German listings, testing new Portfolio123 APAC + EM microcaps, and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

The (New) Global Microcap Universe

Until now I only traded microcaps from US, Canada, UK and Europe by using the North Atlantic universe as starting point. In previous investigations, I often discussed my common universe rules for defining a microcap universe, filtering out:

• exchanges not available for IBKR-based retail investors,

• top stocks by market cap,

• stocks with tiny liquidity,

• stocks with tiny stock prices,

• OTC stocks and MLPs,

• financial stocks (for accounting reasons).

Of course, the exact values and thresholds are somewhat arbitrary. Usually, I also exclude utility stocks, sometimes even consumer staples. Minimum stock prices and liquidity needs in USD or EUR can highly depend on the underlying market structures and exchange rates, which make the defining of universal rules more difficult. For example, an Australian microcap trading below 0.10 AUD can be a totally fine “trading sardine” with sufficient liquidity, whereas I would never touch a 0.10 EUR German microcap.

Regarding the definition of exchanges tradable on IBKR, Portfolio123 user InmanRoshi did a great analysis here. The final universe screen I will use for my analyses looks as follows:

This gives us 14595 microcaps with primary exchange data to choose from. Not bad at all. The biggest stock, as of today, has a market cap of 1160 EUR and trades in China:

So how does this universe perform as a whole and how stable is it over time? For this, I translated the rules into a quarterly screen backtest:

Looks like a great starting point to find hidden gems. As you can see, the number of stocks in the screen nearly doubles from 2001 to today, likely originating from low international coverage in early years. I will explore this for different countries and regions in later sections of this post.

For now let’s stick with the naive full global universe and test two different multifactor rankings:

1. Portfolio123 Core Combination EW (Value + Mom + Growth + LowVol + Quality + Sentiment equal-weighted)

2. My personal VMFS composite (Value + Stock & Fundamental Momentum + Size)

For the modified core combination system, I copied the original and equal-weighted each component to remove biases. For details on the VMFS composite, check out my “Layers of Return” series.

For each screen backtest, we will buy 20 stocks, rebalanced monthly with 1% slippage and 3% rank tolerance. I will maintain this procedure in following sections (if not stated otherwise).

Core Combination EW:

VMFS:

Both approaches deliver strong returns, which is a great first sign. Of course, a lot of design questions are still open:

Should you trade China and EM and if so, how reliable is the data (investing in mainland China as foreigner via Stock Connect programs is comparatively young)? How does reconstitution without position rebalancing affect the performance. What are actual slippage costs? Should you limit country and sector exposure?

Those questions go far beyond a first beta test. Still, I want to have at least a look at some subregions in the following sections. I will show the backtest results as simple charts for each region and then I will summarize the findings in a final “Conclusions” section.

North Atlantic (Old Global)

All stocks:

CCEW:

VMFS:

Japan

All microcaps:

CCEW:

VMFS:

South Korea

All microcaps:

CCEW:

VMFS:

China & Hong Kong

All microcaps:

CCEW:

VMFS:

Singapore, Malaysia & Taiwan

All microcaps:

CCEW:

VMFS:

Australia & New Zealand

All microcaps:

CCEW:

VMFS:

Brazil & Mexico

All microcaps:

CCEW:

VMFS:

Middle East

All microcaps:

CCEW:

VMFS:

Conclusion

The following table summarizes the backtest results for each region:

From this, we can draw the following conclusions:

1. The new APAC data basically triples our microcap universe. If you exclude China, Hong Kong, the Middle East and EM, you still get access to over 6000 new microcaps (according to my definition).

2. Japan, Korea and China each are big enough to be a viable subregion for a standalone microcap strategy including ML training. If you pair Singapore, Malaysia and Taiwan microcaps, you get another viable universe with > 1000 microcaps in the same time zone and similar culture.

3. Australia, South America and Middle East are (in my opinion) too small for standalone microcap strategies, especially if you want to use ML. They also are hard to pair together due to the large difference in culture and time zone. The best practice likely would be to pair at least Australia with a Western universe.

4. All new Asian, EM and global microcap universes are performing well on a universe basis (way better than North Atalantic and Australian microcaps).

5. Both the Core Combination EW and the VMFS system lead to outperformance for each region. The VMFS system is superior in each case, with regard to both CAGR and Sharpe ratio. This suggests that value-momentum systems with additional size factor exposure beat equivalent strategies with quality and low volatility focus in the new regions.

While the results are promising, please consider that this is still a beta version. The risk of data errors, bugs and similar problems in this stage is moderate but definitely not zero.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

US microcap forensics, largecap recency bias, dynamic sell rules, crypto shorts and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Screens

Let’s first take a look at two different ways to define a US microcap universe. In each universe I will exclude the largest 25% of stocks by market cap, stocks with a daily liqudidity below $25,000, stocks with a stock price below $0.10, as well as OTC stocks and MLPs:

a) US primary listings only but including companies with non-US headquarter:

b) US primary listings with US headquarter:

Exclusively US-headquartered microcaps did way better as a whole, especially in Q2. But does the same hold if we use a simple multifactor overlay to pick a concentrated portfolio of stocks? For this, I use the Portfolio123 Core Combination ranking system which combines Value, Momentum, Growth, LowVol, Quality and Sentiment into one score. I will pick the best 20 stocks monthly with a 0.25% slippage:

a) US primary listings only but including companies with non-US headquarter:

b) US primary listings with US headquarter:

The picture flips. A mutifactor approach seems to work worse YTD on pure US microcaps. The key driver: greece-based shipping companies like , , , , etc.

I have those in my exUS and global universes, a design decision which shifted a lot of returns into those substrategies for me.

So let’s focus on US-headquartered companies only. How can we make it work?

Let’s start with some basic screens:

Operating Cashflow Per Share, Recent Quarter < 0:

Operating Cashflow Per Share, Recent Quarter > 0:

1-Year Sharpe Ratio > 0:

1-Year Sharpe Ratio < 0:

Beta3Y > 1:

Beta3Y < 1:

F-score > 6:

FCF Yield > 10%:

The results look pretty reasonable. Value works, F-score (Quality) works, Momentum works. Everything should be fine.

Not Everything is fine…

Everything is fine if you have a broad strategy. But some concentrated strategies with long-term proven track record are suffering YTD. We can simulate this by simply reducing the number of stocks down to 10 for the core combination strategy mentioned in the beginning:

Great long-term…

… but struggling YTD:

A struggling 20-stock strategy this year on Portfolio123 is the popular “Small & Microcap Focus”:

A way to “fix” it in 2026 was to exclude stocks with a 3-year beta > 1.5 (which did not affect prior returns):

The recent drawdown you see here also shows up in my ML strategy. Something happened in June/July and if you rebalanced a lot of trades in this period without controls for beta or sufficient diversification, some ranking systems led you into a costly trap. There is quite some start date sensitivity and timing luck going on as well.

Typical trades closed in 2026 present only in the original, which failed, are :

and $MDGX:

2026 Tickers only present in the low-beta version were either flat or worked, like :

… and :

Please note again that is actually headquartered in Canada!

A Portfolio123 screen which is actually killing it YTD is “Tiny Titans”. I tried to replicate the rule set as a simulated strategy (without slippage) to analyze the transactions. I once again enforced closing all positions at the end:

The rules are a mix of small, FCF-profitable, liquid, low PS, low analyst coverage, with threshold quality (Altman Z + FCF/Net Debt). For more info, check out the screen on Portfolio123.

So what were the main sector contributions to the return:

The model managed to catch a lot of popular 2026 themes:

Healthcare, electronics, semis, aerospace, mining. Frankly, all concepts my value+momentum heavy system did not catch this year. Going back to “Small & Microcap Focus”, this model especially missed out on semis:

I personally note for my ML system that missing a few big biotech winners earlier in the year (by to rigid value overlays) broke the strategy.

“Tiny Titans” only lookls at P/S valuation + FCF threshold profitability.

“Small & Microcap Focus” looks at forward earnings yield, FCF PEG, and more, making it much more valuation-restrictive. YTD that means missing out on key themes and instead buying into some value traps, earnings misses and momentum roundtrips.

In the end, the key lesson here is that you cannot judge a concentrated strategy based on one calendar year. Sometimes, design decisions which make sense long-term, don’t play out in the short term.

The best remedy against this problem is strategy diversification.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

US microcap m&a prediction, YTD design decision madness 2.0, and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Universe and Target

I filter down the US Primary universe by excluding non-US stocks, the largest 25% by market cap, pennystocks below $0.1, illiquid stocks with daily volume below $25,000, OTC stocks and MLPs. As of today, this leaves us with 2508 stocks of $3B market caps or lower.

I first screened the universe for stocks with future 52-week merger pending or past actions and currently no such markers. In a backtest to last year, that leaves you with an impressive return of 36% annually with rebalancing only once a year:

Of course, this is with perfect foresight. The question is, what are the best predictors for future acquisitions. So I created a dataset in the Portfolio123 AIFactor UI using the following target:

Eval(!(PendingCorpAct(#MERGER) OR PastCorpAct(#MERGER)) & FHist("PendingCorpAct(#MERGER) OR PastCorpAct(#MERGER) ", -52), Future%Chg_D(200) ,0) +Future%Chg_D(200)*0

Translated that means, if currently there is no merger pending or past flag for a ticker but their such a flag 52 weeks from now, then assign it’s future 200-day return as target. If the condition is not met, assign 0. This target combines actual future acquisions or mergers with 200-day return potential. The additional +Future%Chg_D(200)*0 term just makes sure that UI handles the lookahead prioperly, since it sometimes has problems with complex targets.

Then I simply load the dataset with 250 handpicked z-score normalized features from 2004 to 2025 with 100% feature analysis and check the feature stats. The feature analysis checks all features for their actual correlation to target via mutual information:

”Mutual information (MI) [1] between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency.

The function relies on nonparametric methods based on entropy estimation from k-nearest neighbors distances as described in [2] and [3]. Both methods are based on the idea originally proposed in [4].”

The best MI was found for the following features:

continued:

This somewhat seems to confirm a hypothesis of me (and I guess many academics and practitioners before me):

The probability of becoming a future acquisition and merger target rises if the underlying sector and industry are thriving.

You see a lot of industry/sector momentum feastures at the top of the list. Also industry/sector growth, EV/sales and stock buybacks seem to be a good sign. On an indivial stock feature basis, qulaity is key. Important factors to mention are the Piotrosky F-score, counts of profitable years, quick ratio and current ratio, altman z-score and common profitability metrics.

As opposed to those industry and quality features, common suspects like individual stock valuation or growth are rather weak predictors.

Here the best value features:

And here the best growth features:

This analysis is meant as an mental exercise and not to actually train an ML predictor. Still, I did the next step and tried:

You can find outperforming stocks by targeting future acquisitions or mergers with high return potential, but I would not recommend it. I could use the trained predictors now in a screener actually check the m&a hit rate, but that would go too far for this post. For me, an m&a announcement pushing one of my stocks 30% higher overnight is a cherry on top but not the primary goal. If you own attractive opportunities, you will make money over time, even if your stocks never get acquired. some would even argue that mergers and acquisitions are a a bad thing because they take the best companies permanently out of the microcap pool and leave you with a universe that gets junkier over time.

Judge for yourself!

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

US microcap factor interactions, YTD design decision madness, and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Universe, Ranks and Backtest Settings

First, I build a simple US microcap universe with a liquidity filter, excluding OTC stocks and penny stocks with a close below $0.1. From that prefiltered list, I select the 2500 smallest companies by market cap (as of today that means market caps smaller than $900 million). This procedure ensures that the universe size is stable over time while still excluding the most illiquid names.

For the screen backtests, I try to keep the multiple tests as comparable as possible. I don’t use any additional filters. Instead, I use ranking systems which create a combined equal weight score of included factors. For each ranking system, I pick exactly 20 stocks quarterly. A slippage of 1% is applied if the stocks get replaced at rebalancing to account for turnover costs. Backtests are performed from 2001 to today.

Single factors

In the following, I will list the backtests of the tested single factors. For this, I use the core style factor composites of Portfolio123. As size factor composite, I use a combination of market cap and liquidity ranks.

From the summary table, you see that in the last 25 years, only three factor strategies outperformed after slippage: simple size, value and sentiment.

A quarterly rebalanced equal-weight size factor in a microcap universe can work by buying low and selling high. It is the simplest rotation you can do and basically means that you trust no microcap rally and you bet on quick turnarounds. But the risk-adjusted return is meager and slippage assumptions are questionable at best.

The value factor is a better version of this thinking. Buy low, sell high and, more importantly, sell fast when there is a bad quarter fundamentally.

High sentiment implies that there is analyst coverage, underlying upgrades, earnings surprises and low short interest. This works wonders in microcaps as the market tends to price in changes much slower and way more reactionary instead of being anticipatory.

Growth and momentum mainly suffer from high noise-to-signal ratios and high turnover resulting in slippage. Quality usually looks good on paper but low debt often means too-junky-to-borrow and high profitability often mean-reverts or stems from one-time effects. Low volatility, a combination of low beta and actual price volatility, will just put you in freak names which don’t move like a stock or don’t move at all.

I would not recommend going with single-factor strategies. Nonetheless, below you see the backtest charts in detail:

Size:

Value:

Momentum:

Quality:

Growth:

LowVol:

Sentiment:

Two-Factor Systems

Let’s now check how these factors interact if combined. The following table summarizes the statistics for each two-factor combination strategy.

There are a lot of viable two-factor combinations. The best improvement by far vs. their own single factor counterparts can be seen for momentum and growth combined with other factors. The combination of those high-volatility signals with slower fundamental factors tames their turnover while creating a symbiosis that amplifies returns. But also other factors can be improved by adding a second factor:

Sentiment was hard to improve because it already is the strongest single factor in the test. Notable combinations that actually work well are Sentiment x Value and Sentiment x Quality. Those pairs work so well because Value and Quality can filter the hype stocks out of Sentiment and Sentiment can filter the traps out of Value and Quality. Why does Sentiment for example not work so well with Size? Because high-sentiment stocks are stocks with good analyst coverage and forcing low size on them works against that nature. As a result, you are often stuck with foul compromise, such as an illiquid microcap with a single analyst who isn’t reliable. This whole example might help you to understand multifactor systems and how to interpret them and the underlying interactions.

The child of sorrow in the analysis is once again low volatility. Low volatility and low beta in microcaps are fringe cases. As the analysis shows, they don’t even lower forward volatility and drawdowns that much. But they sure lower returns. It’s like driving with the handbrake on. Most other factors live from cyclicality and rotation. Low-Vol works actively against those drivers.

In the following, you find all backtest charts for tested two-factor systems.

Size x Value:

Size x Momentum:

Size x Quality:

Size x Growth:

Size x Low Volatility:

Size x Sentiment:

Value x Momentum:

Value x Quality:

Value x Growth:

Value x Low Volatility:

Value x Sentiment:

Momentum x Quality:

Momentum x Growth:

Momentum x Low Volatility:

Momentum x Sentiment:

Quality x Growth:

Quality x Low Volatility:

Quality x Sentiment:

Growth x Low Volatility:

Growth x Sentiment:

Low Volatility x Sentiment:

Conclusions for Complex Multifactor Systems

Summing up the findings, the following table shows the average two-factor system statistics including the underlying factor stated in each row:

If we now naively assume that our highest goal is raw return, a clear priority emerges for US microcaps:

Value > Sentiment > Size > Momentum > Growth > Quality > Low Volatility.

I could now continue to test all three-factor combinations, then all four-factor combinations and so on but that would break the mold. You can do such tests automatically and in simpler form for large test matrices with the P123 ranking system optimizer. But here I will just try some extrapolation:

The naive conclusion for multifactor systems with three or more factors would be that adding factors along the priority list will improve returns up to a maximum through positive factor interactions and diversification until the effect of adding more factors a) becomes more and more diminishing and b) the factors that are left are suboptimal and low-performing.

Inverting that logic, I tested the combination of all factors and then excluded low-priority factors step by step. The results are summarized here:

The peak raw return is actually already hit at value + sentiment + size. However, the risk metrics are considerably worse. For best Sharpe Ratio, I had to add back momentum, growth and quality. Coming back to the hypothesis above, the diminishing effect of adding more suboptimal factors (with the big exception of the low volatility factor) was less pronounced than expected but can have very positive effects for risk-adjusted returns.

If in doubt, combine as many independent judges as possible. Even adding back the low volatility factor is not completely useless, as it helps to lower drawdowns, likely by acting as a tie-breaker for more and less risky stocks with similar fundamentals. Also please consider that we used a very specific universe and backtest setup for this analysis. Rebalancing monthly with a rank tolerance, adding back largecaps, or weighting factors differently within the ranking system, might lead to different outcomes. To emphasize this, I redid the above table with weekly rebalancing and 5% rank tolerance, which is closer to my approach:

Below, you find all backtests with three or more factors and original quarterly rebalancing without rank tolerance.

All Factors:

Ex Low Volatility:

Ex Low Volatility and Quality:

Ex Low Volatility, Quality and Growth:

Ex Low Volatility, Quality, Growth and Momentum:

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

Portfolio Update, US underperformance and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

Subscribe now

Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Portfolio Performance and Holdings

By July 31, the portfolio gained +22.6% YTD and +8.3% MTD. It was a great month overall, but most importantly, the drawdown streak was finally broken after 5 months and the portfolio reached a new all-time high. So YTD, I am back to outperforming classic ETF benchmarks like the MSCI World (IWDA), the S&P 500 incl. dividends (SXR8) or a mix of the AlphaArchitect factor ETFs (AA).

Regarding current returns of my book in comparison to simulations back to 2004, I am currently sitting at the median for rolling 13-week returns. The current rolling 26-week returns are still near the bottom decile historically due to the March crash being included:

It still hasn’t been a full year for the current levered long-short book. Nonetheless, I like to look at the 12-month performance as a reality check since hard calendar cut-offs like in the YTD chart can distort the picture through seasonality. I am not a big believer in seasonality as a reliable signal, but I acknowledge that certain calendar months can shift the range of outcomes to one direction or at least introduce volatility for certain factors.

The current 12-month return of the account is 58.3%, including the strong August and September of 2025 (unlevered long-only era). The next 5 months will be very testing. The goal is to hold the rolling 50% mark, even with the strong second half of 2025 falling out of the equation (not that it is in my power but still):

If we look at the YTD and July returns by sub-strategy, we see the main drag recently came from the US with some costly reversals, for example in energy names, and some bad entries like . The main star of the show once again: the short strategy with +10.5% in July and +23% YTD. This is mainly lucky timing, as the model’s expected annual return is just around 5-8%. But more importantly, the negative market correlation in combination with frequent rebalancing allows me to lever my longs with less headaches. July also showed the importance of regional diversification in the longs:

The weekly risk statistics (weekly for sufficient sample size) of the long/short book since inception looks way better now. I used the FTSE Developed Markets ETF as benchmark for the analysis (good beta/correlation match and US longs/shorts canceling out). High alpha, low beta, raw outperformance, preserved Sharpe and Sortino ratios… What could you want more?

Checking the PortfolioAnalyst’s sector contribution analysis, most of the July returns came from being net short technology and net long energy and basic materials:

As of today, the portfolio allocation has shifted away from upstream energy and became more diversified. The highest allocations now are in industrials and basic materials.

Current total allocation (as of August 1, 2026) Long:

Current total allocation (as of August 1, 2026) Short:

US Long Small/Micro

Realized trades in July:

Energy Services of America Corp. (+31.4%)

Energy Services of America Corp. is a contractor and service company operating in water and wastewater pipelines, natural gas pipelines and stations, ancillary facilities, corrosion protection services, electrical/mechanical controls, and fire protection services, schools, local and state buildings, and small bridge projects.

This was a classic breakout trade that worked out well.

DHI Group, Inc. (+36.3%)

DHI Group, Inc. engages in the provision of artificial intelligence-powered software products, online tools, and talent acquisition services for technology-focused roles and employers hiring tech talent globally.

Another breakout trade that worked out just fine.

PMV Pharmaceuticals, Inc. (-1.6%)

PMV Pharmaceuticals, Inc. is a precision oncology company. The trade was a roundtrip with a perfect exit on recovery. The stock collapsed since.

Pangaea Logistics Solutions Ltd. (-8.9%)

Pangaea Logistics Solutions Ltd. engages in the provision of seaborne dry bulk logistics and transportation services to industrial customers worldwide. Like other dry bulk names in my book, this was mainly an Iran war trade on strong momentum with fundamental macro tailwinds. Unfortunately, this one didn’t work out. After flatlining, the stock was pushed out on rank drift due to better alternatives.

Ring Energy, Inc. (-28.5%)

Ring Energy, Inc. is an oil and gas exploration company, which engages in oil and natural gas acquisition, exploration, development, and production activities. Bought on strong momentum, I was late to the party in energy. Despite oil prices recovering in July, the recovery in many energy names was lackluster. The ticker was sold likely due to the loss of momentum.

Smith & Wesson Brands, Inc. (+6.2%)

Smith & Wesson Brands, Inc. is a well-known holding company, which engages in the manufacture, design, and provision of firearms. Classic momentum trade exited for a quick small profit.

Ranger Energy Services, Inc. (-3.9%)

Ranger Energy Services, Inc. serves as a provider of high specification mobile rig well services, cased hole wireline services, and ancillary services in the US oil and gas industry. Its services facilitate operations throughout the lifecycle of a well, including the completion, production, maintenance, intervention, workover and abandonment phases. The story is the same as for other energy names. I entered the energy trade late. This one was actually sold after earnings based on fundamentals. Still, a key feature once again was flatlining momentum.

Kolibri Global Energy, Inc. $KEI.TO (-5.0%)

Kolibri Global Energy, Inc. is an international energy company, which focuses on finding and exploiting energy projects in oil, gas, and clean energy. Same sector, same story. But this one is actually a US company listed in Canada.

Trading noise:

Tickers (-1.6%), (-5.1%), and (-0.1%) were entered and exited within a 2-week time frame with a small loss. They likely showed a noisy technical pattern that was not confirmed in the following days, resulting in the ML reversing its opinion quickly. This can happen, especially in volatile macro environments. There are ways to avoid this behavior, for example by using rank tolerances or other sell rules that scale with the holding period, but usually that also dilutes true signals. According to my experience so far, it is best to just accept the occasional noise and move on.

Conclusion: My strategy has trouble finding promising long-term winners recently and the whipsawing is still a problem.

ExUS Long Small/Micro

Realized trades in July:

Capricorn Energy Plc $CNE.L (+11.2%)

Capricorn Energy Plc engages in the exploration, development, and production of oil and gas properties, mainly in Egypt. An energy breakout trade that actually worked out, but only because it got an acquisition offer.

Cavvy Energy Ltd. $CVVY.TO (+22.5%)

Cavvy Energy Ltd. is another energy company engaging in the exploration and production of crude oil and natural gas. A clean momentum trade exited on a short-term rip. Somehow, the ExUS energy trades worked out better than anything in the US.

Calfrac Well Services Ltd. $CFW.TO (-5.8%)

Calfrac Well Services Ltd. engages in the provision of specialized oilfield services. Another flat energy trade, no harm done.

Global Long Small/Micro

Realized trades in July:

FNM SpA $FNM.MI (-2.7%)

FNM S.p.A. is an Italian holding company, which engages in providing sustainable mobility services, management and development of motorway and railway infrastructure, as well as in the renewable energy and logistics sectors. More noise than trade, nothing to see here.

Capricorn Energy Plc $CNE.L (+11.1%) and Calfrac Well Services Ltd. $CFW.TO (-3.7%) were part of both the ExUS and the Global subportfolio. For more info see above.

Global Short Large Caps

Realized trades in July:

Oklo Inc. (+46.4%)

Oklo, Inc. engages in the development of fission reactors. A classic 2025 hype stock, that peaked and is on a steady downward spiral since. The exit only happened because it fell through the market cap bottom of the shorting universe. The perfect short trade.

Bitmine Immersion Technologies Inc. (+34.9%)

Bitmine Immersion Technologies, Inc. is a technology company focused on acquiring, holding, and actively managing ETH as its primary treasury reserve asset. The poor man’s so to say. This was the second profitable trade in the name, once again sold after it fell out of the universe due to low market cap. The recent broad selloff in tech likely shifted the market cap landscape, so that it reentered my screen and entered a new third full-size short shortly after.

Summit Therapeutics Inc. (+13.1%)

Summit Therapeutics, Inc. is a biopharmaceutical company that focuses on the discovery, development, and commercialization of patient, physician, caregiver, and societal-friendly medicinal therapies intended to improve quality of life, increase potential duration of life, and resolve serious unmet medical needs (whatever that means). After a quick scare in April, the short paid off and was closed due to rank drift.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

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Price-to-book in microcaps, screens vs. ranking, trading LLC, new ATH, and the week.

Mentioned Excess Returns podcast episode: Link

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

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Simple Book-To-Market

As always, we have to define a universe first. I will take all primary US listings and filter out stocks with market caps above $500 million, prices below $0.10 and average 20-day daily dollar volume below $10,000.

Then we rank by book-to-market. Why not price-to-book? Because this way, negative-book stocks can distort the picture.

So how did book-to-market perform if we picked the 50 highest ranked US microcap stocks yearly since 2001? It sucked!

And believe me, shorter holding periods look even worse. So is book-to-market just a bad factor nowadays? Let’s check the return distribution across the full sort:

2000 - 2013:

2013 - 2026:

So, as you can see, the return distribution looks reasonable for most of the sort but returns totally collapse in the top ranks. This behavior got even worse in recent years. In the following sections, I will look for ways to fix this.

Price-To-Book Lower Bound

One way to make book-to-market great again is to embrace the sort but filter out stocks with a price-to-book < 0.5. Extremely low P/B ratios can indicate frauds, near-bankrupcy stocks, cash burners, or similar. So skipping the extremes can make sense. It also adds a component of “selling losers” if the P/B picture of a held stock deteriorates until the next rebalancing date.

This simple method improves the CAGR to 13.7% at market-like Sharpe ratio and beta. However, it’s a rather brute filter and might seem overfit to the past. But the concept behind each improvement of book-to-market as a signal is an attempt at answering the same question: Which high-BM stocks do I have to skip to outperform? A more elegant way to tackle this problem might be to use a different metric as a filter prior to ranking by book-to-market.

Profitability

One way is to check for a certain profitability. Earnings can be manipulated or unstable but operating cash flow margins and free cash flow margins might do the trick.

OCF margin > 0%:

OCF margin > 10%:

OCF margin > 20%:

FCF margin > 0%:

FCF margin > 10%:

FCF margin > 20%:

From the results, we can conclude that free cash flow profitability beats operating cash flow profitability as a filter, with CAGR ranging between 13.7% to 16.2% for FCF-filtered screens and 6.6% to 7.5% for OCF-filtered. It is also interesting that for both types, a simple “margin > 0” filter works best and returns deteriorate with increasing margin thresholds. This implies that high margins in low-PB stocks tend to mean-revert.

Momentum, Beta & 52-Week High

Another way to find low-PB stocks that will go up is to buy stocks that already go up. For that, we will set different Sharpe ratio requirements and check nearness to recent 52-week highs. We also check 3-year market beta to find out if buying low-PB stocks that differ from the market works.

Sharpe ratio > 0:

Sharpe ratio > 0.5:

Sharpe ratio > 1:

% from 52-week high < 10:

3Y Beta < 1:

All those principles obviously work really great. Sharpe ratio > 1 was a bit too restrictive during the GFC and resulted in picking less than 50 stocks. The clear winner is picking low-PB stocks at their 52-week high and holding them 12 months. Sounds counterintuitive, right? Well, “trading at an all-time low” does not mean “cheap” and “trading at an all-time high” does not mean “expensive”. Bottom-picking is a bad idea. Also for deep value stocks, as the screen with a filter “% from 52-week low < 10” shows:

Growth, Sentiment & Fundamental Improvement

The last category also checks for momentum but not in price but in fundamentals. As proxies, we will check for 12-month growth in sales and operating income, average analyst EPS estimate upgrades for the current fiscal year in the last 13 weeks and the Piotroski F-score, a barometer for quality improvements (description see here).

TTM sales growth > 0:

TTM operating income growth > 0:

Average analyst EPS estimate current fiscal year, current > 13 weeks ago:

Piotroski F-Score > 6:

All those are great indicators for outperformance, with analyst sentiment and F-score showing the best Sharpe ratio. Value traps are often an issue of hidden fundamental risks. A quality checklist or listening to analyst sentiment can help to uncover such problems.

Conclusion

Combining the best of those filters is too restrictive and doesn’t work that well compared to simply buying at the 52-week high. To really create a proper value strategy with guardrails, you will not get around using more flexible multifactor ranks which don’t rely on hard thresholds.

Book value can be a useful anchor but should never be used as a standalone signal. There are just better ways to measure value. In the post-covid gold rush, the metric had a revival due to cheap money supply and the sudden unlocking of many value traps. But in normal markets and bear markets, P/B is mediocre at best, even in US microcap land.

That said, pure book-to-market is still alive and well in ExUS markets. The following backtest shows the original pure BM 50-stock screen but in a Canada+Europe universe, resulting in a CAGR of 12.9%:

Ex-US microcaps are, by nature, less junky and more underfollowed. So if you want to stick with price-to-book as your go-to value metric, maybe look for stock picks outside the USA.

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This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

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German tax season, financial goals, momentum construction, play money, position sizing and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Static Sizing Concept

The way I run my strategies, I pick static target position sizes for buying new positions and I let the weight of an individual position run freely until a sell signal kicks in. In technical terms, that means I only do reconstitution without actual rebalancing. In comparison, common factor ETFs usually do both at once, for example once per quarter.

The advantage of my concept, which is the standard concept for strategy simulations on Portfolio123 by the way, is that it reduces the number of trades and it leans towards the concept of “letting your winners run”. The latter is especially powerful if you have a momentum-centric strategy. Frequent rebalancing of weights is more powerful if your system is based on mean reversion. One level above, in a book of uncorrelated substrategies and other assets, rebalancing is even mandatory because there is no layer of entry and exit signals, only holding. But for concentrated momentum-centric stock trading with mid-to-high turnover signals, the free-floating weights are often superior, especially after considering fees and slippage.

However, the concept also has 2 major disadvantages:

1. A strong winner can in theory become so big that it distorts your portfolio to have unreasonable amounts of idiosyncratic risk. This of course gets amplified if your starting portfolio was overconcentrated to begin with.

2. If a big winner or loser gets sold, the proceeds amount often deviates strongly from the target position size, so that you are now not able to enter the required number of new positions without leaving cash on the sidelines.

With static position sizing, there are essentially two levers you can pull to deal with those limitations: number of positions and position buy size tolerance. Of course another solution can be to switch to more complex dynamic weighting schemes but I won’t deal with this topic here.

Every design decision is a trade-off though. Increasing the number of positions substantially will reduce idiosyncratic risk overall and limit the max amount of cash on the sidelines but it will also dilute your factor exposures and long-term returns. Increasing the sizing tolerance makes cash usage more efficient but it will also add a random multiplier to the idiosyncratic risk of each position.

To visualize the effects on position size itself, the following table shows the maximum possible deviation from the target position size in percentage points as a function of position count (5 to 15) and buy size tolerance (10 to 50%):

The return tables in the following strategy sections will have the same layout. The showcased strategies are simulated long-term backtests of my live strategies, which are close to the 3 regional long strategies and the short strategy described in the “Layers of Return” series. For each backtest, I changed the number of positions between 5 and 15 stocks (because my substrategy standard is 10) and the buy sizing tolerance between 10% and 50%.

Please consider, that the goal of this exercise is not to cherry-pick the best setting. The goal is to explore the impacts of design decisions, especially regarding risk and return dispersion. A big return dispersion without reliable pattern could be a hint that the strategy is unstable and you should go back to the drawing board. So in a sense, this is also a robustness check, just like Boston Dynamics poking their robots with a stick.

If in doubt, meaning if there is no clear pattern with substantial tendencies for improvement, the best way is usually to stay with the most conservative option (in this case closer to the bottom left of the table) instead of the aggressive one (top right).

The “Combined Score” is calculated as 50*(SR/SR_max)+50*(RR/RR_max).

Long US Microcaps

In the US strategy, a clear pattern for extremes emerges. The most aggressive sizing concept with 5 stocks and 50% sizing tolerance resulted in raw returns of 55.9 % close to test averages but the worst Sharpe Ratio in the test with 1.24. On the other side, the most conservative concept with 15 stocks and 10 % sizing tolerance resulted in good Sharpe Ratio of 1.62 but the worst raw returns of the test at 51.9 %.

From this, we already see that the dispersion in Sharpe ratio is much higher than the dispersion in raw returns. The findings also highlight the two key drags of extreme settings mentioned before: Too conservative means diluting returns, too aggressive means overconcentrated idiosyncratic risk without return compensation.

Regarding the counteracting effects, it makes sense that the best-scored concepts are clustered in the middle of the matrix, with the best-ranking setting for 10 positions at 50% sizing tolerance resulting in 63.5% raw CAGR and a Sharpe ratio of 1.68.

Of course, please don’t just take the best setting and run with it. A lucky backtest in a matrix with substantial multiple-testing bias is not a good indicator. Take each result with a grain of salt. Also from the normalized combined score values, you see that the range of results is comparitively narrow. Most sizing concepts have over 90% of the performance of the “best” concept in the test. So should you really take double the concentration risk for 10% higher backtest Sharpe ratio? Probably not.

The best compromise between raw and risk-adjusted returns seems to be around 10 positions. Within this row, increasing the sizing tolerance has no significant effect besides increasing the real-time idiosyncratic risk dispersion a lot. So the prudent choice would be to go with 10% tolerance.

I learned it the hard way recently, when I sold my position to buy . To not leave cash on the sidelines, I bought a substantially oversized position, which collapsed 40% since. While the backtests imply that such oversized losers will eventually be evened out by oversized winners, I can tell you that this increased outcome dispersion and source of randomness will be annoying, distracting and frustrating as soon as you have real money on the line. I changed the limit to a more prudent 20% since.

The findings are similar for the other strategies, so I will just list the underlying tables below, so you can explore them on your own. It is interesting to mention, that the absolute return dispersion across the testing matrix is comparable even for the shorting strategy, however, the relative outcome dispersion is much higher due to the much lower expected returns.

Long Europe & Canada Microcaps

Long North Atlantic Microcaps

Short North Atlantic Largecaps

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

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Portfolio123 designer models, Wikifolio, factor regression analysis and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Factor portfolios

As factor proxies, I created long/short screens picking the highest ranked 500 stocks long and the lowest ranked 500 stocks short in equal-weight rebalanced monthly based on the core style ranking systems of Portfolio123. I set NA handling to neutral to avoid distortions from missing data. The universe is based on North Atlantic Primary universe excluding stocks priced below 0.1€ and with median 100-day volume below 10,000€.

Where does this deviate from usual factor definitions?

1. Equal-weight contruction including microcaps,

2. monthly instead of quarterly or annual rebalancing,

3. composite contruction instead of single metrics.

In my opinion, factor construction and universe should be close to actual practical implementation toget the right picture.

As “Size” factor, I added a rank combination of 100-day liquidity and market cap. As market factor proxy, I took simple returns ex returns.

Long/Short Book Factor Regression

Here you see the factor portfolio returns and long/short book returns since book inception:

I took the returns of my live book, subtracted returns as risk-free proxy and regressed the factor returns on the series. From this naive factor regression I got the following results:

The data is limited and the year has been messy. The only clear contributors to the book returns according to the regression were momentum and size. The market beta of 0.41, the sentiment beta of 0.58 and the growth beta of 0.45 were also strong contributors to portfolio movement but statistically the relationship is not as clear. The low contribution of LowVol and Quality factors is expected as I am agnostic to those. A bit surprising is the low value factor loading.

The ML component seems to have tilted the multi-factor anchor recently in the direction of momentum. This becomes also clear looking at the above chart where momentum seems to have a high correlation to the portfolio until recently, seemingly decoupling in May.

I took the factor portfolio returns and entered them into the regression to calculate the absolute return contributions. To get a more realistic picture I replaced the regression alpha with the actual outperformance of the live portfolio and subtracted the risk-free return net of funding costs:

From this analysis, it becomes even clearer that momentum was the main driver of recent returns. Despite the low loading, even value contributed a large chunk as the second-best performing factor portfolio in the period. The main drag came from poor perfomance of sentiment components and size.

The poor performance of the size factor as I defined it seems surprising at first with the Russell Microcap and Russell 2000 making new all-time highs. However, what most people miss is that these cap-weighted indices are filled with stocks with > $10B market cap and have recently decoupled from “true” equal-weighted microcap universe. Here is a simple comparison of somewhat liquid stocks below $1B market cap vs. the Russell “Microcap”:

Of course, a simple factor regression only catches single-factor contributions. Any factor interactions will land in the “alpha” portion. So, what if I add a multifactor component? The results are shown below:

Alpha shrinks and multifactor becomes the highest exposure factor. Still, 8.1% hypothetical alpha remain showing that a substantial amount of stock-specific factors and/or non-linear ML signals are present.

More data is definitely needed. While this year is tough, the strategy navigated it reasonably well so far. Simpler factor stratgies did not work much better than my complex approach. In my opinion, the main source of volatility and lackluster outperformance is my investing universe. Broad true microcaps are still struggling worldwide and lack AI exposure. China’s weakness affects European markets as well. Meanwhile, the precious metal bust and oil volatility hit Canadian microcaps.

But the storm will end eventually.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

July portfolio update, benchmark portfolios, factor regression analysis and the week.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

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Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

June Portfolio Update

By June 30, the portfolio stood at +13.23% YTD. As the US-Iran back-and-forth continues, so does the volatile sideways movement of my portfolio. For a brief moment on June 23, I was close to making a new all-time high and breaking the 2026 curse but since then I once again had to give back most of the return.

Regarding current returns of my book in comparison to simulations back to 2004, I am currently sitting at the lower quartile border for both rolling 13-week and rolling 26-week returns:

Still, the 1-year performance holds up strong at 49.5% which includes the unlevered long-only era until November 2025:

If we look at the YTD and June returns by sub-strategy, we see the main drag recently came from ExUS (and global which currently has a high overlap to ExUS). The only performer this month was the short strategy:

Looking at the weekly risk statistics (weekly for sufficient sample size), the main selling point for the levered long/short book since inception in its current form is the low beta (resulting in an artificially high alpha). I used the FTSE Developed Markets ETF as benchmark for the analysis since it’s usually the best beta and correlation match from the list of available benchmarks. It also makes sense considering the fact that the shorts are mainly US tickers cancelling out the US exposure of the US microcap strategy, leaving the book with almost pure ExUS exposure recently.

Taking the size and the value factor into account by using the Russell 2000 Value as benchmark results in similar alpha and beta metrics. But let’s be honest, you cannot really “eat” that alpha. The book’s annualized return, Sharpe ratio and Sortino ratio all lag behind. Of course 32 weeks is a way too short period to make any conclusions.

2026 is a difficult market so far, especially for concentrated microcap industry bets outside the AI compute trade. There is a lot of whipsawing going on due to fast-paced macro narratives. Global market breadth is mediocre at best. Over the past 3 months, only two US/CAN sub-sectors outperformed the S&P500 significantly: electronic components and hardware, so basically semiconductor-related stocks. The European sub-sector landscape looks very similar compared to the S&P500, although broad European indices also lag behind. Also note that many 6-month and 12-month winners are recent 3-month losers. Examples are energy stocks (a key Q2 component of my portfolio) and mining stocks (a key Q1 component of my portfolio). Considering this whipsawing headwind, the YTD performance actually turned out alright.

As of today, the portfolio allocation stays heavily long upstream energy and industrials with main focus on transportation. Materials exposure moved almost entirely on from the gold trade by now.

Current total allocation (as of July 1, 2026) Long:

Current total allocation (as of July 1, 2026) Short:

US Long Small/Micro

Realized trades in June:

None

ExUS Long Small/Micro

Realized trades in June:

Thor Explorations Ltd. $THX.V (+153.8%)

Thor Explorations Ltd. is a small Canada-based gold producer and exploration company with projects in Africa. I bought in early 2025 and held since. In the autumn of 2025, I started my global strategy and moved part of the position there. The sale in June 2026 followed a volatile downward grind giving back almost 50% of the gains from the high. The stock simply never got to ugly or expensive to sell, so it was finally sold on rank drift below the tolerance threshold (too many better opportunities). Still, a great ride. I tried to “learn” from it, backtesting different exit signals on overheating or being more strict on “old” positions but I didn’t really find a solution with good signal-to-noise ratio. So the main lesson is: giving back some return on strong trend plays is just part of the game. Trying to force perfect exits usually just leads to missed opportunities.

TOYO Co. Ltd. (+28.9%)

TOYO Co., Ltd. is a Tokyo-based solar technology company. They design, manufacture and sell solar cells and photovoltaic modules across Asia and have expanded into the USA. Bought on a strong breakout, sold within 48 days on rank drift without major news. Good example for lucky timing. Since then dilution risk and expansion plans caused a selloff.

Rubellite Energy Corp. $RBY.TO (+21.1%)

Rubellite Energy, a Canadian heavy crude oil producer, was one of the few successful energy trades so far. Bought on a pullback, sold within 58 days into strength on rank drift. While this looks like a perfect swing trade, this usually isn’t the sort of trade that brings the money to the table. Trades like this are important and help offset short-term losers but due to the low hit rate, they don’t move the needle on overall return. Multi-quarter multi-baggers are the actual golden goose

Monument Mining Ltd. $MMY.V (-6.2%)

Monument Mining Ltd., another Canadian gold mining company, this time operating in Malaysia and Australia, is a rather tragic story with a lesson. As you see from the chart, I bought them end of 2024 and was set to ride the whole uptrend but I sold in autumn 2025 due to what turned out to be a NA handling error on my side. Wrongly sold I still decided against reversing the trade because there were supposedly better alternatives. In a cruel joke by the market gods, the stock exploded after bomb-shell numbers. My system bought back in shortly after. Following the recent volatile downward spiral for miners and disappointing numbers by $MMY, I had to close the trade with a slight loss. Without the blind sell in 2025 on a design mistake on my end, the trade would have been similar to $THX and other miner trades. Now it is just an expensive lesson reminding me to always double-check the source of a signal.

Serabi Gold Plc $SRB.L (-19.2%)

With Serabi Gold Plc, a UK gold miner operating mainly in Brazil, I had an intentional on-off relationship. Serabi was one of the last men standing in recent months. The chart held up pretty well vs. peers losing 20-50% in market value. So my system entered the ticker for one last speculation but had to capitulate eventually.

Global Long Small/Micro

Realized trades in June:

Thor Explorations Ltd. $THX.V (+14.2%)

As mentioned before, I shifted part of my position in THX to the global portfolio which also was sold recently. At inception of the new strategy, which opportunistically can over- or underweight my US and ExUS exposures, the gold trade was already very mature.

Afentra Plc $AET.L (-3.8%)

Afrenta is a UK-based oil and gas company operating in West Africa. At buy, it looked like the perfect entry. Intact uptrend, slight pullback, strong macro tailwind, cheap and profitable with good sentiment… But just as for many other energy stocks, peace hopes send the prices south. I still consider this a very successful trade as it demonstrates where most return come from:

1. Constantly check if the entry thesis is still intact without sunk cost fallacy.

2. If the entry thesis breaks, sell and move on quickly.

3. Let winners with intact thesis run, sell losers (or winners) with broken thesis fast, even if that means selling few weeks into the trade.

Whatever those points mean in detail to you, I believe they are somewhat universal. Of course there is always a fine balance between signal and noise, especially when taking trading costs into account. I assume the tree-based ML component of my system saw a technical pattern in the shifting industry momentum and volatility it did not like and pulled the plug fast. Classic linear multi-factor signals usually do not react that fast without an earnings announcement or stock price crash.

StealthGas, Inc. (-16.0%)

Stealthgas, a Greece-based LPG shipping company, seemed like an obvious play for the Hormuz crisis but faded since. Not much more to tell about it. Some trades don’t work out and then it’s best to cut losses fast and move on.

Global Short Large Caps

Realized trades in June:

Bolloré SE $BOL.PA (-5.3%)

Bolloré SE is a French holding and industrial conglomerate with activities in logistics, energy distribution, communications, amongst others. The chart includes the large special dividend.

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Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

Canada/Europe Microcap Sub-Sector Analysis and Weekly Portfolio Update.

This article is sponsored by Portfolio123. The platform provides high-quality industrial-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.

Subscribe now

Full Disclosure: This article is sponsored by Portfolio123. The platform provides high-quality industry-standard data and powerful no-code stock analysis tools to boost your investment process. The growing community of skilled and creative traders and investors shares their knowledge publicly and enables quick success. Nowadays, I do most of my research and all of my single stock analysis using Portfolio123.

If you want to support my work and want to check out the platform, sign up today and start your free trial using my affiliate link.

The Universe

As in the previous post, I will use a custom universe. A pure canada universe with my criteria would contain roughly 300 stocks, which is not enough for a sub-sector analysis. For this reason, I will pair Canada with Europe. To do so, I have to use North Atlantic (Primary) as starting universe and exclude the US as exchange country. Please note that the backtest results include some exchange countries which are not available for trading on common international brokers like IBKR, such as Turkey. For the sake of completeness, I left them in for this analysis. Some samples tests showed unrestricted results are slightly biased towards higher returns when including those “exotic” European exchanges. However, the impact on the multifactor overlay results was negligible.

I exclude OTC stocks and MLPs, stocks with a median (50-day) daily dollar trading volume below $10,000, stocks with a price below $0.10. Furthermore, I exclude the upper 25% by market cap to create a pure small and microcap universe. The rules reduce the total Canada/Europe universe from 7,891 stocks down to 3,304 stocks.

For the US universe in the last post, I excluded the top third of stocks by market cap. The same rule would restrict the non-US universe to stocks below $500 market cap, much lower than in the previous analysis. To make both analyses comparable I thus use slightly higher threshold. As of today, the largest stock in the new universe screen is AEGN.AT with $1.3 billion.

Sub-Sector Analysis

Due to the high number of sub-sectors, I will let the numbers and charts talk for themselves. The goal is to get an overview of the following topics for each sub-sector:

• How many small & microcap stocks exist in this sub-sector over time?

• How did the subset perform relative to Europe Smallcaps?

• How did a selection of top stocks by a multi-factor overlay perform?

  • For this, I will pick the top 10 stocks quarterly by P123 “Core: Combination”, a multi-factor composite ranking system combining well-known metrics to measure value, momentum, growth, sentiment, quality and low-volatility

• What if we only pick sub-sector stocks with universe-wide ranks >90%

  • This is different from picking the top stocks from the sub-sector, no matter the absolute rank value.

  • If this much stricter system finds no stocks, it will stay in cash.

  • While this is not practical for direct trading, this test visualizes regime dependencies and, for example, whether the sub-sector making it into the top of the ranking is a “trap” or if this sub-sector is totally overlooked by a multi-factor ranking system.

For each case, I will use quarterly rebalancing and a 0.25% slippage setting. Sub-sectors without stocks are omitted.

Summary table:

Full universe

Unrestricted:

Top 10 multi-factor picks quarterly:

Top multi-factor pick examples today:

Business Services

• Underlying sector: business services

• Typical industries: marketing, admin, consulting, security, waste management

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Hospitality Services

• Underlying sector: consumer services

• Typical industries: hotels, restaurants, travel services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Media and Publishing Services

• Underlying sector: consumer services

• Typical industries: broadcast, print media, publishing, entertainment programs

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Consumer Goods

• Underlying sector: consumer cyclicals

• Typical industries: apparel, footwear, leisure goods, appliances, furnishing

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Miscellaneous Retail

• Underlying sector: consumer cyclical

• Typical industries: auto retail, gas stations, pet supply, home builders

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Consumer Vehicles and Parts

• Underlying sector: Consumer cyclical

• Typical industries: consumer vehicle parts and manufacturing

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Consumer Retail

• Underlying sector: consumer discretionary

• Typical industries: apparel retail, electronics retail, home improvement stores

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Upstream Energy

• Underlying sector: energy

• Typical industries: coal/uranium/oil/gas exploration/production/services, tankers

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Downstream and Midstream Energy

• Underlying sector: energy

• Typical industries: LPG distribution, refineries, oil/gas pipelines/storage

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Banking

• Underlying sector: finance

• Typical industries: regional banks, mortgage banking, international banks

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Insurance

• Underlying sector: finance

• Typical industries: insurance brokerage, life/health/property/casualty/re-insurance

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Investment Services

• Underlying sector: finance

• Typical industries: asset management, financial advisory, investment banking, holding companies, trading services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Real Estate

• Underlying sector: finance

• Typical industries: REITs, real estate services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Specialty Finance and Services

• Underlying sector: finance

• Typical industries: finance software, commercial and consumer finance services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Biopharmaceuticals

• Underlying sector: healthcare

• Typical industries: biopharmaceuticals (oncology, pain, neurological, etc.)

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Healthcare Services

• Underlying sector: healthcare

• Typical industries: health plans, admin services, patient care, nutrition

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Healthcare Equipment

• Underlying sector: healthcare

• Typical industries: diagnostic devices, medical instruments and supplies

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Industrial Manufacturing

• Underlying sector: industrials

• Typical industries: aerospace & defense, power systems, machinery

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Industrial Services

• Underlying sector: industrials

• Typical industries: transportation, logistics, distribution, construction

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Chemical, Plastic and Rubber Materials

• Underlying sector: non-energy materials

• Typical industries: plastic, rubber, fibers, paint, agrochemicals, additives

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Mining and Mineral Products

• Underlying sector: non-energy materials

• Typical industries: metal parts, ore/mineral mining, precious metal mining, construction materials

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Manufactured Products

• Underlying sector: non-energy materials

• Typical industries: wood and paper products, packaging, textiles

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Food and Staples Retail

• Underlying sector: consumer non-cyclical

• Typical industries: grocery stores, department stores, drug stores, discounters

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Food and Tobacco Production

• Underlying sector: consumer non-cyclical

• Typical industries: animal/crop/food/beverage/tobacco production

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Household Products

• Underlying sector: consumer non-cyclical

• Typical industries: personal care, cosmetics, baby accessories, small appliances

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Household Services

• Underlying sector: consumer non-cyclical

• Typical industries: educational and personal services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Electronic Components and Manufacturing

• Underlying sector: technology

• Typical industries: electronic components & equipment, semiconductors

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Hardware

• Underlying sector: technology

• Typical industries: commercial/consumer electronics, IT/communication hardware and equipment

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Software and Consulting

• Underlying sector: technology

• Typical industries: software, data services, video games, tech consulting

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Telecommunications

• Underlying sector: telecommunications

• Typical industries: satellite services, wireless services, telecom services

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

Utilities

• Underlying sector: utilities

• Typical industries: electric, gas, water utilities

Unrestricted:

Top 10 multi-factor picks quarterly:

Top 10 multi-factor picks quarterly (if universe-wide rank > 90%):

Top multi-factor pick examples today:

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If the presented tools and concepts resonate with you so far, feel free to join the Portfolio123 community through my affiliate link – it supports my work at no extra cost to you.

Disclaimer

This publication provides general information on systematic investing and Portfolio123 strategies for educational purposes only. It is not personalized financial, investment, tax, or legal advice. All content reflects my independent opinions and analysis. I may hold securities mentioned in posts. Past performance does not guarantee future results. Investing involves significant risks, including potential loss of principal.

No recommendations: Do not buy, sell, or hold any securities based on this content. Consult qualified professionals before making investment decisions.

Disclosures: This Substack is sponsored by Portfolio123. I am a Portfolio123 affiliate and may earn commissions from links at no extra cost to you.

Your use of this information is at your own risk.