Daily Fintech Conversations

Any tools out there that I can use to test my small cap hell theory?

My theory - that valuations of publicly listed stocks rise as the market cap goes over $2bn. I have observed this from years of investing and it has a logical premise - only funds designated as small cap can invest below $2bn.

Would love a tool that would enable me to test that in volume. Basically look at all stocks with a market cap around $1.9bn and see if once momentum takes it over $2bn. Or another way would be to take a basket of comparables and compare PSR and PE for stocks under $2bn and over $2bn.

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Hey @BernardLunn I think some of the reasons behind this is investment guidelines which Institutional investors think/believe are smart to follow. Some of these are actual legal guidelines, like mutual funds are not allowed to invest in a company until it has reached a certain size.

I forget the exact detail, but when I traded for Fidelity, there was some rule and the number 5 is stuck in my head. So It may have been $50 million revenue, or market cap, or something else. This prevents funds from investing in penny stocks.

There are other guidelines like “we dont invest in seed stage” which I have heard way more than is pleasing. This also makes little sense, because these investors are seeking high returns, but dont want to take risk. Which is like looking for gravity that makes you float. It does not exist.

I can easily see an unofficial guideline with thinking like, “If it is over 2 billion in market cap, and we need to dump, there should be enough order flow for us to unload a rather large position”. or some other idea about Risk decreasing after hitting that threshold. Keen observation any which way.

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thanks @KarmaCoverage. I get why funds have to cover stocks above $2bn and have observed the small cap hell theory “anecdotally” in a few shares I have invested in. I am looking for the tools to do some backtested quant analysis. @Prathamesh is this in you neck of the woods?

Regarding actual tools, the one that i am wanting to learn the most, which has all the data and capability to do quant analysis is Wolfram Language https://reference.wolfram.com/language/guide/Finance.html

You will need to select the time frame you are interested in and take into account the problem of the DOT Com boom time. Then, the biggest question is which market. The US? Then you may want to start with a specific segment of the market. REIT OR UTILITIES.
Hedge funds have been exploding momentum of smal to mid caps for a long time.
HiddenLevers.com or Morningstar can give you a selection of target Stocks today.
Lastly you may want to adjust your return findings with the index return during the same market cycle.

Thanks @andrefassler very helpful. Hidden Levers looks interesting. If this democratizes what Hedge Funds have long done its an interesting trend. I am just being a guinea pig for that democratization.

Sorry I’m responding rather late to this, missed this notification. I’ve tested market cap based portfolios, and haven’t found any consistent trend, the valuations also tend to vary based on sector and the “theme” at the time. For example tech stocks 96- 2000, financials 04-08, etc. I did find that companies that are otherwise the best among their peers, get a boost when they hit major thresholds in market cap- $1b, $2b, $10b…

This would be too complex to test on my webapp Tradestream, but very easy to track for today’s valuatiions.
I created 2 watchlists-
one containing all Technology stocks with a market cap close to 2b but just below it- $1.8b - $2b

one containing all Technology stocks with a market cap close to 2b but just above it- $2b - $2.2b

At a glance, it does appear that PS ratios for companies above $2b are higher than the smaller companies.

Thanks @Prathamesh very helpful. Do you know of any tool I can use for backtesting (even if that is not focus of Tradestreaming)?

Most tools have an easy way to generate signals and test signals on single stocks, but you can put together a portfolio from it in virtually infinite ways depending on position sizing, risk management criteria. No tool provides the granular level of detail, and if I’m going to code it like in Quantopian, might as well write the entire code independent of a platform.

I ended up using a back tester I wrote in Python for testing financial statements/ quantitative strategies. For fundamental metrics based backtesting, I haven’t found any product good enough, tried out Quantopian, but its a hassle to use. For technical analysis based / simple strategies I bought Amibroker, its one time ~$300, but worth it. Data from Quandl & Yahoo Finance.