June of 2024 was a good month for financial markets. Leading the pack were (again) technology stocks, with the NASDAQ up 6% on the month. Close in second place were emerging market ex-China stocks, largely driven by India, Taiwan, and South Korea, all of which had large rallies in the month. The U.S. stock market also had a good month, with the S&P 500 up 3.6%. International stocks ex-U.S. did poorly, down 0.8% on the month, widening the already wide outperformance chasm (more below). U.S. bonds were up a bit on the month, while their international counterparts were down, representing a monthly divergence of close to 2%. Finally, utilities -- despite the recently-emerged narrative of AI boosting energy demand and even more recent evidence of this dynamic playing out -- had an abysmal month, down 5.5% and underperforming the NASDAQ by a whopping 11.5%!
Zooming in on the U.S.-international performance chasm, the next chart shows the total return (including dividend income) U.S. investors would have obtained by investing in U.S. stocks versus a broadly diversified portfolio of non-U.S. stocks over the last ten years. In this time period, U.S. stocks outperformed their global peers by around 9% per year. Even against this backdrop, June's return divergence of 4.4% still stands out as extreme.
The two competing hypotheses for the recent outperformance of U.S. stocks are that U.S. stocks may be very overvalued at this point or that the U.S. stock market is, in some structural sense, superior to the international peer set, and this outperformance is likely to persists into the future. Based on QuantStreet's current model forecasts, we are very much in the latter camp. Our view is that the U.S. -- along the dimensions of innovation, regulatory environment, and government policy -- is likely to outperform the rest of the world for the foreseeable future.
QuantStreet's performance in June of 2024 was strong, as all three our risk-targeted portfolios handily outperformed their mutual fund asset allocation benchmarks (details here). Part of the reason for this outperformance -- in June as well as in the preceding months -- has been our overweight in U.S. assets relative to the exposure of asset allocation mutual fund peers. Of course, things can change, but given today's information set, QuantStreet remains overweight U.S. markets relative to the rest of the world.
Portfolio for the month ahead
In financial markets, you win some and you lose some. If you win just a few more than you lose, that adds up to meaningful long-term outperformance. While we've been right about some things, our marginal portfolio reallocation at the start of June definitely has to be chalked up to the loss column. We reduced our NASDAQ position (right before its +6% return in June) and initiated new positions in energy and utilities (right before they were down 1.4% and 5.5% respectively). Our portfolios still did well in June (because of our NASDAQ and U.S. overweights, among other reasons), but the performance would have been even better had we just forgotten about the June rebalance (and we are not very likely to have done that).
For the month ahead, we cut our position in utilities in half. We still buy into the AI-energy demand thesis. Utility stocks are still relatively cheap on a valuation basis. And utilities are still diversifying for the rest of the portfolio. But our trend discipline forces us to reduce positions after such bad months. Should such underperformance continue, we will be forced to further reduce the position.
We initiated a small new position in an India ETF, based on a favorable model forecast, a good trend, and because India is a highly diversifying asset class for our portfolio. For a hint of the reason why India looks so compelling (and why the country's stock market has been so strong), take a look at this article from the FT which discusses how favorable the country's demographics are relative to the rest of the world. One negative has been a relative decline in the country's freedom index, though Modi's recent underwhelming electoral results may indicate voter pushback against some of his more controversial policies.
Rounding out the portfolio changes are a decrease in the NASDAQ allocation, though we retain a technology overweight, and an increase in the S&P 500 portfolio allocation, as the S&P 500 index remains an attractive risk asset both from the machine learning model and trend perspectives.
S&P 500 sector strategy
In response to a discussion we had with a prospective investor at the end of May, we used our existing machine learning forecasting and portfolio construction analytics to develop a sector-allocation strategy for the S&P 500 index. The idea is to start with the baseline weights of the 11 S&P 500 constituent sectors and to dynamically allocate across them based on our machine learning and trend signals. Our portfolio optimizer uses the return forecasts and lagged risk and correlation estimates to construct a portfolio on a monthly basis that (i) stays within earshot of the S&P 500 sector baseline allocation (see below) while (ii) overweighting and underweighting sectors based on our year-ahead return forecasts.
If you would like to see the backtest — to be taken with a grain of salt, as is true of all backtests — let us know. We have already rolled out this strategy for our subscription model portfolio clients at the start of July.
Working with QuantStreet
QuantStreet offers wealth planning, separately managed accounts, model portfolios and portfolio analytics, as well as consulting services to our clients. Our approach is systematic and data-driven, but also shaped by years of investing experience. If you are an existing client or if you are thinking about working with us, we'd love to hear from you. Please reach out to us at [email protected].
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