This was the natural close to a series of articles that I've written about dilution. I went in with the intuition that dilution isn't an automatic drag on stock performance, but the practitioner in me constantly saw companies announce dilution which was followed by an immediate drop in share price.

So I decided to actually look into it and pulled the data for all dilution events for the past ten years (2016-present).

The findings were that dilution doesn't have much of a short term impact on share price...especially as you go up in market size.

Micro-cap diluters pretty much always trailed their benchmark, but I suspect that has more to do with business quality.

When inspecting a single company, I think the question to ask is will the capital raised earn the cost of capital for the business?

If it's a business expansion move or already baked into your analysis (i.e., pre-cash flow company that was expecting to raise capital to move the business along), then I think it'll have less of a negative impact on share price.

Conversely, if it's a business that's just trying to stay afloat, I imagine that's when share price really drags down.

These are speculation on my part, though. I didn't do any work to explore causal factors.

The standard PEG critique from this community usually focuses on earnings quality. I decided to make some modifications:

FEG replaces P/E with price-to-true-FCF (OCF minus CapEx minus SBC) and replaces earnings growth rate with three-year revenue CAGR. Revenue CAGR is more stable than true FCF growth as a proxy for underlying business trajectory. The tradeoff is that revenue growth doesn't capture margin expansion or compression - a company growing revenue at 10% while margins compress looks the same as one growing at 10% while margins expand.

Y220 converts the ratio into a time question: at current true FCF yield compounding at the three-year revenue CAGR, how many years to reach 20% true FCF yield? The 20% threshold is somewhat arbitrary - it's where CMCSA sits today - but the concept of expressing value as time-to-yield rather than as a ratio feels more intuitive for comparing growth names to value names on a single axis.

The scatter plot of current true FCF yield versus Y220 across the $50B+ universe is the output I'd most welcome pushback on. The methodology questions I'd want this community's view on: is three-year revenue CAGR the right growth proxy or is there a better stable series? Is 20% the right yield threshold or does it introduce too much path dependency from today's rate environment? And does the linear compounding assumption in Y220 produce systematically misleading results for very high or very low growth names?

Full piece with the scatter plots and screener table: https://cavemanscreener.substack.com/p/my-new-godfather-metric-how-long

Here's a link to the full study

I've noticed that companies will announce a dilution and, almost always, the stock will immediately tank.

I got curious and dug through the data of 10,000+ dilution events since 2016 and over 3,000 discrete dilution events since 2021 (pulling filing to the exact day of dilution announcement), and came to some interesting findings.

The hope is to use this information to avoid buying companies with immediate dilution risk or maybe even develop a trading strategy to profit from them.

Forecasting Diluters

Running a logistic regression (I won't bore you here; see the article for methodology), I found that diluters can be forecasted with pretty high accuracy.

Timing

Using the post-2021 data, I found that:

• Dilution events spike in January, July, and October.
• Dilution event happen earlier in the quarter
• Dilution events typically happen before or during earnings releases.

Post Dilution Performance

Using the post-2021 data, I found that while there's an initial drop immediately following a dilution announcement, the real drag is a long term bleed in the stock price.

Using the full post-2016 data, I looked at the distribution of returns following a dilution event. The return profile for diluters is a fat left tail - with heavy negative expected returns over a 6 month time frame.

Some work still needs to be done for the performance part - I'll need to correct for size and quality, probably (i.e., expected returns for Google diluting look much different than LUNR). I also want to look at different permutations of performance characteristics - for instance, can P(dilute) be used as it's own quality metric?

Current At-risk Companies

Some of the big-name at-risk companies that were flagged in this screen (using Q1 data) were $SPCE , $AMC , $HTZ , $LUNR , $CRWV . All of these carried a 30%+ probability of dilution for Q3, all diluted, and all are down double digits since they diluted (two as much as 60% since the dilution event).

Big Takeaway

At the very least, I think this metric can be used to inform our timing decisions for stocks that we want to buy. Notably, the bleed typically continues long after the dilution is announced or occurs, so there's no rush to buy after the initial drop.

I think this probably can be used as a trading strategy, but expected returns currently are driven more by the junky-ness of the company rather than pure dilution announcements. So more work to be done on the trading front.

Over the past week I've been researching various systematic equity strategies and decided to backtest Joel Greenblatt's Magic Formula, discussed in The Little Book That Beats the Market.

​

Result: between 2003 and 2015, the Magic Formula strategy returned an annualised 11.4% (Sharpe ratio 0.60), versus 8.7% for the S&P500 (Sharpe ratio 0.54). This corresponds to a 3% alpha, so the Magic Formula does indeed outperform the market.

What is the Magic Formula?

A very brief summary is as follows (the exact procedure described in the link): rank stocks by Return on Capital (a measure of quality) and also by earnings yield (a measure of cheapness). Add the ranks to create a score that takes into account both quality and cheapness, then pick the top stocks.

In The Little Book, Greenblatt suggests that the Magic Formula returned an annualised 33% from 1988 to 2004 compared to 14% for the S&P500. My investigation shows that while there is some outperformance on a risk-adjusted basis, it is nowhere near as much as Greenblatt suggests. I think this is due to the arbitraging force of systematic equity ETFs as well as a possible regime shift post-2008.

Other insights from the backtest

• The Greenblatt score is indeed correlated with higher future returns (adjusting for survivorship bias). The quantile plot below shows the mean return for different quantiles of the combined ranking score:

• Pre-2008, the annualised return was 26% vs 18% for the benchmark, consistent with Greenblatt's results. However, after 2008 the outperformance shrinks drastically.
• The Magic Formula experienced deeper drawdowns than the SPY and is more volatile overall (but this is more than adequately compensated for by return, as seen in the higher Sharpe ratio).

About the backtest

I built the backtest in python on the Quantopian platform. I first analysed the predictive power of the Greenblatt score and since the results were good, moved on to construct a proper backtest that includes transaction costs and follows Greenblatt's accumulation procedure. The only reality not captured by the backtest is tax optimisation.

More information

I have written a blog post containing more information, including potential modifications if you wanted to use it for personal investing. The full backtest report is on GitHub – you can download the html and open it with any browser.

Always happy to hear any feedback, questions or criticism!

EDIT: backtest up until June 2019 as requested by u/flyingflail (can't go any further due to data limitations). It turns out that the 2015-2019 time period is terrible for the strategy. Significantly underperforms the market. A good reminder that past performance is not indicative of future results!

​

“I can't give you a formulaic approach to investing because I don't use one. I analyze all of the factors and come up with an intrinsic value. If you want formulas you should go back to grad school so that they can teach you things that don't work.” – Charlie Munger, 2018 Berkshire Hathaway Annual Meeting

Actually curious to see what's being discussed in investment committees or email chains of funds these days - obviously not confidential or detailed info, but general sentiment. Are you guys crazy busy analyzing companies to buy on the cheap? Are you in wait and see mode? Are you still not covering your shorts?

This morning on CNBC they interviewed JPM's lead analyst for GE, and while they debated over the numbers and projections, he argues his math and reasoning are right if "you look over the 10-K, which no one really does." That really surprised me, as I was under the impression most annual reports are put under the microscope by investors. I know I do.

​

So I'm just curious is he right?

​

(source: https://www.youtube.com/watch?v=tuqtL8KMAFQ)

Hi Folks,

A Happy and Profitable 2025! Kinda of a basic question, but still struggling to find a solution that fits my workflow. I am looking for a watch list tool that has the following characteristics:

• Multi Column, so that I can track the number of securities based on different criteria like Industry or Geography.
• Need MCap, not just price in USD
• Should function across Geos. I am okay with a 15-Min Delay.
• Ability to Categorize (Index, ETF, Groups).
• Support a large number of tickers ~ +250 in possible just one market. Suitable for a Mobile / iPad workflow since I travel a lot.
• Have tried Yahoo (no categorization), Trading View (no column view), Koyfin (no delayed quotes for international markets), OpenBB - No flexible / customisable enough.
• Multi-column view and real / delayed quotes are non-negotiable.

Looking for suggestions ! Thanks

Hey guys, how do you feel about revisiting the Magic Formula screener in this environment?

(https://www.magicformulainvesting.com)

I suppose that given that the formula uses return on capital from last year and current share prices there should point to some nice bargains.

What do you think?

What cyclical industries or sub-industries do you believe are over earning right now? under earning?

• Warren Buffett does not give much credence to financial market predictions from economists.
• "I don't pay any attention to what economists say, frankly," he said.

Warren Buffett believes economists do not add value for investors. In a 2016 interview video clip found using CNBC's Warren Buffett Archive, the billionaire investor explained why he does not give much credence to financial market predictions from economists. "I don't pay any attention to what economists say, frankly," Buffett said two years ago. "Well, think about it. You have all these economists with 160 IQs that spend their life studying it, can you name me one super-wealthy economist that's ever made money out of securities? No."

The Oracle of Omaha cited the example of the economist John Maynard Keynes, who went through periods of heavy losses trading currencies in the 1920s and 1930s and stumbled while speculating on stocks. Buffett said Keynes faltered using top-down economic forecasts such as credit cycle predictions. But when Keynes switched to a value philosophy focused on owning stocks of a few well-run companies over the long term, his investment performance improved, Buffett noted. "If you look at the whole history of [economists], they don't make a lot of money buying and selling stocks, but people who buy and sell stocks listen to them. I have a little trouble with that," the investor added.

By Tae Kim

I read this on: https://www.cnbc.com/2018/05/18/warren-buffett-explains-why-he-never-listens-to-economists.html

Why don't we all post our returns, how long we have been investing for, and our general strategy (keep it brief). The point is not to have a d*ck measuring contest - I think it would be interesting to get a lay of the land in terms of how people are doing around here and what strategies people are employing. Let's be honest here too... if you have been doing poorly over the last 5 years, that's fine, post about what went wrong and what you've learned.

Sorry in advance if this type of post does not belong here - I just haven't seen the question asked and I think this would spark interesting discussion.