After a strong November 2024, markets were generally down in December. The S&P 500 index was down 2.3%, while energy, small caps, value stocks, and REITs performed considerably worse. Nasdaq was a bright spot, up 0.5% in the month, as was the dollar and, despite dollar strength, commodities rallied as well.
Some of the price action in December represented a reversal of the November Trump trade. As the next chart shows, energy (XLE), value stocks (VTV), and small caps (IWM) all did well in November as market participants interpreted Trump's policy stances to be highly beneficial to the energy sector and small, domestically-focused firms. (See our discussion in last month's letter.) These sectors largely gave up their November gains in December.
Positioning for the month ahead and realizing tax losses
QuantStreet's portfolio allocation process considers two sets of return forecasting signals. First, we use the output of our machine learning, data-driven forecasting model. Second, as an acknowledgement that no model can capture all possible market influences, we also incorporate a trend signal, measured as the prior 12-month return of each asset class. Based on this trend metric, energy and REIT stocks looked very attractive as of the end of November. But after the December selloff, the trend in these sectors became considerably worse.
For our January rebalance we reduced our energy positions across our different risk-level portfolios (0.4% to 1.5% of the portfolios) and sold all REIT positions (0% to 2.1%) across all risk levels. This reallocation highlights a natural tendency of our investment process to harvest tax losses: when the trend turns, i.e., after a given investment has lost money, our portfolio process generally reduces this exposure and realizes capital losses. This is akin to outright tax-loss harvesting strategies but it happens automatically in QuantStreet's allocation process.
The other portfolio adjustments in January were a continuing reduction in our Nasdaq-100 exposure, which has been ongoing for the last several months, a switch from longer- to shorter-term Treasury exposure (based on the more attractive return-risk tradeoff for shorter-dated Treasuries), and an increased allocation to the S&P 500, communications, and financials (across most portfolios). While financials also had a bad performance in December, their trend and model signals are attractive relative to their anticipated risk level (in addition to which, Trump's deregulatory push and an expected resurgence in M&A bode well for the financial sector).
Our portfolios followed the broader market down in December, though our overall 2024 performance was strong in absolute and relative terms. You can see details about our performance here.
Changing the trend part of our signal
A well-known academic paper (Asness et al. 2013) which examines the momentum effect across different asset classes defines "momentum" as the asset class return over the prior year, but excluding the most recent month (e.g., on January 1st of 2025, momentum is defined as the asset class return from the start of 2024 to the end of November 2024, excluding the December return). The authors of the paper explain their modeling decision: "We skip the most recent month, which is standard in the momentum literature, to avoid the 1-month reversal in stock returns, which may be related to liquidity or microstructure issues." The original academic paper to document the momentum effect in stocks (Jegadeesh and Titman 1993) skipped the most recent week of returns for the same reason: "By skipping a week, we avoid some of the bid-ask spread, price pressure, and lagged reaction effects that underlie the evidence documented in Jegadeesh (1990) and Lehmann (1990)."
When Asness et al. (2013) say "liquidity or microstructure issues," they mean that extreme buying (selling) by a subset of investors leads dealers to have a dearth (abundance) of a given asset class, leading them to increase (decrease) prices until the order imbalance corrects over the subsequent month, at which point the price action partially reverses. Another potential channel for the effect might be that investors overreact to news in the short-term (one month) but underreact to news in the medium-term (one year).1
Without taking a stand on the underlying reason, our own experience at QuantStreet has been consistent with the short-term reversal effect. We have seen on multiple occasions a strong one month rally or selloff in an asset class partially reverse in the following month. The trend signal we have historically used in our portfolio allocation process has been the last year return (i.e., without skipping the most recent month). But in light of the evidence in the academic literature, and our own experience (see the above chart), starting this month, we have updated our trend signal to be the 12-1 (12 minus 1) month signal (i.e., the return over the prior year excluding the most recent month) instead of the last 12-month version.
In backtests (always to be taken with a grain of salt), the 12-1 month signal performs better than the last 12-month variant. Also, switching to the 12-1 signal is consistent with our recent finding that at the industry level 12-1 momentum works best. Part of our process at QuantStreet is to continually refine and improve our investment decision making. The modification to our trend signal reflects this philosophy. We will monitor the performance of the adjusted trend signal to ensure it is beneficial for our investors.
Working with QuantStreet
QuantStreet is a registered investment advisor. We offer 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, please reach out at [email protected].
Endnotes
1 Over even shorter time horizons, like several days, there is ample evidence of market underreaction to news.
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