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A big thanks to Jim for explaining his LargeCapCash methodology and to AdrianC for explaining the mechanics in greater detail.

Some questions for Jim. I apologize in advance if these questions were previously answered or if the answers were implicit in the original methodology description:

Suppose one has taken initial positions in the forty (40) companies with each position representing 2.5% of the LargeCapCash portfolio. A second assumption is the LargeCapCash portfolio is reconfigured annually.

It is now the beginning of the second year and time to reconfigure the portfolio in accordance to the LargeCapCash rule set.

Situation A:
One (1) company now represents 10% of the portfolio's value. Five (5) other companies each represent 5% of the portfolio. These six (6) companies represent 40% of the portfolio at the end of Year 1. These six (6) companies again meet the LargeCapCash criteria for inclusion in the portfolio's Year 2. Has does LargeCapCash handle this situation in Year 2? Does it sell them back down to 2.5% of the portfolio? This obviously provides cash for buying new companies meeting the LargeCapCash criteria in Year 2. Is this the annual process of rebalancing the portfolio?

Situation B:
Three (3) companies have lost 50% of their original value during the first year, yet again meet the LargeCapCash criteria in Year 2. We assume the method calls for buying more stock to bring those three (3) positions up to each representing 2.5% of the portfolio. Is this assumption correct?


Jim and others, please tell us your thoughts on managing the LargeCapCash portfolio when a few positions have good fortune and run away from the pack.

An additional note:
In pulling together the forty (40) stocks meeting the LargeCapCash criteria, it was interesting seeing several having met previous screens and thus have been previously studied. It would seem managing money in the LargeCapCash portfolio over time may also provide deeper knowledge of many of these companies to where one could put money outside the LargeCapCash portfolio into the better companies when they experience a market swoon. To know them is to love them kind of opportunistic approach. . . . . . .

Thanks in advance for this help,
Uwharrie

[this should probably be marked "off topic", or on the mechanical investing board, but since you asked here I'll reply here--apologies to BRK purists]

The original suggested way to run the screen (by no means mandatory) went like this:
(1) Buy equal dollar amounts of the top 40 ranks
(2) Hold for two months
(3) Calculate fresh picks. For every current position(s) no longer among the top 45 ranks, sell and replace with the highest ranked pick(s) you don't already own.
(4) Go to step 2.
(* see note at bottom)

This is silent on how often you'd rebalance. The two months is the hold period and trading cycle, but the rebalance cycle may be different.
A "rabalance" involves doing all the small trades needed to get all positions back to equal weight.

On a trade date that was not a rebalance date, you'd usually do this: calculate the total market value of the N positions you're selling plus any dividends received since the last trading day, and put 1/N of that cash total into each new position you're buying. So new positions are equally weighted among themselves, but might be a little bigger or smaller than the average of the positions you are not touching, which may vary amongst themselves. This is what I usually do.

The two typical ways to run an MI screen would be to do a full rebalance every single time (lots of small trades, rarely worth the bother), or to do it only every once in a while, out to (say) once a year. Those are both pretty easy to test, given the right backtester.

In reality, it generally doesn't matter a whole lot. The difference in returns is well within the statistical noise of any backtest, and in any case is small. So a totally acceptable approach is like this: if some position has become noticeably larger than the others, trim it back to the average size the next time you are selling and buying, but otherwise ignore rebalancing. I don't have a feel for what constitutes "noticeably bigger", but personally I wouldn't mess with something until it's at least 40-50% bigger than average. With a 40 stock portfolio, that's still not exactly a concentration risk: average position is 2.5% of portfolio, versus 7.2% for SPY which lots of people consider acceptable. FWIW, I have done no rebalancing in the last six months and my largest position in this screen is currently only 32% above the average. Most of these big boring positions track the index (and each other) relatively closely.

In this specific case of LargeCapCash, the backtests show only very slight differences based on the rebalancing frequency. Assuming a fairly generous 0.4% round trip trading cost, if you are running the version which requires a dividend, then the highest returns are seen in the backtest that rebalances every time (every two-month hold period), but rebalancing every 10 months is almost the same return. I do the latter. If you're running the version which does not require a dividend (which has a worse overall backtest return), then the backtest suggests it's best to rebalance annually, not every two months. There is a good chance these differences are just statistical noise. There is also a possibility that it's meaningful: perhaps dividend paying firms mean revert on a different cycle on average (less multi-season trending) so it's statistically not as worthwhile to let the winners run as long? Beats me.

Jim


* Note
Another good way to run this, not previously mentioned in the context of this screen, is pretty similar. This is called the "dozens" approach on the MI board.
(1) Your first day, buy equal dollar amounts of the top 20 ranks with half your portfolio's cash.
(2) Two months later, buy equal dollar amounts of the top 20 ranks you don't already own, using up the other half of the cash.
(3) Two months later, sell all the picks you've held for four months and replace them with equal dollar amounts of the top 20 ranks you don't already own using up all the cash in the account. Typically about 18 of the 20 positions will be held over rather than sold.
(4) Repeat step 3 over and over.
Thus every position is held four months, but it's like two separate portfolios with their trade dates staggered by 2 months. Once in a blue moon you'd want to check to see that both "sub portfolios" are staying about the same size. Though the backtests of this and the "original" version at the top are extremely similar (well within statistical noise), it weakly suggests this variant has a hair higher returns (0.1%/year) and a hair fewer trades per year (17% fewer).

Two months later, sell all the picks you've held for four months

What is the magic of using 2 months and 4 months? Did someone also do a backtest to show that 2 months/4 months showed the best results? Maybe 2.3 months and 4.6 months would be better?

Two months later, sell all the picks you've held for four months
...
What is the magic of using 2 months and 4 months? Did someone also do a backtest to show that 2 months/4 months showed the best results? Maybe 2.3 months and 4.6 months would be better?


Two different types of answer

In general, the traditional MI screens have holding periods ranging from a month to a year. I've tested and used strategies down to 3 days. Many strategies test well at hold periods of a month or less, but a lot of the gain is either spurious luck in the backtest or disappears in the additional trading costs. Personally I don't trade more frequently than once every two months, for a variety of reasons. First, it's just too much work. Second, in real world results I don't think it hurts, because (secondarily) I don't have as much confidence in backtests with higher frequency trading. In particular, since most quant screens do not add value after they are created/tuned/published, frequent trading is just adding drag from trading costs. Even if a screen is totally random, you can generally expect to track an equally weighted portfolio within a statistical window...minus the trading costs. That's why we try to keep the total costs under 1%/year.

Another general answer: many (most?) screens work better as "dozens". For example, as here, divided up into N sub-portfolios each with hold period X months, with trading dates for each portfolio staggered by X/N months, generally ensuring that no stock is held in more than one sub-portfolio. This has fairly low turnover, while ensuring that each purchase you're making is done with the most recent available data. It seems to offer an advantage in backtests, I think for two reasons: keeping the recentness of the data for each new pick, and the fact that you're never doing a whole lot of trades all at once, which can be an issue if it happens to be right at a big turning point int he market (in direction, or leadership). Returns seem to be quite a lot smoother.

In the case of this specific screen, the rate of return seems to roll off for hold lengths longer than about four months, likely simply because of stale data (maybe something had a great ROE when you bought it but the business has deteriorated in the last 6 months...) So the likely suspects for trading would be holds of 1-4 months. I'm too lazy for monthly, so the choices would be 2-4 months. As mentioned above, two staggered portfolios of four month holds is generally preferable to a four month cycle. In backtest, you get even better performance with two sub-portfolios each with two month holds, with a trading day for alternating sub-portfolios each month, maybe 0.2%/year higher returns if the backtest is representative. And simple "top 40 every month" tests at another 0.25%/year better. Since this is really more of a "skewed indexing" investment approach, I don't think chasing the last fraction of performance is likely to be fruitful, and it involves a bunch of typing every single month. If the general strategy beats the S&P by 5%/year over time in real life (highly unlikely, but that's what the backtests show for all these variants with holds up to 4 months) then it is doing more than well enough.

As for whether 2.3 months or 4.6 months might be better, I don't know. First, the difference almost certainly be smaller than the already-large error bars on the tests. There are only so many picks to be had--for this screen, they change very slowly. And second, I don't currently use a backtester with the appropriate data that tests anything other than hold periods that are a multiple of a month : )
As mentioned above, I find anything under 2 months to be too much of a bother for no convincing additional upside, so I'm not really missing much. I can test 2 and 3, and they are extremely similar (difference smaller than the error bars), so 2.3 would be the same.

Jim

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