According to a famous Warren Buffett quote, “Rule No. 1 is never lose money. Rule No. 2 is never forget Rule No. one.” And according to me, “Rule No. 3 is never mistake cute folklore for stock-market reality.” Assuming we agree on Rule No. 3, let’s tackle the real question: How much can protect ourselves against big losses without pretty much forfeiting an opportunity to earn reasonable equity returns?

Getting Real About Losing Money

Rule No.1 and Rule No. 2 sound so macho, and so much cooler than reality. Sadly, we live in the real world, where if you have equity exposure, you cannot eliminate the risk of losses (unless you invent a time machine that lets you zip ahead, check stock future prices, and then return to the present without losing your notes along the way.

Stocks go up. Stocks go down. We make money. We lose money. The idea is that netting it all out, we can come out far ahead by being invested, as has worked out in the past for those with reasonable time frames. There has been and will likely continue to be an upward bias in the big-picture equity trend based on growing population, improving education and health care, growing economic activity, rising profits resulting from ownership of productive assets, and in the end, rising stock prices.

But being that the equity market is, in essence, a free market in which prices are set by supply and demand rather than administrative fiat, we have to assume short and intermediate downward moves as economies continually adjust, leading profits to continually adjust, not to mention the countless ebbs and flows of ongoing business and investment-community sentiment.

So how big is too big?

True or false: A loss of 50% is too big, way too big.

OK, I’m sure you knew that was a trick question. Let’s try this:

What’s worse: (a) a temporary paper loss of 70% that by the time you sell winds up a 5% gain, or (b) a realized (permanent) loss of 10%?

Advocates of minimizing Maximum Draw Down (Max DD, the statistic that measures peak-to- trough declines) probably answer (a) as a matter of reflex. And I get it; you don’t know it’s temporary at the time. When you see it in your portfolio, you have no way of knowing it’s only temporary.

Hopefully, you’re scratching your head after the last statement. Do we really have NO way of knowing? If you’re a pure quant, yes, that’s the case and it’s pretty frightening. So I completely understand where they’re coming from when it comes to Max DD. But if you can work with fundamentals, you may not know for sure (the future is never knowable), but you can work with some well-established (through theoretical understanding and empirical research) probabilities.

A focus on Max DD, with it single period-specific chaotic nature often driven by transitory emotion is not going to produce anything worthwhile for us. Instead, I’m going to explore big-loss exposure in two ways.

1. Maximum losses within a portfolio within a series of consistent time periods (for rules-based strategies such as I use, that will be the intervals between re-running of the model and rebalancing of the portfolio) in comparison with the experience of a benchmark portfolio
2. The experience of large realized losses over the life of the portfolio

Neither measure is as clean and simple as Max DD. But the two-prong approach I’m using gives a better sense of potential dollars-and-cents experiences and perhaps even more important, how well my strategy can potentially do controlling losses based on things I can control (i.e. the big-loss exposure that results from the my strategy’s stock-picking capabilities, as opposed to uncontrollable market movements).

Putting This Into Practice

Earlier today, I posted a 20-stock S&P 500 strategy for controlling large losses. (You can track it live as a Portfolio123 free Ready-to-Go model.)

Let’s start by looking at the maximum per-period losses. Since this is a model that refreshes-rebalances weekly, we’ll look at each one-week interval during the backtest period, which started 1/2/99 and ran through 9/21/15.

Figure 1 shows the plot, for each week during the period, of the difference between the worst performing stock in the portfolio and the worst performing stock in the S&P 500 according to a backtest of the stock screen I used as a basis for the portfolio strategy.

Figure 1

It’s a bit hard to see each data point, as might be expected considering that there are 872 observations. But it’s easy to see that the vast majority of the differences are positive numbers, meaning that in most weeks, the worst preforming stock in the portfolio was better than the worst-performer among all S&P 500 constituents, and that the differences in favor of the portfolio were often considerable.

Table 1 presents a summary of the data.

Table 1

There is, of course, a tradeoff. Based on the notion that extreme fundamentals lead to extreme performance (discussed in the 9/21/15 post), this strategy defined fundamentals in terms of a Quality raking system and eliminated not just the poorly ranked stocks, but also the best stocks, those ranked 90 and above. Table 1 conforms that we did, indeed, receive quite a bit of downside protection as a result of having done that. But Table 2 shows the tradeoff, the extent of upside potential we forfeited.

Table 2

Is the tradeoff worthwhile? The initial response is necessarily that it’s a matter of individual preference. Actually, though, we can go further. We can look at overall results, which reflect not only these mainly unrealized extreme gains and losses but also everything else (gains and losses that were ultimately realized after varying periods, and, or course, the performance of the majority of stocks that were between the extremes. That’s encompassed in Figures 2, 3 and 4.

Figure 2

Figure 3

Figure 4

Let’s finish with consideration of our experience with worst-performing realized losses. That’s summarized in Table 3., which compares the portfolio experience with that of a a hypothetical portfolio of S&P 500 stocks (the latter of which experienced quite a few realized trades due to bankruptcies, mergers, and index composition changes).

Table 3

Conclusion

If Max DD is your measurement of choice, the strategy presented here is a dud. During that singular brief interval during late 2008, the portfolio had a loss (mainly a paper loss of 67.35%, versus a draw down of 55.19% for the benchmark. Even the secondary drawdown, a quick market hit that occurred during 2011, the portfolio showed a deficit of 12.09%, trivially worse than the 11.91% benchmark drawdown.

Speaking for myself, and mindful of how quickly the market and most stocks snapped back from those events, and considering Tables 1 and 3 which give a more systematic picture of genuine exposure, coupled with the return information in the figures, I’m fine with the strategy.

By the way, the strategy described here is not a one-shot reverse-engineer-it-if-you-can magic formula. There are countless other ways to accomplish this. It’s a matter of developing your ideas with respect to the general notion that extreme outcomes (including the dreaded extreme losses) are associated with extreme fundamentals, and that as we apply this to a narrower more investable slice of the market, we can get away with moving our ideas (factors) off dead center and giving them a general, but not full out, upward slant.