The biggest issues for the funds were liquidity, capacity, alpha-decay, and out-of-sample performance.
For the first two points, big annual returns on $100,000 aren’t going to move the needle nearly as much as above average returns on $100,000,000. This is not saying that small cap strategies are not valuable, but rather that they do not align to asset managers’ incentive structures. Moreover, the will discount them for liquidity risk and market impact.
The next two points are also related. Anyone these days can produce good backtests and boast of robust out of sample performance. But statistical cross-validation is not what they mean by OOS. They will need to look at real account performance in order to foremost assess the rate of alpha (information) decay using real dollar terms. Moreover, real money results will helps them with the first two points as well.
Personally, I think it would be helpful for your pitch if you could give some additional color around these points. Fund managers may not ask these questions up front, but they will be thinking about them regardless. If you don’t give them that information, they use what they already believe as a placeholder.
I actually agree with this (assuming you have cross-validated the backtest). But it is also a NoBrainer that one would bag what works on the backtest or boost a decision tree.
Not to mention a rational stack here or there. Just a guess but I bet they knew what it meant to “bootstrap” long before I had ever heard of it.
James Simons is a mathematician that…… Well maybe he just looked at the equity curves of out-of-sample stuff that anyone else could see.
Small point. One that I will amend YESTERDAY when someone shares with me what it is—exactly—that those Physics Ph.D.s at Renaissance Technologies are actually getting paid for.
Hmmm. Do you have any insight into this?
Thank you in advance for sharing. But for now I will assume that they do not always wait 10 years for their out-of-sample ideas to show significance (probably).
And I was that first to say that much of this is “jargon.” Jargon that can be heard in the cafeteria of many of the quant funds, I bet. But no reason to think that one would have to talk their language while eating there. Not that I will ever have to worry about how to dress, talk or more to the point: get a job there;-)
You cannot go further but probably explains your interest in Kelly Betting.
BTW, I always figured Simons was secretly using classic finance and discounted cash flows. As soon as I understand this link I will be able to prove it;-)
Anyway much appreciated. And I do not know why I like this stuff so much (and defend to excess). Maybe same reason Kurtis and David do what they do so well (and enjoy it).
Beating IWC consistently using small caps is a heck of a lot easier than beating SPY consistently using small caps. And beating IWC says nothing about whether there’s a small-cap premium or not . . .
I sort of agree with this but really it is hard to say. Some microcaps have spreads of 1-2% which is what? Half of your annual alpha? Plus factor in liquidity risk, wider spreads , greater volatility.
I think companies in the 500M - 5B range are more in the sweet spot.
Liquidity. My slippage estimates are inline. A former client allowed me to run $500K in a 100 position smallcap model for a few months. As well, for the past 4-5 years I have a client who uses P123 run various smallcap systems I designed. The 0.35% estimate is not excessive. Using IB algos we typically get 0.25% slippage and up to 10% of the daily volume. As the lowest threshold for liquidity per stock is $100,000 - you can buy $3mm per day (300 stocks x $10,000) if you strictly keep this equal weight. Median average liquidity is $400,000. With some weighting towards liquidity and accumulating positions over 1 - 2 weeks (avg hold time is 174 days) should allow for $30 - $50mm without too much of an issue. Yes, that will affect the performance somewhat. Dan Rasmussen trades a very focused group of smallcaps and has somewhere around $100mm I believe without too much issue. I can’t recall the # of holdings but I thought I read it was less than 100.
Attached is a chart with the 300 stock model versus SPY.
Anyway, thanks for your comments. This isn’t my first gun-fight as I have consulted with numerous small advisory firms and family offices and even worked side by side with ClariFI’s head quant instructor for 9 months. My point was more along the lines of how to partner up with a smallcap fund willing to trade these strategies. There seems to be a disconnect between what we can design in P123 and larger firms willing to run it. There are many more advisors and such that want to white-label these strategies but few that I am aware of willing to build a hedge fund around it.
Thank you for your comments. There are a few good ideas here.
There was a small cap premium, and in some ways there still is one. So many academics use the market from the 1920s onward in their studies while ignoring some very significant changes that took place recently. The original Fama French study used data from 1927 to 1981 to identify the small cap effect. Why is that important? Because in 1980 401(k) programs came into law and in 1982 stock buybacks became legal. Very few academics even address this step change in market conditions, never mind the continued accounting changes, but I digress. Over time the influx of money into 401(k) funds has largely been invested in index funds weighted by market cap, pushing valuations higher for larger cap stocks. More directly, larger companies have generated more cash flow than small cap companies and they have plowed those funds into buying back their shares, which compounds year after year. I have never seen an academic study address or correct for either of these dynamics. If someone has seen one, please let me know.
So let’s actually run some numbers on the buybacks since it is much easier to quantify than the 401(k) issue. Going back to 1999, an average of 275 of the 500 S&P 500 stocks had positive buybacks, or roughly 55%. By contrast, only 539 of the R2000 had positive buybacks, which at 27% is about half of the ratio of S&P 500 stocks. Over that time period, those positive buyback S&P 500 stocks returned 9.85%, while the R2000 stocks returned 12.29%. If you ratchet up the minimum to 1%, 2% or 3% buyback yield, you see a similar 2+% higher return for the R2000 stocks.
So the small cap premium is alive and well…if you know how to find it.
Set offset periods and length of each run. Great for models with very loose selling rules or perhaps even those with none at all. You can monitor performance over 5 years (and rolling periods) for buy and hold strategies based on your initial buy criteria.
I use it because my sell rules are often looser than my buy rules (unlike the screener). And different start dates can mean different holdings even though there is time overlap. e.g. if you buy on one week reversion and then hold for 6 months, you portfolio offset of 1 week will have every portfolio with very dissimilar holdings despite time period overlap.
My frustration wasn’t directed at you. Just how hard it is to ‘hit the next level’. You’d think that providing free models, offering free work and having years of out-of-sample work and clients vouching for you would be enough to have someone give you a shot for half the wages of the guy who cleans their office toilet. But not so. Just re-thinking what’s really the best strategy here. So far it seems that swirling the toilet wand provides a steadier return with very defensive properties that has low correlation to momentum and value factors (but positively correlated to the # of office workers with IBS).
Kurtis, have you ever thought about getting a certified financial planner degree? and just doing modelling on the side? CFPs always seem to be in demand and it might be easier to break into on the retail side than the institutional side.
When I was taking a graduate level finacne class recently, the instuctor was trying to encourage everyone to get a CFA, but the students themselves said it was an awful amount of work and testing with an unsure payoff. But the CFP was faster out of the shoot.
I started down the CFP route but decided that at my age it would take too long to build up a business. But younger folk could have a good run at it.
The really big incomes always seems to revolve around managing other peoples money directly. And not working for someone who is the ‘front guy’ (who is usually, as Buffett says, more salesman than anything else).
I did take the Canadian Securities course. There are some add-on’s if you want to be a Financial Advisor or such. However, I really don’t have interest in that. I love research and model development and over the years I have had some really good gigs. The goal is to bury my head in the data and keep designing and doing research.
I am actually in Indonesia right now. Previously spent 2.5 years in Malawi Africa. Remote work developing models helps me do that. Really wish WorldQuant would pay at least one-fifth the going rate for quants but they hire 1,000 quants to manage 5 billion. And there are many layers of PMs and staff above them. A real shame that they are hiring an army and paying them so little.
Anyway, this discussion got kind of derailed. It’s time to clean the toilets again.
Isn’t one of the critiques of the current state of capitalism that most of the wealth is going to the top? That would partly be reflected in the largest companies accruing most of the wealth. If true that could explain a weakening small cap premium.
I think that some of us are conflating two issues:
The size risk premium
The size-inefficiency relationship
While both of these are related to size and returns, they have very different implications. The first says that investors can earn a risk premium by agnostically holding small companies. The latter says that there is greater opportunity in small cap stocks, but not necessarily a latent risk premium.
But let’s bring some data to the discussion. This is an analysis of the size premium based on Fama-French data:
Revisiting the above graphs, the most prominent feature is that small has significantly outperformed big in only five distinct time intervals since 1926:
Mid 1930s
Early to mid 1940s
Mid to late 1960s
Mid 1970s to early 1980s
Early to mid 1990s
This I think begs a reversal of the question, “is the small cap premium dead?”
Here is an interesting read that uses an adjusted way to calculate size and addresses critisisms of the size premium. They conclude it’s not dead if you calculate size correctly. Worth a look. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275366
Aswath Damodaran thinks it is “fiction”
He notes that it doesn’t appear in other countries. Maybe it is only in the US markets because US dominated the 20th century and US small caps killed it. What happened to Argentinian small caps? I think the word for that is survivorship bias.
