invest(igations)

Super Stocks: A New Assessment of Ken Fisher's Pioneering Book

This article is the fourth in a series about screens designed by famous investors. The first, on Benjamin Graham, can be found here; the second, on William O’Neil, can be found here; the third, on Joel Greenblatt, can be found here; and for an overview of the subject, see my article “Can Screening for Stocks Still Generate Alpha?”

Kenneth L. Fisher finished writing his very first book, Super Stocks, in September 1983, when he was 32 years old. Back then, it made sense to write,

First, get a personal computer. . . . It would help to have two disk drives. Then get a modem and communications software. . . . Through a normal push-button telephone line and modem, [personal] computers can access broad data-base services such as “The Source” by Reader’s Digest. . . .

Despite some dated stuff like this, it’s still a good read, full of good advice. And surprisingly, Fisher’s system still works, despite enormous changes in the market over the last 38 years. That’s more than I can say for most stock market writers.

What Is a “Super Stock”?

Fisher defines it right up front.

A Super Stock is defined to be both:

• A stock which increases 3 to 10 times in value in three to five years from its initial purchase.

• The stock of a Super Company bought at a price appropriate to an inferior company.

Fisher was a true pioneer here. He wasn’t kidding when he opened the book with the words “THIS BOOK OFFERS CONCEPTS NEVER BEFORE PRESENTED.” Those concepts were two value ratios: the price-to-sales and the price-to-research ratios. At the time he was writing, no other investors used these, and nor did academics. James O’Shaughnessy, author of What Works on Wall Street, named Fisher as the first person to define and use the price-to-sales ratio, and the price-to-research ratio is still on very few people’s radars. He was also one of the first people to identify small-cap value as a universe worth looking at. It’s one of the many reasons that Fisher became one of the richest Americans, a billionaire whose firm, Fisher Investments, now manages $160 billion.

Super Stocks is a guide for discretionary stockpickers. Unlike Benjamin Graham or Joel Greenblatt, Fisher did not lay out a very specific step-by-step strategy for picking stocks. There are only two hard-and-fast rules in the book: never buy a stock with a price-to-sales ratio greater than 3, and if you’re looking for Super Stocks, avoid any stocks with a price-to-research ratio greater than 15.

Fisher doesn’t give any rules for liquidity in his book. After all, in order to buy a stock back then, you had to call your broker, so stocks weren’t really that liquid to begin with, compared to today. So I’ll use some off-the-cuff rules here. If you stick with stocks with a market cap of $30 million, a price greater than $1, and an average daily dollar volume greater than $50,000, you still have between 150 and 250 stocks to choose from if you follow Fisher’s rules. How you choose them is the subject of most of the book.

Most of the criteria that Fisher identifies are unmeasurable. Five of the key criteria are (and I use his terminology here):

• growth orientation,
• marketing excellence,
• an unfair competitive advantage,
• creative personnel relations, and
• the best in financial controls.

Of these, only the last is somewhat quantifiable. If the company has negative income or negative cash flow, there should be more than sufficient working capital to cover it, and the debt-to-assets ratio should be relatively low.

Fisher also places a good deal of emphasis on margins, but a super stock doesn’t have to have strong margins when one buys it. Instead, it has to have strong evidence that its margins will be good. Fisher believes that a company’s market share, along with the market share of its largest competitor and the growth of the industry, has a lot to do with its margin potential.

There are a few other quantifiable odds and ends in Fisher’s approach—he favors companies in industries without giants; he favors companies with few analysts. But these factors are not as important as his two value ratios, the five criteria above, and the possibility of strong future margins.

Creating and Backtesting a Screen for Super Stocks

Let’s first stick with Fisher’s two-rule system—low price-to-sales and low price-to-research (and research is best defined as R&D expenditures)—and use the minimum liquidity rules I gave above. Fisher is a long-term buy-and-hold investor, so let’s try a rolling backtest with a two-year holding period. If we assume slippage of 0.5% per round-trip transaction, the average two-year return per stock that passes this test since 1999 is 36.44%, compared to the S&P’s average two-year return of 15.55%. (I used Portfolio123 to perform this test, relying both on Compustat and FactSet data.) If we look at just the past ten years’ worth of two-year returns (which means starting twelve years ago, in January 2009), the average return goes up to 44.58%, compared to 30.09% for the S&P.

Fisher suggests that we should really focus on stocks with a price-to-sales ratio of less than 1.5 and a price-to-research ratio less than 10. Stocks with price-to-sales between 1.5 and 3 aren’t forbidden, but are not preferred; the same goes for stocks with a price-to-research ratio between 10 and 15. These two changes narrow our universe considerably: now we’re buying only about 100 stocks at a time. And now our results are even higher: the average stock since 1999 gets a two-year return of 45.65% and the average stock since 2009 gets a two-year return of 52.03%.

If we narrow our universe even more and just buy the ten stocks with the lowest price-to-sales and price-to-research ratios, we get even better returns. Because Fisher places more emphasis on the price-to-sales ratio than the price-to-research ratio, I’ve ranked these stocks according to the product of the square of the price-to-sales ratio and the price-to-research ratio, and chosen the lowest.

The average of these top-ten stocks goes up to 47.26% since 1999 and 79.43% since 2009. Moreover, the former per-stock results reflected the fact that you bought a lot more stocks during a down market than during an up market; this 10-stock backtest simulates an even portfolio, and therefore it’s harder to get good per-stock numbers from it.

If we try to add some of Fisher’s other criteria using a screen, we’re necessarily extrapolating wildly from what he wrote. Using a ranking system loosely based on them may be a bit better, but when I tried this, it didn’t improve backtested results, and it’s once again a real extrapolation. The simple value system that Fisher came up with in 1983 still works as a general system for buying stocks.

Sell Rules

But holding stocks for two years is quite arbitrary. Fisher actually lays down some hard and fast sell rules. Only sell a stock when its sales ratio is between 3 and 6—or if you judge, based on the above criteria, that it’s no longer a super stock.

We can simulate the first part of this strategy using Portfolio123’s simulation engine. We’ll use the same liquidity rules as we used above, and the narrower limits—price to sales less than 1.5 and price to research less than 10. We’ll buy every stock that passes those limits every week and hold until either the company gets bought, goes out of business, or rises to a price-to-sales of 3 or higher. If we run this since January 1999, we get a record of buying close to 3,000 stocks—quite a huge sample. Their average return is 70.52% with an average holding period of 3.5 years. If you held the S&P 500 for 3.5 years over this period your average return would be 30.77%. The portfolio itself would vary quite hugely in size, sometimes holding over 600 stocks and sometimes fewer than 300; the annualized return of its equity holdings would be 18.33%.

We can also simulate the strategy over only the last ten years, buying stocks from January 2011 until now. The average return is now 62.73% over an average holding period of a little over three years. The annualized return of its equity holdings is 15.38%.

A Look at Past Screen Results

Three years ago today, the top ten stocks ranked by the product of the square of the price-to-sales ratio and the price-to-research ratio included three Chinese companies about which reliable information would have been difficult to come by. Besides these, the top ten stocks were Eastman Kodak (KODK), Renesola (SOL), Centrus Energy (LEU), Unisys (UIS), Radisys (RSYS), Ophthotec (which later changed its name to Iveric Bio (ISEE)), Voxx International (VOXX), Perion Network (PERI), Toshiba (TOSYY), and Aviat Networks (AVNW). Some of these stocks turned out to indeed have Super Stock returns: 500% or 600%; some of them just sat there; and one of them—Kodak—tanked, losing 20%. Would it have been possible three years ago, using Ken Fisher’s methodology, to separate the wheat from the chaff?

Perhaps not. Out of these ten stocks, the “Super Stocks,” in terms of their return, turned out to be Renesola, Centrus Energy, and Perion; back in January 2018, the potential of the first two of these companies didn’t look a whole lot better than Kodak’s or Toshiba’s. Of these ten stocks, only Ophthotec and Aviat had improving margins at this point, with Perion looking hopeful as well. Here are my very rough (and rosy) guesses about how these stocks would have met Fisher’s criteria in January 2018:

If you had blindly invested in all ten stocks and held for the next three years, you would have earned 194%, compared to only 47% with the S&P 500. If, on the other hand, you had stuck only with the stocks with higher “Super Company” potential—Ophthotec, Perion, and Aviat—you would have earned 190%, which isn’t much different.

This, of course, doesn’t prove anything. It’s a sample of ten stocks out of about 3,000 that passed the screen over the last 22 years. Perhaps if I had the energy, time, and research tools to go through all 3,000 I would find that Fisher’s criteria actually made an appreciable difference. Or perhaps I’d find that they didn’t.

What Companies Pass the Screen Today?

The ten highest ranked stocks according to this system (square the price-to-sales ratio, multiply by the price-to-research ratio, and look for the lowest numbers) are—if we exclude companies that are under M&A conditions or have filed for bankruptcy—Tenneco (TEN), Weatherford (WFTLF), Power Solutions Intn’l (PSIX), L.S. Starrett (SCX), Goodyear (GT), Nissan (NSANY), Ion Geophysical (IO), Diebold Nixdorf (DBD), Ford (F), and Cooper-Standard (CPS). Interestingly, half of these companies are in the automobile industry. Are any of them “super stocks”? If we look at the first part of Fisher’s definition—stocks that increase 3 to 10 times in value in three to five years—I would guess that some of them will be. But if we look at the second part, which concerns “Super Companies,” I doubt it.

Conclusions

Last year I wrote about the value of the ratio of R&D to market cap, so it was a great pleasure for me to discover now that Ken Fisher had advocated using the same ratio back when I was an undergrad. Of the many books on investing I’ve read, Super Stocks is one of the more prescient. Fisher figured out how to beat the market by using innovative takes on value ratios, and his strategy has been working well ever since. I find that quite impressive. Joel Greenblatt’s 2005 Little Book that Beats the Market, like Super Stocks, offered a rather simple two-factor investing model; but that formula stopped working about five years after he published it. The formula Ken Fisher offers in Super Stocks has been working for over 35 years.

My CAGR since 1/1/2016: 45%.

My top ten holdings right now: TRIB, FKWL, AVNW, STRT, MTEX, NTWK, CPIX, ISDR, RNDB, DVD.

20 Things I Learned in 2020 (About Investing, That Is)

2020 was an insane year on a lot of levels: the pandemic, the impeachment proceedings, the massive unemployment, the killings on America’s streets, the hubris of law enforcement, the rampant misinformation, the contesting of the election results . . .

But it was an insane year on a personal level for me too. Because when it comes to investing, it featured my biggest loss and my biggest gain. By March 18, I had lost one-third of my money. And then I tripled it. By year’s end I was up 105%.

So I thought it would be a good time to sum up (in completely random order) twenty things I’ve learned in this craziest of years.

How to Better Diversify By Using Subsector Classifications

I used to simply buy whatever stocks were the most highly ranked by my Portfolio123 ranking system without regard to what industry they were in. But that caused me to vastly overweight certain industries. Now I set a strict limit: if I have shares in more than a certain number of companies in one subsector, I adjust the number of shares in those companies downward proportionally.

How Inventory Changes Can Affect a Company’s Prospects

I hardly paid attention to inventory before 2020. The only use I found for it was in calculating a company’s cash conversion cycle. But I now know that companies that are decreasing their inventory (relative to their total assets) tend to slightly outperform companies whose inventory is unchanging or increasing. I studied this using Portfolio123. If you bought all the companies in the Russell 3000 whose inventory on their latest annual statement is less than what it was on their previous annual statement, you’d have a compound annual growth rate (CAGR) of 12%; but if you’d bought those whose inventory is greater, you’d have a CAGR of only 10%. And if you bought only companies whose inventory decrease is in the top 10% of all companies (relative to their average assets), your CAGR goes up to 13%. Why? There are a number of obvious reasons why reducing inventory is a good thing. You’re reducing excess and obsolete items, which you might eventually have to sell at a discount; you have lower carrying costs; you have less working capital tied up in goods; you’re increasing your cash flow and your efficiency. There are, of course, many instances in which reduction of inventory is a bad sign, but if you include this factor with many others, it might improve your assessment of a company.

How to Use Cash Flow to EV in Value Ratios

One of the most useful value metrics I’ve found, and one which I’ve been using for years, is unlevered free cash flow to enterprise value. The formula is pretty simple: take operating cash flow, subtract capital expenditures, add back after-tax interest expense, and then divide by enterprise value. But too often accounting for capital expenditures distorts the picture. Technically, free cash flow should only deduct maintenance capital expenditures, not growth capital expenditures. And there are other items in the cash flow from investing activities that muddy the picture.

There are basically six solutions to this dilemma:

1. Subtract the entirety of cash flow from investing activities (if you count capex as a positive number) from operating cash flow. Some financial writers favor this approach, which lumps in acquisitions with capital expenditures, and I personally use it when looking at the ratio of free cash flow to assets.
2. Stick with the above formula.
3. Try to differentiate (manually) between maintenance and growth capex.
4. Instead of deducting capital expenditures, deduct depreciation and amortization.
5. Deduct all capex but add back a rough estimate of growth capex (for example, Bruce Greenwald’s formula, which is the five-year average ratio of gross plant to sales times the annual increase in sales).
6. Disregard capex altogether and just use operating cash flow (plus after-tax interest expense). This is the one that I’ve just started using in 2020, in addition to the old one, with some positive results.

How to Vastly Increase Your Tax-Deductible IRA Contribution

Your annual tax-deductible contribution to an IRA is $6,000, or $7,000 if, like me, you’re over fifty. But in 2020 I was able to contribute more than five times that amount. How? By setting up a Self-Employed 401K. Anyone with some self-employment income can take advantage of this—it doesn’t even have to be a major part of your total income.

With a Self-Employed 401K, you can contribute both as the employee and as the employer. As an employee, you can contribute $19,500. You can contribute an additional $6,000 if you’re over fifty. And then as an employer you can contribute up to 25% of your self-employment income (up to certain limits).

Other than the contribution limits, the self-employed 401K is just like a traditional IRA: the same rules about what you can invest in apply, you have to take RMDs at age 72, and you can rollover other IRAs into it or roll it over into another IRA.

Note that this option works only if you don’t employ anyone else. If you’re self-employed but have other employees, then this account can’t be used.

How to Deal With a Value Inversion

The first half or two-thirds of 2020 continued the “value inversion” that we’ve seen in the stock market since, by various accounts, 2010, 2014, or 2017. No matter what the starting date, the 2017 to early 2020 period was extraordinary, as stocks with high price-to-sales and EV-to-EBITDA ratios absolutely crushed stocks with low ratios.

In April I published an article about this. I think my conclusions there are still valid. I deal with the value inversion by balancing simplistic and common value ratios, which have to a large extent been arbitraged, with more unconventional or innovative ones, and heavily weighting my Portfolio123 ranking system with measures of quality, growth, sentiment, and stability.

How to Take Into Account SG&A Expenditures When Valuing Companies

If you divide selling, general, and administrative expenditures by market cap, it’s a pretty useful ratio to add to your arsenal of value formulas. For a couple of years I’ve used R&D to market cap, but adding SG&A to market cap as well adds some balance and includes a lot more companies (since R&D expenses don’t apply to a large number of industries). (The SG&A figures I use exclude R&D expenses.)

How to Classify Companies Better Than Using GICS

I’ve long had a beef with the Global Industry Classification Standards stewarded by MSCI and S&P Dow Jones. I’ve never understood why Commercial and Professional Services should be included in the Industrials sector, for example. And GICS was designed without considering two important things: the correlation of industry returns and the fact that many companies operate in a number of industries at once.

In 2020, I discovered RBICS, a classification system owned and operated by FactSet. It is, in my opinion, superior in many ways to GICS. The two important things I named above are factors that RBICS takes into account. Companies that operate in several industries are assigned to the primary industry based on the percentage of revenue generated from operations in that industry. RBICS certainly has its share of faults, but after close study of both classification systems, I feel its advantages are quite clear.

How to Diminish the Market Impact of Your Trades

Before 2020 I foolishly believed that placing one large order would be cheaper in terms of transaction costs than breaking up your order into little pieces. But the advent of commission-free trades helped me realize I was very wrong. I decided to break up my large orders into smaller ones, thus significantly lessening the market impact of my trades. In addition, I began using conditional orders, which are not placed until the price breaches limits I’ve placed on the bid and/or ask. So the orders are effectively “hidden” until the price reaches a point at which I want to buy or sell. A large order that’s visible on the books at a price above the ask or below the bid will strongly affect that ask or bid for low-volume stocks. Now I avoid that as much as I can.

How to Use the VIX to Adjust Your Use of Margin

This year I learned that the CBOE’s VIX index has an extraordinarily high correlation to the future drawdown of the S&P 500. I did a little simple linear regression and came up with the following formula: the maximum future drawdown of the S&P 500 in the short term is probably going to be 0.01*VIX – 0.09 or less. So if the VIX is 30, the expected drawdown is probably 21% or less, if it’s 40, it’s probably 31% or less, and if it’s only 15, it’s probably 6% or less. In my margin account I now calculate how much of a drawdown will result in a margin call, compare that to the VIX-forecast drawdown, and adjust my margin debit accordingly. (Because I invest exclusively in small caps and microcaps, I actually use RVX rather than VIX.)

Here's my formula. Let m be the market value of your securities; let d be your margin debit (expressed as a negative number, or, if you have no debt, the excess cash you have, expressed as a positive number); let e be the expected drawdown calculated according to VIX or RVX (expressed as a negative percentage); and let x be the drawdown that will result in a margin call (also expressed as a negative percentage). The amount of money you'll want to put into or take out of securities in your margin account will be:

So, for example, if you have –$200,000 in debt, $700,000 in securities, a permissible drawdown of –30%, and an expected drawdown of –20%, then you can invest an additional $42,424 in securities.

How to Tell If a Company Has Too Much Debt

For a long time I’ve been using the debt-to-EBITDA ratio, but I’ve now found that the debt-to-cash-flow ratio is also important to take into account. The formula is simply total debt to operating cash flow. I find these ratios work better than the debt ratio (debt to assets), the current ratio (current assets to current liabilities), or the quick ratio (current assets minus inventory to current liabilities) when I’m trying to avoid overleveraged companies.

How Best to Eliminate Outliers When Performing Simple Linear Regression

Simple linear regression is the act of estimating a line that will fit a data set with two variables (x and y). I use it most often to find the alpha of a strategy’s past performance. Linear regression is extraordinarily subject to influence by outliers because it relies on the sum of the squares of the distances from the line. The simplest way to eliminate outliers is to take each set of data and trim all values that are in the bottom or top percentiles. But a more complex way may get you better results. If you plot all the data and it’s more or less linear but has a huge blob in the middle (this is always the case if you’re calculating alpha and beta by comparing portfolio returns to that of a benchmark), the best way to eliminate outliers is to draw a confidence ellipse around the central blob and eliminate data points that are outside that ellipse. Here’s how I do this. I calculate the mean and variance of all the x points and y points, as well as the covariance of the two sets of points. Then for each (x, y) point, I calculate

I end up with a number that’s always greater than zero and usually less than 5.

You can then set a sensible limit for how much data to exclude: I usually exclude any data whose number by this formula is greater than 5.3 (approximately one standard deviation), but most people use a higher limit. I’ve found this method gives a “better” version of alpha and beta than linear regression without eliminating outliers, and it’s a lot easier to calculate than using Theil-Sen estimation, which is what I was using last year. Here’s an illustration of this (the orange points are the ones I’ll use for calculating alpha and beta, the blue ones are the outliers I’ll ignore):

How to Take Advantage of Consistent EPS Growth

Companies whose EPS grows consistently have a strong advantage over other companies. Here’s what I do. I look at the last six quarters and compare them to the same quarters one year earlier. Then I look at the number of those quarters that are higher, without regards to how much higher. (If you’re a Portfolio123 user, the formula is: LoopSum (“EPSExclXor (Ctr, Qtr) > EPSExclXor (Ctr + 4, Qtr)”, 6, 0).) This tally (which will always be between 0 and 6) measures the consistency of EPS growth. It adds a nice soupçon of excess returns to a ranking system.

How to Adjust the Number of Positions in Your Portfolio According to How Much Capital You Employ

Obviously you want to hold a certain number of positions in order to diversify your holdings, but past a certain point (five to ten positions), diversification becomes, in Peter Lynch’s word, diworsification. On the other hand, the larger your holding of a stock, the more market impact you’ll have when you buy and sell it. So as an investor making huge buys and sells in illiquid stocks, I needed to find a formula for a happy medium. Using some regression analysis and some calculus, I realized that maximizing returns means that the number of positions I hold should be proportional to the square root of my total portfolio assets divided by the square root of the median daily dollar volume of the equities I hold. The multiplier I use is 8.6, but that might vary for you, especially depending on how often you buy and sell. For me, if my portfolio is $2.5 million and the median daily dollar volume of my holdings is $125,000, then I want to hold about 38 stocks at a time (8.6*√2,500,000/√125,000). But if my portfolio went down to $1.5 million, then I’d hold only 30 positions.

How to Deal With Losing a Third of Your Money

By March 18 this year, I had a 33% drawdown. If I’d been investing in 2008, it would have been even bigger, but I wasn’t. So for me, this was unprecedented. A lot of investors I knew got out of the market earlier, but not me. In fact, at the end of February I’d taken out a huge cash-out refi and put it all in stocks. Now my losses were bigger than the refi. The entirety of my home equity had more or less vanished. And there was no telling when or where this crash would bottom out or how long it would stay at the bottom.

Here was how I dealt with it.

1. Don’t talk about it. Keep it as quiet as you can. If you talk about it, someone might convince you to sell. Especially if that someone has a financial stake in the outcome.
2. Keep on doing exactly what you’d be doing if the market wasn’t falling at all. Don’t move into any special mode. Stick with your system. Don’t panic and sell. Even if it makes no sense to you to buy stock in a bunch of Canadian mining companies while the market is collapsing, if that’s what your system tells you to do, do it.
3. Keep telling yourself that the biggest gains always follow the biggest losses, and that there’s no telling when they’ll happen. Trying to avoid a large loss by divesting yourself of stocks almost always results in missing the biggest gain. In fact, my biggest one-day percentage gain of the entire year (and my second biggest one-day dollar gain) was the day after I hit rock bottom.

How to Get Great Fills Every Day

Let’s say you want to buy a stock. Here’s what you do. Put in a small limit order before the open at a price significantly lower than the previous close. When it gets filled, follow up with another limit order at the same price (or at the ask if it’s not much higher). If it doesn’t get filled, modify the order so it’s still below the most recent price; if that doesn’t get filled, near the end of the day modify the order so that it’s at the bid; and if that doesn’t get filled, only then put your limit at the ask. At each point, if your order gets filled, follow up with another order at the same price (or a little higher if the ask is close). If you can place conditional orders, that’s even better: your condition should be that the bid is lower than your limit order.

As the price goes up and down over the course of the day, your initial order will usually be filled at a relatively advantageous time, and you can then follow up with further orders at that time. If you were to place your entire order at once, you wouldn’t get the very best fill if the price went down significantly, since your entire order would have been filled at your limit.

If you want to sell a stock, just do the same thing except substitute higher for lower, ask for bid, and so on.

Before 2020, I didn’t trade this way. Now I get much better fills.

How to Use Mean Reversion to Your Advantage

Short-term mean reversion is when the price of a stock that has recently dropped goes back up or one that has recently risen falls back down. This tends to happen more often than not outside of earnings season. But following an earnings announcement, the opposite tends to happen—prices that have risen go up and prices that have fallen keep falling—and that’s called post–earnings announcement drift.

So I wrote a new factor with the following formula: WeeksIntoQ * (Close (0) / Close (20) – Close (0, #Sector) / Close (20, #Sector)). Here’s what it does. It takes the price change over the last four weeks relative to the price change of the sector as a whole and it multiplies that by the number of weeks since the latest announcement. The lower the result, the better. So a stock whose price has fallen by 10% six weeks after its earnings announcement will have a much lower score—and a much higher rank—than a stock whose price has fallen by 10% immediately after its earnings announcement. This factor not only makes me buy stocks that have just had a nice dip without any earnings announcement, it also makes me sell stocks that have had a huge increase in price far beyond their earnings announcement—and those are the kinds of stocks whose next earnings announcements often precipitate a dramatic fall in price.

How to Estimate the Intrinsic Value of a Company

I wrote a long article about this recently, and I’ll refer you to that.

How to Stop Thinking You Can Do Everything

It’s easy: try to do everything and then look at your sorry-ass results. Case in point: A few years ago, after I experienced a great deal of success in creating a complex algorithm for trading microcaps, I decided to create systems for long-term buy-and-hold investing and for mid-caps and large caps. Fortunately, I put those systems into amber, as it were, creating “live strategies” and designer models on Portfolio123 and choosing buy-and-hold stocks for Portfolio123’s stockpicking competition. Now I see the out-of-sample results of those experiments: they’re all dismal. I came to realize I was not terribly good at large-cap or buy-and-hold investing. I can do microcaps and small caps, but I can’t do everything.

How to Gauge a Company’s Volatility

This was also the subject of a long article I wrote. In a nutshell, look at the volatility of the company’s fundamentals, not its price.

The Last Thing I Learned

was not to promise twenty things when I only had nineteen. Sorry I lied in the title of this article.

Happy New Year!

My CAGR since 1/1/2016: 44%

My top ten holdings right now: TRIB, FKWL, SMTS, RNDB, DVD, AVNW, STRT, CRWS, SMTX, CPIX.