March 2022 Outlook — Fast Times
Photo by Takahiro Taguchi on Unsplash
Market movements seem inconsequential compared to the devastation in Ukraine. Given the circumstances, it feels strange to see life progressing mostly normally in the United States. People keep working, and people continue living. I can’t imagine what it’s like to be in Ukraine now, but I sincerely hope for the war to end soon and for Ukraine to maintain its hard-earned sovereignty.
In this post, I’ll start by recapping market performance in February before touching briefly on what the economic impacts of the war and sanctions may have on Ukraine and Russia. I’ll then discuss my updated forecasts before closing out by talking about my decision to add technical analysis to my portfolio construction process.
Looking back at February, it’s amazing to think the Winter Olympics occurred. I didn’t avidly follow the action, but I’m still shocked at how quickly I’ve basically forgotten the event happened. I suppose a nuclear power starting a war with its neighbor will do that though. Although I acknowledged that the markets may have been too sanguine about the potential of a war in my prior post, I was also too sanguine. I won’t fall prey to hindsight bias and say the outcome was obvious, but I should have paid greater attention to the large military buildup near the border that was reported at least days before the invasion if not earlier. Market performance in the month of February was mostly in-line with one might expect given the situation. US core fixed income was the strongest performer of the assets I track falling just over 1%, and US value was the second best falling by about 1.3%. The energy sector and crude oil were strong outperformers and played a part in carrying the S&P 500 Value Index higher. The Vanguard Global ex-US Real Estate, which has high exposure to England, Japan, Australia, and Hong Kong as well at other developed European countries, fared better than some may have expected – it was down about 2.1%. The MSCI EAFE Index was down a little less than 3%, and US Real Estate was down about 3.5%. The worst performers were US Growth and Emerging Market Equities, down 4.4% and 4.3% respectively. The tanking of Russian stocks was partially responsible for the poor performance of EM, and higher inflation and the potential for higher interest rates hampered US Growth.
It’s difficult to say exactly how much the Russian economy is facing in sanctions, but JP Morgan estimated that the sanctions will cause Russia’s GDP to decline 13% this year which would put it in line with the 1998 crisis1. There were multiple factors that played into Russia’s crisis in 1998, but the end result was a significantly devalued ruble and the Russian government defaulting on domestic debt. Based on a chart from CNBC2, the MOEX Russia Index declined about 44% from February 24th before recovering slightly on the 25th. Since then and to the date of this piece, however, stock trading has been suspended in Russia3.
The damage to Ukraine’s economy feels less relevant given the people of the nation are fighting to protect their independence, but the impact still ought to be considered. An article from The Guardian suggested a 60% decline in the economy is possible based on historical performance of other countries where war occurred4.
Transitioning to my expectations from here, most of my quantitative forecasts are lower than they were a month ago. My forecast for EAFE dropped from a slightly positive total return to a low-negative single-digit total return over the next six months. Sustained inflation in Europe along with a slight drop in the dollar were the primary drivers of my lower forecast. Surprisingly to me, while Russia was the European Union’s 5th largest trading partner in 2021, only 5.8% of the EU’s total trading was done with Russia7. While this is substantial, it’s less than I expected. The impact of a decline in trade with Russia, however, could be larger than the percentage drop since Russia provides the EU with significant amounts of fuel and mining products7, both of which may be of greater importance to the EU’s economy than their sales price. In Ukraine’s case, it was the EU’s 18th largest trading partner and accounted for 1.1% of the EU’s total trade8. I’m not sure how much a nation at war is able to export, but I imagine the amount is minimal. Other members of the EAFE index, namely Japan and Australia, may be less impacted by declining trade with Russia.
Moving to emerging markets, my EM forecast dropped substantially more. Previously, it was about barely positive, but the forecast is now between negative 5 and 0%. Russia itself was about 1.5% of the index5. Importantly, this number did not factor into my forecast, and I’m not absolutely sure how the indexes that previously held Russian equities will reinvest the capital, but my assumption is the funds won’t lose 1.5% of their value at once. The main drivers of the reduced forecast are a weaker dollar and sustained negative momentum.
Rounding out the international asset classes I track, I’m expecting international real estate to return between negative 4 and 0%. The asset class performed well in January, but volatility in some of its major geographic components and a weaker dollar drove the forecast down.
For real estate in the US, my forecast declined to the low single digits. This was mainly driven by higher yields in the medium and long parts of the yield curve. Additionally, high yield spreads widened out in February. These factors could lead to reduced demand from consumers who often use loans to fund real estate investments.
As a whole, I’m expecting US equities to slightly outperform US real estate. My return forecast has come down, and my expected spread between the growth and value indexes also tightened, but I still favor growth. Sustained inflation is driving the overall expected return down in spite of valuation multiples contracting. The value index, however, is likely less impacted by inflation, and the forecast for that asset class actually increased by about 40 basis points. It is the only asset class I track that looks slightly more attractive than it did last month.
Finally, my forecast for US Core Fixed Income declined to between negative 4 and 0%. Rising interest rates reduce the value of future coupon payments for fixed income securities that lack and inflation-adjustment component. If the war expands significantly from here, investors may flock to US fixed income as a safe-haven, but I’m not leaning on fixed income as a risk hedge.
That sums up my updated views for now. Before signing off, I’ll talk about some challenges I’m facing and why I’m opting to begin integrating technical analysis into my process.
Note on Technical Analysis
In the short time since I’ve started maintaining my investment strategy, the highlight has been by decision to underweight EAFE and EM while my worst call to date has been favoring growth over value. I stand by my decisions and the reasons supporting them, but I shouldn’t be so arrogant as to believe my process and the forecasts themselves can’t be improved. When I initially built my capital market expectations, I used a six-month horizon for the forecasts. The rationale behind this length was that while it may be possible to generate higher accuracy forecasts over longer horizons, longer-term forecasts effectively ignore path dependency. For example, if the 10-year forecast for emerging market equity returns is 50%, it’s unlikely that the return for the asset class every year will be about 5%. It’s far more likely that the variance between sub-periods will be great, and my objective was and continues to be capitalizing on that variance. To that end, I built econometric forecasting tools for shorter time periods, and I’ve learned two main lessons since I started.
The first is one of the documented weaknesses of econometric models – these models struggle to identify turning points in trends. Knowing this struggle exists and experiencing it, however, are different feelings. One practice in the industry is to utilize regime-shifting models. The implementation takes various forms, but the concept is that factors are used to classify markets as being in one regime over others. Depending on the current regime, different econometric models are used for forecasts. For my process, I neither built regime-detecting models nor alternative models to utilize within different regimes. Thankfully, some heuristic rules I used for risk management purposes prevented my models from blowing up (showing terrible returns).
The second challenge, which is related to the first, is that it is difficult if not impossible to know exactly when a model or any of the factors within the model begin losing predictive power. Some firms attempt to get ahead of this by studying alpha decay6. For numerous reasons, time-series models can lose predictive power over time, so correctly recognizing when a model has lost its power can substantially improve strategy performance. Unfortunately, in order to see evidence of a model underperforming, the users of the model have to endure periods of poor performance. Even then, the users have to decide whether these periods are temporary deviations or if they’re evidence of alpha decay. Terminating usage of the model could result in Type 1 error if the model in fact shows predictive power, and persisting with its usage could lead to Type 2 error if the model is in fact no longer predictive. This is not a decision to be made hastily.
In my view, the sample size of underperforming periods is not large enough to warrant an overhaul of any of the forecasts, but I won’t ignore the underperformance either. Rather than throw out the models and start from scratch, the first option I’m considering for performance improvement is technical analysis. Even though my horizon is already on the shorter side, the path an asset’s performance can take over six months is still highly variable. My intended use of technical analysis will be to opportunistically hedge positions for periods shorter than two months. My initial array of tools includes moving averages, trendlines, the relative strength indicator, and support/resistance levels. There is subjectivity to these tools, but they can quickly be calculated or drawn using many common research tools. Over time, I may add more tools and may even create a quantitative process to leverage this information. Combining technical analysis with my current process will be challenging given that the technical analysis I’m doing uses higher frequency data than my quantitative forecasts utilize, but that doesn’t mean there’s no way to blend the tools. For now, however, I’ll focus on building a process before worrying about automation. Going forward, I will do my best to comment on technicals if the numbers affect my forecasts, but given the frequent changes in these numbers, what isn’t significant one day could be significant the week after. Nonetheless, when I’m ready, I’ll share what I see and how I’m arriving at those views. It’s a privilege and a luxury to spend my early mornings and nights honing my investment skills instead of fighting for my freedom. Stay safe, stay sane, and happy learning.
Works Cited
1. https://www.reuters.com/world/europe/jpmorgan-shock-russian-gdp-will-be-akin-1998-crisis-2022-03-03/
6. https://ec.europa.eu/trade/policy/countries-and-regions/countries/russia/
7. https://ec.europa.eu/trade/policy/countries-and-regions/countries/ukraine/
8. https://ec.europa.eu/trade/policy/countries-and-regions/countries/ukraine/