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Date: 2022-12-31 14:28:41
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For my last two annual review pieces for 2020 and 2021, I used the term "crazy" to describe both years. I could easily use the term again for 2022, but here are some other relevant terms - insane, volatile, emotional roller coaster, geopolitically uncertain, etc. To keep it brief, and for the sake of tradition, let's stick with "crazy".
In this annual review piece, I will look at some of the research I've been working on over the year, some of the ideas I've been thinking about and haven't yet made it to a published piece, recap my overall performance and share some lessons learnt for the year.
As I noted in a recent piece, 2022 was a lean year for me in terms of published research, however I probably had more "work in progress" projects than any year previous.
To recap some of the pieces published this year:
Some other research unpublished on Seeking Alpha, but I wrote about more briefly on Twitter:
I have a few other research ideas on the go, and hope that some will be published early 2023.
There is another project I've been working on, but it is not quite in the same vein as the quantitative research I usually publish.
While investing in some of the net-nets described above, I noticed that some of them were making significant moves in a single day (50-200%!), only to return to their original price in the next few days (if not immediately the next day).
In looking into how to capture or predict these moves, I look upon "Episodic Pivots", as popularized by both traders Pradeep Bonde and Kristjan Kullamagi, aka Qullamagi. In quant investing or stock picking, you typically look for stocks before they make their big moves. In the "Episodic Pivot" strategy, you look at the stocks only after they've made their big move, as a sort of confirmation.
This is a much different approach to what I'm used to, relying almost exclusively on technical and price action, and is much closer to day trading than my current approach. It requires much more "screen time", constantly watching these positions as they move throughout the day, as they can rise very fast, and fall even faster. There are also other software platforms used for this type of analysis as well.
As appealing as these intraday returns are, they are very tricky (as you'll see below on my performance (or lack of) with this strategy), and the returns provided by my current method are quite satisfactory. Still, if this element of trading can be incorporated into my current strategy, I may still consider it.
The table below summarizes performance across my various strategies for 2022 (including 2021 for reference), and a brief description of each:
In summary, I was fortunate to achieve an overall return of 29% for 2022, while many major indices and benchmarks were in the red by double digits (SPY -19%, DJIA -9%, QQQ -33%).
All strategies beat their benchmarks, except for the SaaS strategy, which suffered a rapid -32% decline in January alone (after which all positions were sold to prevent any further losses).
For any new readers, my style of investing is entirely quantitative based, with a focus on small and microcaps in both the US and Canada (my home country). For each strategy that I use, I rank all stocks in a given universe based on several factors, and take the top 15-25 positions. I then rebalance (weekly/quarterly). All strategies are developed and backtested using Portfolio123. For an overview of my approach, please see here.
For reference, you can also refer to my previous 2020 and 2021 annual pieces.
The "all round" strategies for US and Canada (#1 & #2) provided some of the highest returns for the year, at 38% and 56% respectively.
These strategies include 50+ quantitative factors related to growth, value, quality, sentiment, technicals, and of course the smaller the better.
Conventional wisdom regarding quant strategies states that you should exclude both financials and utilities, as their fundamentals do not screen the same as for other industries (i.e. banks have many liabilities on their balance sheet, but this is not necessarily a bad thing; utilities are regulated). For Strategy #1, in the original design of this strategy, I decided to modify factors to include any financial related stocks. As it turns out, this strategy is currently 1/3 financials. This concentration may have benefited this strategy this year, as many other industries were hit hard. Stocks in this strategy are currently allocated:
This strategy also has a minor concentration in energy (unlike some of the other strategies with heavier energy tilt).
The standout winner of the year in strategy #1 was RCM. The strategy provided a buy signal in September of 2021, where return was relatively flat for months, until April of 2022 when the business really started delivering, and the market noticed.
On the other hand, the same strategy also found this loser, textbook "buy high sell low", HRTG:
It is typical for quant strategies to signal losers amongst the winners (provided there are more winners than losers), however HRTG was an extreme case here, something I'll look to try and improve in the ranking system and/or factors.
Strategy #2 uses mostly the same factors to #1 (but with different weights), using Canadian stocks. Note the very high concentration in energy (>40%):
This strategy is also more heavily weighted towards small-caps rather than microcaps.
Strategy #3 & #4 are inspired by William O'Neil's CAN SLIM strategy, with some variations and adapted into a purely quantitative strategy (one for the US, the other for Canada). Both strategies had similar performance in 2021 (60% & 82% respectively), however very different performance in 2022 (7% & 61%).
The Canadian version of this strategy did well at 61%, but is very highly tilted towards energy at nearly 75% of holdings:
The strategy does not specifically look for energy, but rather strength in an industry (which energy had for much of the year). When another industry shows strength, the strategy tends to pivot towards the new industry.
Where Strategy #4 based on CAN SLIM performed well in Canada, the equivalent strategy for the US did considerably poorer, we'll see below.
The strategies in this category did well relative to the broader market, but not as well as the strategies in the "Good" group.
Strategy #3 returned 7% for the year, and is the other strategy based on CAN SLIM.
This strategy did markedly better in 2021 (and 2020).
Looking at transactions for the year, several holdings for the year lost more than 50%. The standout loser was OTC:RBCN:
The strategy has a tilt towards high growth and momentum, which can be a double-edged sword. If the high growth and/or momentum is not sustainable, it can be mean reverting and the stock price can follow. This is what happened with RBCN, as shown from the buy and sell arrows, for a whopping loss of nearly 90%. Several other holdings were similar, but not quite to the degree as RBCN.
It is for this reason that I prefer a portfolio of 20-25 stocks per quantitative strategy (particularly in the realm of small and microcaps). If this strategy was 10 stocks or less, the impact of RBCN's loss would have been much more significant on overall performance.
Strategy #6 was originally implemented near the bottom of COVID specifically as a "crisis recovery" strategy (I wrote about this strategy several times during 2020, see here & here). This strategy is in Canadian stocks, I also had a US version during 2020 & 2021, which I sold positions in last year as the alpha began to reduce. The Canadian version (#6) on the other hand, kept plugging along, and with the high concentration in energy, I kept this strategy in play, and did not rebalance either.
This is another high energy focused strategy, with more than 50% of holdings in energy related industries:
I will likely dissolve this strategy in early 2023 and reduce overall energy exposure, and replace with another strategy.
In 2020 & early 2021, software-as-a-service (SaaS) stocks performed quite well. I developed a scorecard to assess the fundamentals of these businesses, called the Rule of 40 Scorecard and SaaS Scorecard to help find the true winners in this group. Strategy #7 used the SaaS Scorecard, and performed well the first half of 2021. Unfortunately SaaS stocks were some of the first to drop when the Fed started adopting their hawkish tone in late 2021. By end of January, the strategy was down more than 30% in the month alone, I capitulated and sold the holdings.
In hindsight, the scorecard looked mostly at fundamentals. If and when SaaS return and become favourable again, I will incorporate a stop-loss or trend following stop to limit this downside on these volatile stocks.
And finally we have Strategy #8, which is more a collection of various "experimental" strategies. Throughout the year, I used this smaller account to experiment with various new strategies, or variations of ones that I already have. The first strategy I experimented with of note is that of net-nets, as I covered briefly above. When I took positions, the majority of the net-nets were biotech, and the industry was experiencing its own bull market at the time. Many positions gained 50-200% in single days, but then came back to earth. I found that these types of biotech stocks are not meant to be held long term, but rather are better intended as day positions. That said, I find the biotech space fascinating, the reward/risk profile very appealing, and hope to develop a strategy to find potential winners here (not necessarily net-nets).
I also used this account to experiment with the day-trading strategy using Episodic Pivots, as described above. I took very small positions, but found the strategy very labourious, and more even more stressful than my usual method of investing/longer term trading. Like all investing/trading strategies, it is one thing to read about a strategy, or to even "paper trade", it is completely another story to trade it real-time, with your own capital, with your emotions and biases "live". This lesson was made very clear to me here, and at least for now, I'll focus my energies on my current style of investing/trading.
Short answer - I have no idea (if you exclude "uncertainty").
That said, my plan is to keep my all-weather strategies in play, while trying to troubleshoot some of the factors that may have resulted in the poor buy signals noted.
As for new strategies and ideas, I am excited about biotech, and look forward to sharing my learnings in the future on this exciting, but risky space.
Until then, I wish you all the best of luck in 2023. I consider myself an "objective permabull", trying to balance optimism with just enough objectivity and skepticism to not blindly see things rosier than they actually are. But, I do believe "there is always a bull market somewhere", you just need to find it. Let's get looking.
Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.
Original Source: https://seekingalpha.com/article/4567211-2022-in-review-research-recap-lessons-learnt%3Fsource%3Dcontent_type%253Areact%257Cfirst_level_url%253Ahome%257Csection%253Alatest_articles%257Csection_asset%253Alatest_articles%257Cline%253A1