving it a priority. Also, thanks to significant feedback from individual investors and financial services professionals, below is a list of features we hope to provide in the near future:
Projection of retirement assets at beginning of retirement for pre-retiree planning
Optimized social security starting age for single or married couples
Roth conversions using either IRA or taxable account funds
Robustness checks with an automated sensitivity analysis for selectable uncertain variables
Risk assessment with simulation of uncertain stock market returns, life exptancy, after-tax income needs, and others
Real estate income and residual value
Support for Financial Independence, Retiree Early (FIRE)
Online storage of previous results for future reference
Of course, our retirement calculator already has many features discussed in the FAQ and listed at the top of the calculator. Also, if you are interested in greater details, you are welcome to download this whitepaper that we developed recently to describe the current model in greater depth.
We hope you have a wonderful 2022!
ETFMathGuy is a subscription-based education service for investors interested in tax-efficient investing with ETFs
Inflation hedging continues to be of great interest for investors large and small. In this post, we quantify some possible ways to combat inflation based on a recent article in the WSJ.
Historical Inflation Trend
Inflation is currently around 6%, well above the 2% rate seen recently. The chart below shows how most of this change occurred in 2021. This rate is well above the 2% long-term target set by the Federal Reserve. So, what are some options for investors in this current inflation climate?
Inflation is about 6% in late 2021
Treasury inflation-Protected Securities (TIPS)
TIPS are one of the most obvious places investors look for inflation hedging. The iShares TIPS Bond ETF (ticker: TIP), with over $30 billion in assets, is a popular option. This ETF has performed notably better than a broad bond benchmark, like the iShares Core U.S. Aggregate Bond ETF (ticker: AGG), as the chart below illustrates. Note that while TIP has slightly higher volatility than AGG, it performance in 2021 is noticably better. In fact, according to ETFReplay.com, the 2021 year-to-date return of TIP is 5.4%, versus -1.0% for AGG.
Commodities
There are certainly other options investors can consider. For example, investors often seek commodity investments when inflation rises. This recent study by Vanguard indicated that a 1% rise in inflation could produce a 7-9% rise in commodities. This estimate looks surprisingly accurate, as the ETF DBC (PowerShares DB Commodity Index) should be up 28-36% in 2021, given the inflation rate increase this year from 2% to 6%. In fact, DBC is up 32.7% in 2021, according to ETFReplay.com
Updated optimal portfolios
For subscribers of our ETF optimal portfolios, we encourage you to log in to see the latest updates. Note that, based on our latest backtesting, monthly portfolios change more quickly now to respond to market dynamics.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
There is good and bad news on the latest proposal for tax changes on investments. In this post, we summarize the latest in a developing set of changes to future taxes on long-term investment gains.
Good news on proposed tax changes
According to the WSJ, the House Ways and Means Committee will not raise taxes on long-term capital gains to over 40%, as proposed by the Biden administration. So, an ETF investor should hopefully not see their long-term capital gains tax nearly double by realizing them.
Unfortunately, the proposed tax changes can have a significant impact on the windfall resulting from the sale of a home or business. Home sellers do have an exemption, but these limits can easily be exceeded for those living in high cost of living areas. And, since some home sellers may be recently widowed, these individuals would be even more adversely affected. Recently widowed individuals will see their exemption cut in half as they can no longer file their tax returns as married. For business sellers who may have invested much of their nest egg into building their business, this additional tax could significantly reduce the after-tax value of their sale.
Updated optimal portfolios
For subscribers of our ETF optimal portfolios, we encourage you to log in to see the latest updates. Note that, based on our latest backtesting, monthly portfolios change more quickly now to respond to market dynamics.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
Tax alpha refers to the additional rate of return generated by making tax-efficient investment decisions. For retirees, we provide an optimal retirement income calculator that models the U.S. tax code and determines an optimal drawdown strategy. Here, we discuss a recent upgrade to this calculator that quantifies your potential retirement tax alpha using an optimal drawdown strategy.
Retirement tax alpha and your optimal retirement income strategy Photo by Nataliya Vaitkevich on Pexels.com
What is alpha?
In the investment world, the return not captured by the movement in the broad market is alpha. Thus, for many investors, it means a risk-less return. In fact, we’ve even talked about it before in the context of CAPM and its counterpart, beta. Alpha and beta provide portfolio statistics important for consideration by any investor.
What is tax alpha?
Tax alpha is a relatively new term and may differ based on the source. We like the following definition.
If “alpha” is the return generated by an advisor’s skill in picking and managing investments, then “tax alpha” protects that return and generates a boost by making sure that taxes don’t eat away more of a client’s wealth than absolutely necessary.”
In retirement, tax alpha focuses on tax-efficient drawdowns. In addition, the industry standard for retirement income drawdowns from taxable, tax-deferred, and tax-exempt accounts is the Common Rule. The image below shows a summary of the default case used in our optimal calculator, which compares its results with those from the Common Rule.
This last line (line 4) indicates the value of tax-alpha of 0.57%. That is, a retiree would need to generate pre-tax returns 0.57% higher using the Common Rule to generate the same after-tax inheritance for their heirs. Therefore, by making optimal drawdown decisions in retirement, a retiree can expect to increase their investment returns using the Common Rule. Interested in seeing the details of this example or inputting your own assumptions for retirement? If so, please try our free online calculator.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
Due to portfolio performance not meeting our recent expectations, we revisited our backtesting results from August 2018 and produced important new insights and portfolio construction enhancements. We discovered that a longer sample period, identified previously, no longer applied. The image below shows that a three-month sample period produced the best returns from January 2020 to August 27, 2021. Each point on this line plot represents annualized backtested performance for 19 monthly portfolios over this testing period.
Backtesting for 2021 to find the optimal sample period (months) for ETFMathGuy Portfolio Construction
What performance predictions occurred with this shorter sample period?
Using this shorter sample period, we produced the plot below of total return since January of 2020. We chose this time period to include the full pre and post-term effects of the coronavirus on the world economy. In addition, and based on subscriber feedback, we now exclude ETFs that issue K-1 tax forms to investors. We made this decision because these 22 ETFs had a marginal effect on backtested performance that used over 1,000 other ETFs that do not issue K-1s. We also increased our ETF filter threshold of median volume to improve liquidity for future portfolios that will likely have a higher turnover rate. The consequences of these decisions on backtested performance appear below.
Backtested Returns from 2020-2021 of the ETFMathGuy Optimal Portfolios
Future ETFMathGuy portfolios
Given the improvement potential identified from this updated backtesting for 2021, all portfolios published in September 2021 and later will follow these updated findings. This update for the September portfolios will likely indicate a significant change from the August portfolios. However, future monthly portfolios will change less significantly. So, we encourage subscribers to log in and see the September ETFMathGuy portfolios that are based on this evidence-based analysis.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
Greeting ETFMathGuy subscribers! This post is a reminder that the latest free and premium optimal portfolios are now available for your review. So, please log in and see how the latest market conditions have affected these ETF portfolios. To begin, we discuss value versus growth ETFs and recent trends in their returns.
Recent returns on value investing leveling off?
A few months ago, we wrote about how value-driven ETFs returned about 5% more in the first quarter than growth ETFs. Revisiting the returns of the ETFs IVV, VUG, and VTV for the first half of 2021 shows this gap has shrunk to 3% after growing to more than 10%. In fact, as the chart here shows, the value ETF is below its early May high, while the growth ETF appears to have begun a new upward trend.
The total return of value and growth ETFs in the first half of 2021. Source: www.ETFReplay.com
Is the relationship between value and growth ETFs typical?
The relationship between two variables can be directly measured using correlation which varies between 1 and -1. So, a correlation of 1 between two investment returns indicates their returns are identical. Traditionally, the correlation between value and growth investments was around 75%. However, as this Wall Street Journal article highlights, the current correlation between growth and value is now below 25%.
Source: Wall Street Journal, June 28, 2021, by James Mackintosh
Performance of the ETFMathGuy Premium Portfolios
Based on actual investment performance, the risk and return of the moderate and aggressive portfolios over the last 18 months appear below. Consequently, this period includes all of the calendar year 2020, and the first half of 2021.
Moderate
Aggressive
S&P 500 (IVV)
volatility (risk, annualized)
19.5%
22.5%
21.2%
total return
23.9%
32.7%
36.4%
Annualized risk and total return of the ETFMathGuy portfolios, 2020-2021 (18 months).
We will continue to update our ETFMathGuy portfolios with current market conditions using our updated backtesting calibration results. So, time will tell if value ETF investing continues to outperform growth ETF investing.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
Below are a few excerpts from my recent article published on the Pepperdine Business Blog. I hope you find these tips helpful when developing your retirement drawdown strategy, whether you are nearing or already in retirement.
Sources of Retirement Income
Your retirement income will differ in several ways from your working income. For many retirees in the U.S., social security will provide a “base” of income, and starts between ages 62 and 70. Since the U.S. government keeps track of life expectancy, it is not surprising that retirees will receive smaller monthly social security payments if they begin drawing down social security at a younger age. So, one drawn strategy could be to delaying social security until age 70, which can provide more income if the retiree expects to significantly outlive their peers.
Retirees often have many sources available to meet their retirement income needs.
To supplement social security, many retirees also employ a drawdown strategy to their taxable, tax-deferred (e.g. 401(k)s) and tax-exempt accounts (e.g. Roth IRAs). The Common Rule is the most common strategy, that begins by taking Required Minimum Distributions (RMDs). Then, the Common Rule draws funds from one account until no assets remain. This strategy then moves to the next account
Your heir’s tax rate
Extending portfolio longevity or increasing your heir’s inheritance requires some thought to your heir’s tax rate. Why? Tax-deferred accounts are more valuable when drawdowns occur at lower tax rates. So, if your heirs expect to have a high amount of taxable income, then it is usually more tax-efficient for you, the retiree, to draw down the tax-deferred account each year up to, but not exceeding, your heir’s tax rate. This drawdown strategy, which includes an assumption of your heir’s tax rate, is embodied in our free online calculator.
As a final note, thank you to all of our premium subscribers! You can now access the June optimal portfolios, based on data through May 28th, 2021. Please note we built these ETF portfolios using our latest backtesting results.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
Backtesting ETF portfolios is a very important part of validating any investment strategy that uses them. At ETFMathGuy, we backtest our optimal portfolio construction strategy periodically. Doing so ensures that our quantitative methodology stays calibrated to the highest performing portfolios. Here, we discuss the key findings from this recent analysis.
Backtesting methodology
Our backtesting methodology follows the same approach we used in our previous backtesting analysis. The key distinction now is our time period begins in 2014 and runs through April of 2021. Also, we focused on one-month holding periods this time. Why? Based on our previous results, we found holding periods between 1-3 months had little impact on returns.
Backtesting ETF results over a longer-term
Firstly, the chart below shows the result of changing the duration of the sampling period on the out-of-sample returns. Note that there are two local maximums, with the first occurring and the 6-9 months, but a second more substantial maximum occurring at about 39 and 45 months.
Annualized returns from backtesting differing sample sizes. Source: ETFMathGuy.com
However, when a risk-adjusted return is considered, we can improve this calibration. In the next figure, we show the annualized return divided by the annualized volatility. Thus, it’s clear that the 39 month sample period is superior with this measure for the moderate and aggressive portfolios. For the conservative portfolios, there is only a slight degradation in risk-adjusted return over these 7+ years of backtesting.
Risk-adjusted returns from backtesting differing sample sizes. Source: ETFMathGuy.com
Backtesting ETF results over a shorter term
We also backtested our quantitative strategy over a shorter interval of the last 15 months, from January 2020, through April 2021. Ideally, our backtesting results over the long-term, shown above, should agree with this shorter time frame. And, in fact, they generally do.
Annualized returns and risk-adjusted returns from backtesting differing sample sizes. Source: ETFMathGuy.com
Once again, with the slight exception of the conservative strategy, the 36-39 month sample size provided the largest annualized returns and risk-adjusted returns.
Key takeaways
Backtesting provides an estimate on how our quantitative strategy would have performed based on historical time periods.
The best calibration for the sample period occurs around 39 months based on both absolute return and risk-adjusted return.
Longer-term and shorter-term backtesting provided similar calibration results.
ETFMathGuy is a subscription-based education service for investors interested in using commission-free ETFs in efficient portfolios.
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