When looking at the market, it’s always best to understand the environment you’re in. For instance, in late 1999 the tech sector had been perceived as doing well for a number of years. There had been some dips along the way, but they were shallow and brief, and the market had bounced back nicely each time. Confidence increased, and every dip clearly was meant to be bought. Until March 10, 2000, when that all changed.
What happened then? There could be books written about that time, and for that matter, there have been. To me one of the interesting questions for that time is what made the market price change? Why was March 10th the time things changed? I think there are two basic circumstances that happened. One is that news started getting worse. The second is that expectations got sky high, then reversed.
The first part, news, is fairly widely recognized, but still surprisingly interesting to look at. From a pure profit perspective, things never did look good. There weren’t profits, just the hope of a better tomorrow. Some market news factors were growing, be they revenues or eyeballs, and as long as those were getting better, that was good enough. That’s the basic key. I’ve long compared the stock market to the old TV game show “Card Sharks.” The point is less about whether things are good or bad, and more about whether they’re getting better or worse.
Even that approach to looking at whether the news is getting “better” or “worse” is messy, though. Data series vary, and the news for some companies ‘breaks’ before other companies. For instance, while a lot of companies started to look worse in the spring of 2000, a company like Cisco (CSCO) stayed strong until the fall. You can see this messiness right now with jobs data. The headline (U-3) data recently showed a sequential improvement, but the peak was months ago. To make it more complicated, wage growth was weak and a very broad measure of employment compiled by Shadow Stats hasn’t improved markedly for years. So what’s the answer? We know what ultimately happened in 2000, but in a present moment example, what’s the answer?
We’ll know the answer for sure only when it comes and no sooner, just like in 2000, but we can take educated guesses precisely like we did in 2000. In today’s jobs example, I’d say the jobs numbers can vary sequentially (and especially in January, as that’s the biggest month for seasonal adjustments), but the broad trend tends to last. That trend has been down. Could we be changing the trend? Sure, it’s possible, but unemployment is quite low, other lagging indicators are in a similar precarious position, and other measures of jobs are generally less sanguine. Given the weight of evidence, I’d say the likely answer is that the top is in for this jobs cycle, but I’m open to the possibility that this cycle was unusually short and thus I’m wrong. That’s how we think about news.
I think the second part, expectations, tends to get glossed over. What happens when a stock announces good earnings? That depends on what the expectations are. If people expected good earnings, the stock should probably do nothing, as good earnings were priced in. In the 2000 bubble, expectations eventually got unbeatably high.
When an internet auto parts dealer was priced at a higher valuation than the big three automakers put together, what is the expectation and how do you beat that? Similarly, when the upper end of expectations for the economy are arguably not physically possible (arguably 4% GDP growth on an ongoing basis isn’t possible given the current conditions), then how do you beat them? In the current market, when profit margins are around historical highs, debt is at historical highs, and sales (top-line revenues) are tough to come by, how much improvement should we expect?
What do we do with this environment going forward? I think we can consider the past and think about what may happen going forward. Imagine this scenario – a rally that gets questioned but keeps bouncing back, creating a high number of bulls who want to buy every dip. Is that 2000 or 2017? I think that broadly describes both situations, though of course that doesn’t mean we should expect the same results. To take some counterpoints, for instance, 2000 was focused on a narrow band of stocks (tech, media & telecom (TMT)) while 2017 is more broad-based, and in 2000 TMT valuations were significantly more egregious than are broad market valuations today.
What could change the current upward grind of the market? I think the stuff listed above is a start- news getting worse and expectations getting ahead of themselves.
News in terms of actual economic data hasn’t been all that spectacular. Yes, the data started getting broadly better in the third quarter, but it wasn’t any huge improvement. It wouldn’t take much for the data to turn back down. Broadly speaking, in the recent past we started with pretty good data, went down a bit, then went back up. That pretty much describes 2013 to 2016.
More interesting is expectations, because they’ve become sky high. That’s now the bogey that markets are shooting against. Just listen to some of the big sell-side firms and mutual fund houses – are we going to 3.5% on the 10-year Treasury or 5%? To 3% GDP growth or 4%? Are we getting three Fed rate hikes in 2017 or four? If we don’t even hit those ranges, that would be disappointing to companies that would benefit from them and even more disappointing to the investors that are using those expectations.
What you need is a basis to frame expectations. For what we do, that involves modeling, whether it be company modeling or economic modeling. That allows you to frame expectations. It’s also worth understanding that many data points mean revert. There are no real limits to how high stock prices may go, but profit margins operate in a band, as does economic data, volatility, sentiment, and more. Positive data trends don’t last forever; eventually they mean revert.
Along the same analytical path is understanding the difference between correlation and causation. All that modeling would be useless if there wasn’t a connection between our modeling forecasts and the subsequent action of the economy or a stock. I think a lot of people are falling down on this path of thoughtful analysis these days. It’s easy to find correlations- it’s just math. To me, that’s not enough. For instance, you can find a positive correlation between the dollar and the stock market, as we saw last year. Well, except when there isn’t, like in 2009. You have to recognize that superficially rational correlations can change, while some correlations are just plain spurious. For instance, there’s a correlation between the Patriots winning the Super Bowl and the market performing poorly. Is that meaningful and causative? Of course not.
There are a lot of common misdirections and fallacies that people fall for. For instance, stocks can’t go down meaningfully because they haven’t gone down recently (recency bias.) Really? Looking at it in print, doesn’t that seem crazy? And yet, both in 2000 and now there are people talking about how it pays to lever up and stay risky, because that’s how you make outsized returns. Upside Beta (volatility) always wins.
Along those lines, market volatility has been quite low of late. What does that mean? I think it depends on your timeframe. For what is likely to happen tomorrow, it means that volatility is likely to be low. Like so many time series, the best predictor of the next volatility datapoint is the current volatility. Over time, though, volatility mean reverts, so when, like now, volatility is quite low, we should expect that to change at some point in the future. Complacency is obviously high, but at some point that will be wrong. When?
What you need is causation. Earnings generally determine stock prices (on that note, since 2012 earnings are up 2% while prices are up 70%, not a great sign for future gains.) Doesn’t that make sense? It’s also true. A lot of things can happen, but considering causal factors to analyze what can happen is very helpful – instead of saying or thinking something like ‘it just doesn’t want to go down!’
Having a catalyst is always useful, but not necessary. My personal, not necessarily commonly shared, opinion of 2000 is that the catalyst that broke the dam was a large number of internet IPO stock holding unlocks in March. All that potential selling pressure was too much for the internet stocks, and the jig was up. What could it be this time?
I think there are several options, but I tend to key in on expectations as being the dam that is keeping this market up – break the dam and things could get ugly. Expectations seem to think a lot of things will go right – Trump’s policies will quickly be enacted and this will lead to a renaissance of American growth. I’d say that’s expecting a lot, and our job here is to figure out how likely that is and where people are too positive (or negative).
People are great at telling you what already happened, but focusing on what already happened is not a money making strategy. Results, both macro and corporate, have slowly improved over the past few years. The question now is what happens going forward? The market seems to have a strong belief that this is the start of great things. Based on what we see and model, I’d say that’s on the outer edge of what’s possible. I’d say it’s more likely that we’re teetering around a top.
To go back to the original question, what changed on March 10, 2000? I’d say tinder (dangerous conditions – very high TMT stock valuations) met a match (catalyst – IPO insider stock unlocks.) Does that rhyme at all with what things look like now? I see similarities, including people who think my position is absurd. I guess we’ll see.
At the end of long bull markets, bad behaviors can get rewarded – for a while. I’d recommend not being the guy who, at the end of many good years, blithely expects that to continue indefinitely. Investing through the rear view mirror is an easy way to crash.