Top Performing Companies Welcome Environmental Regulation

Yesterday on CNBC (video here), Tim Solso, CEO of Cummins, Inc. (CMI) was interviewed on Fast Money. Cummins is one of the year's top S&P performers. Mr. Solso had some very interesting things to say about environmental regulation.

"In the 1990's, we saw regulation as a challenge and a problem. But [...now...] we think we're the technical leaders. We invest in key technologies... The tougher the emissions standards and the faster they're implemented, [sic] gives us an advantage. It's a barrier to entry for other engine manufacturers ... Emission regulations are going all over the world ... We're already starting to get ready for the 2014 CO2 regulations with better fuel economy which will benefit consumers. Regulations are a good thing for us and it's a good thing for clean air and a clean environment."

The quote demonstrates that the top performing companies welcome regulation. It's their weaker rivals that are the first to seek regulatory relief. And, the purpose of Capitalism is to sort out and eliminate the weak competitors. Smart environmental regulation is an essential part of the process. Next time you hear politicians complaining about the effects of regulation on "small business," remember that they really mean "weak" businesses.

Back to Cummins, they're stock price history is displayed in the first image (above) along with step-ahead model predictions. The predictions are based on a best-fit model and, in this case, the best fit model is based on secular and cyclical trends in the US economy (the USL20E model). Unlike GM (here), the Cummins stock price is not a random walk.
Over time, there have been periods where Cummins stock was both over- or under-valued. The graph above plots the USL20E model predictions without external shocks, that is, the equilibrium position for Cummins stock price. Right now, at the end of 2010, Cummins stock is about at its equilibrium value.
For the future, the model predicts (above) that Cummins will have a pretty good run at least until 2015. However, there is a lot of variability in the prediction (the dotted lines are the upper and lower 98% prediction intervals), so there is plenty of both potential upside gain and downside loss if you're interested.

Downside Risk for the "New" GM

The new post-bailout, post-bankrupt GM has been generating some buzz on Wall Street this week. Yesterday, its stock jumped 2.8% in premarket trading after investment groups initiated coverage on the new stock. Credit Suisse gave the new GM an "Outperform" rating and a $43 per share price target (the stock was at 36.0 at the end of trading, see above). JP Morgan gave it an "Overweight" and a $44 price target while RBC Capital Markets set an "Outperform" rating. What should we think about this enthusiasm for "Government Motors." Is it just more "Irrational Exuberance"?
Let' s look at what history can tell us. In the time series above I've spliced together the old GM stock price history (MTLQQ.PK) with the new stock (GM) after the IPO in November. The red dotted line displays the one-step ahead predictions for the best fit model [1] to the stock trend. The best-fit model is a random walk, that is, today is like tomorrow except for random shocks! A "Random Walk Down Wall Street," indeed!
That's a surprise result for the world's largest multinational automaker, the engine of growth for the post-War U.S. economy [2]. The graph above shows a plot of the GM random walk model without the random shocks. Until mid-2005 (the beginning of the end?), the stock price did not stray far from it's initial value in the 1960s. What should we expect for the future?
"More of the same" would be a good guess. The graph above shows the random-walk forecast for 2011 with confidence intervals. What this shows is that any stock price between 10 and 50 is probable (within the 98% confidence interval for a random walk). The price targets from the investment groups seem a little more conservative. What's also interesting is that the investment houses don't talk about the downside risk.

My opinion: stocks that are random walks without even some observable drift are best left to the investment houses. Supposedly, the "smart money" can anticipate shocks and trigger events in ways that the average investor cannot. But, what do I know. I'm not even the 800-pound gorilla in the room!

[1] To find the best fit model, I used some statistical techniques to search among various candidate models ranging from business-as-usual models to models based on a broad index of secular and cyclical performance in the U.S. economy. None of these models fit any better than a random walk. The result doesn't mean that at some time in the future I won't find a model that outperforms the random walk.

[2] Some of the choppiness is due to stock splits. GM has had three stock splits since 1950, including a 2-for-1 split in October 1950, a 3-for-1 split in September 1955 and a 2-for-1 split in March 1989. The company has also adjusted its stock after spinning off subsidies such as Hughes and Delphi.

Financial Crises, Income Inequality and a Return to "American Values"

Today, in a CNN interview, Republican Marco Rubio (former Florida House Speaker) said that we need a return to "American values" that encourage the accumulation of wealth. Also, on Fareed Zacharia GPS, Lloyd Blankein CEO of Goldman-Sachs said no one should be "ashamed" to make a lot of money. Finally, it turns out that part of the reason for the Greek financial crisis is that wealthy people in Greece (there are a lot of them) are underreporting (OK, lying about) their income.

I'm, not sure what Mr. Rubio wants to return to, possibly the high levels of income inequality right before the Great Depression and the current Subprime Mortgage Crisis. For Mr. Blankfein, it probably doesn't matter (what's a million or two when you made $53.4 million US$ in 2006). And, of course, the wealthy use their accumulations for the benefit of society, just look at Greece and the US.

Turn Off the Bubble Machine

Today on NPR, Michelle Norris interviewed Lloyd Blankfein, the CEO of Goldman Sachs. When asked what Goldman might do in the future to prevent Financial Crises, Blankfein said "...recognize bubbles." On April 27, NOVA presented "Mind Over Money" asking "Can markets be rational when humans aren't?" Taken together, the Blankfein interview and the NOVA program beg a lot of questions.

First, it's not difficult to recognize bubbles (see earlier posts here). Even if Goldman were smart enough to recognize bubbles (I assume they are since they are the "smart money"), it's not their job. What would they tell the "dumb money"? Sorry, we won't place your bets! What would remain of investment banking? In fact, Matt Taibbi thinks Goldman Sachs is "The Great American Bubble Machine." If we wait for Mr. Blankfein and Goldman to recognize and do something about bubbles, we'll be waiting a long time.

How about the Federal Reserve? The Fed also failed to recognize past bubbles since it is the Fed's job to create growth rather than keep the economy from growing too fast.

How about the short-sellers who recognized the bubble and bet against it? They did their job and made a lot of "smart" money, but short-selling neither created nor defused the bubble.

Are there any other institutions that have the power to recognize and control bubbles? I can't find any and I'm not sure that breaking up Goldman would actually solve anything.

As with a lot of political issues, we probably aren't looking at the root cause. Since, 1990 there has been a sharp increase in the share of pre-tax household income held by the top 1% in the US. In 2005, it had almost reached 20%, the same level it had reached in 1929 before the Great Crash. There's a lot of money being held by people who's only objective is to make more money and Goldman is there to help them out (smart) or relieve them of their burden (dumb).

Even Alan Greenspan thinks income inequality is a problem (but doesn't connect it with bubbles) and the cause appears to lie in US politics.

Wall Street High Rollers on Capital Hill

Today, before the Financial Crisis Inquiry Commission, J. P. Morgan CEO Jaime Dimon and other Wall Street Barons testified about their role in the financial crisis. A few days ago, Mr. Dimon commented that J. P. Morgan's operations are run for clients and "it is not a casino." Although Mr. Dimon tempered his comments a bit before the Committee, me thinks he doth protest too much.

A Random Walk Among the Undead

The efficient market model was pronounced dead over a year ago at the World Economic Forum in Davos. But, Jeff Anderson-Lee, a commentator on Paul Krugman's blog, commented that "the 'efficient-market hypothesis' ... seems harder to kill than the undead."

What's going on here; why won't the theory die? In this case, I have to agree with Robert Shiller, the theory won't die because it's partly true! If you've been following my attempt to forecast the S&P 500, you know that I've produce a number of plausible forecasts for the market future from optimistic to pessimistic. One explanation is that I don't know what I'm doing, another is that the future is unknowable and a third is the efficient market hypothesis (EMH).

What is the efficient market hypothesis? That's a little difficult to present clearly because the concept has become overloaded with multiple meanings. The simplest form of the hypothesis is the random walk hypothesis, that is, stock prices move according to a random walk. The random walk hypothesis requires that the largest characteristic root of a state-space model including S&P 500 prices as the only output variable should be close to unity. For a monthly model I estimate running from January 1950 to January 2010, the largest characteristic root is 0.9986935 or, with rounding, unity.

A forecast with this model (displayed above) shows that our best prediction for the future of S&P 500 prices is the current price, as called for by the random walk hypothesis. Actually, the forecast above was not made with a pure random walk model but rather a random walk with drift model. The pure random walk equation is X(t) = X(t-1) + e(t-1) where e(t-1) is random, uncorrelated error. The random walk with drift is X(t) = a + X(t-1) + e(t-1) where a is the drift term. Supposedly, the drift term invalidates the random walk hypothesis, but even with drift the market is not very predictable.

Interestingly, plotting just X(t) = a + X(t-1) provides a very basic bubble predictor. From 1950 to 1995, a buy-and-hold investment strategy (advocated by EMH proponents) made some sense. No matter when you purchased the stock that tracked the S&P 500, you made money on any sale. After 1995, things became a lot more risky. Stocks purchased in 2000 and sold in 2010 generated huge loses. Buy-and-hold after 1995, in retrospect, wouldn't have made much sense as an investment strategy.

If EMH was restricted to random walk or random walk with drift models, it is a good basic starting point for understanding investment strategies and market bubbles. However, "efficient" should not be equated with "optimal." A casino is essentially a random walk with drift for the house. Where the EMH embraces visions of perfection, it obviously (after the dot-com bubble and the subprime mortgage bubble) goes too far. In future posts I'll struggle with what optimization would mean in the context of the stock market and struggle even harder with the question of whether, whatever the stock market is, it benefits the US economy.

Double Bubble, Toil and Trouble

The NY Times today posed the question "Fed Missed This Bubble. Will It See a New One?" Both Fed chairmen Alan Greenspan and Ben Bernanke famously missed the development of the dot-com and the Great Recession bubbles (even conservative News.max accepts this analysis). So, how hard is it really to identify bubbles?

In an earlier post I showed how feather forecasting from a state-space model estimated up to 1990, showed that the market was over-priced during both the dot-com and the subprime mortgage bubbles. Implicitly, the feather forecast points to a sustainable level for the market but doesn't really display the level explicitly. To do that we need to remove the cyclical components and the month-to-month shocks from the model and run a counterfactual simulation for the stock market. The simulation is displayed above. Rather than reaching almost 1600 in the peak of the dot-com bubble, the simulation suggests that the market should have been at about 600. And, 1000 would have been a better level when subprime mortgage crisis broke.

Why is it so difficult for the US Federal Reserve to identify bubbles? Part of the problem is that one of the major cyclical state variable is the US economy is driven by Fed policy. Another cyclical state variable involves more fundamental economic factors such as housing, corporate profits, oil prices and unemployment. Whether the Fed is counter-cyclical or pro-cyclical will have to be a topic for another posting but clearly the Fed is in the middle of market cycles and has trouble looking outside the box.

And, compared to my pessimistic forecast for the S&P 500, the forecast eliminating the cyclical state variables for the US economy is optimistic. In the figure above, the S&P 500 is back to pre-crisis levels by 2015. The usual disclaimers apply even more strongly to this counterfactual forecast.

At best, financial reform might reduce but not totally eliminate bubbles in the US economy. Since late 20th Century bubbles seem to last about a half decade, the pattern suggests a bubble investing strategy that would differ from the standard diversified portfolio approach--a topic for a future posting.