To cut to the chase, Gary N. Smith became an independently wealthy multimillionaire by investing in stocks. In his new book, the Pomona College finance professor reveals how smart value investing made that happen.
Key to his strategy is the underlying idea: How much would you pay to own a stock that generates cash every few months for years to come? The answer: the equity’s intrinsic value. The cash? Its dividends.
In an interview with ThinkAdvisor, economics scholar Smith, whose new, often humorous, book is “Money Machine: The Surprisingly Simple Power of Value Investing” (AMA), discusses his stock-screening process, stocks he likes, why he’s not a fan of Amazon.com and the blunders security analysts make, among other investing matters that advisors — and their clients — should know about.
A specialist in writing about statistical pitfalls, Smith, 71, received his Ph.D. in economics from Yale, where he taught for seven years. He has authored numerous papers, including “Do Statistics Test Scores Regress Toward the Mean?” (1997) and “Would a Stock by Any Other Ticker Smell as Sweet?” (2009).
Among the several books he has written is “Standard Deviations: Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics” (2014), which Nobel Prize-winning economist Robert J. Shiller called “a very entertaining book about a very serious problem.”
“Money Machine” features analyses and applications of the stock-picking models of John Bogle, Shiller and John Burr Williams.
Smith is now at work on a new book, “Artificial Unintelligence,” all about algorithmic trading.
ThinkAdvisor recently chatted with the prolific professor, who was on the phone from Claremont, California. The debated existence of a current market bubble is one issue on which he opined. Here are excerpts from our interview:
You write that the two main drivers of the stock market are companies’ profits and the interest rate used to discount profits. That makes it sound so easy: Just keep track of those two.
It is easy. It’s not rocket science. One of the problems is that we have human emotions — fears, hopes, greed.
How do those enter the investing picture?
Because of those emotions, the things we do aren’t completely rational, and that’s what makes us trade too much. When a stock price is going up, people want to buy it, which, of course, is the wrong time to buy. In the 2009 crash, prices were way down, and people said, “I’ve got to get out of the market before I lose more.”
But not if you’re a value investor.
Right. If prices go down, that’s the time to be buying.
Is that why you’re a value investor?
It’s very hard to predict short-term price movement because it’s hard to predict surprises. Most of the people who try that do poorly; the ones that do well are mostly just lucky. So it’s not a sound investment strategy.
You’ve written books about how to invest, and you’re a professor of finance. With all that expertise in investing, why aren’t you independently wealthy?
I am! I started with nothing and now have literally millions of dollars. The only reason I keep working is because I have a fun job.
You write that human emotions and animal spirits create potentially profitable opportunities for value investing and that that’s “the essence of value investing.”
Yes. When people base their investment decisions on human emotions, it’s probably not going to turn out very well. Don’t let your decisions be ruled by greed and animal spirits. Just think of stocks as a money machine: What’s going to give me money year after year, and what would I pay to get it? Stop trying to predict the price for tomorrow and the next day. Instead, think: “I’m buying this stock for the long haul, and I’m going to be happy with the dividend it’s paying.” It’s as simple as that.
You recognized the dot-com bubble early on. Is there a market bubble now?
Not at all. A bubble occurs when prices are going up because people believe they’re going to keep going up. If prices are going up because stocks have good earnings and good dividends, like now, that’s not a bubble. That’s a rational reaction to good economic news.
You say that the true benchmark for gauging investment ideas is “how tomorrow will differ from what others expect.” Please elaborate.
When you’re considering announced earnings, don’t think: Are earnings higher than they were last year? Think: Are earnings higher than analysts expect them to be? It’s earnings surprises that cause stocks to jump up and down.
You say that Warren Buffett views stocks somewhat as “disguised bonds.” And you agree.
If you look at a bond as: Here’s a bond that pays a dollar coupon every quarter for 30 years. What would I pay to get that dollar coupon? In the same way, you can think: “What would I pay to get this stock that pays a dollar dividend every quarter — and dividends also go up over time. What the stock is really worth is what the cash flow is worth. What would you pay for that amazing money machine that gives you money year after year? You don’t have to try to predict price fluctuation. That’s good because you can’t predict short-term price fluctuations.
Various personality types perceive things differently from one another, you point out. How does that relate to investing?
When an event occurs, some people will react one way and others will react a different way. So getting a handle on who you are is often the key to making better financial decisions. If you’re trying to save for your retirement, say, knowing your own personality type is the first step because if you haven’t realized how hard it is for you to save, you shouldn’t set a rule to save $100 a month — since you’re not going to do it.
As a value investor, what are your criteria for picking a stock?
The starting point is that it has to be a good company. The first place I look is Fortune magazine’s annual list of the most admired companies in the U.S. They survey tens of thousands of executives throughout the country, as well as financial analysts. The companies that top the list are sound, very well managed and have great products. The top five for 2017 are Apple, Amazon.com, Starbucks, Berkshire Hathaway and Disney.
What do you examine next?
Profitability. Google and Apple are very profitable. Amazon is a great company, but it’s not very profitable. So I want not just a great company — I want one that’s profitable. I look at their earnings and P/E ratios. Then I want to know their dividends compared to the price. Apple’s dividends are about 2% of the price; so even if Apple doesn’t grow at all, you still get a 2% return — and then add the fact that the company will grow over time. Even if it grows by [only] 5% a year, it gives you a return of 7%. That’s a great value investment.
Must the stocks pay dividends for you to invest in them?
I like Google even though they don’t have dividends. Their earnings have been growing fast. They’re fabulously profitable, and they’re a great company. I visited with them and was very impressed by the smart people that work there.
When is it appropriate to sell a value stock?
When it turns out that the company isn’t as good as you hoped it would be, and the earnings and dividends have disappeared. But you need to give yourself a margin for error. For example, if Apple talked about dividends growing at 15% a year and they went up only 5%, would you still be happy owning the stock?
You write that growth stocks are a risky trap. Why?
All the great value investors — like Benjamin Graham and Warren Buffett — have always been leery of growth stocks. The problem is you’re not buying things that are here and real. You’re buying hopes and dreams, and what the company is [expected] to be like 10, 20 or 30 years from now. That’s risky.
But Warren Buffet recently bought Apple. Isn’t that a growth stock?
He changed his mind about Apple when he realized it was actually a consumer company and not a growth stock. It’s got a great brand, a great product, customer loyalty, solid earnings. For some, that makes Apple less attractive, but for value investors, it makes it more attractive. The company generates so much cash every year. So Apple has definitely turned into a value stock from a growth stock. You’d buy Apple because of all the cash it generates — all the earnings, dividends, stock buy-backs. It’s a money machine.
Let’s get back to Amazon: You said it isn’t very profitable. Please explain.
Some years their earnings are up, but some years they barely break even or actually lose money on their core business of selling products at ridiculously low prices with a free return policy. And they’re getting bigger and bigger: Now they’re also producing movies. So how can they make money? They’re not. [Except] the thing they’re making some big money on now and are profitable with is their cloud data storage business.
You’ve written a great deal about the concept of regression to the mean. Why is that important for investors to understand?
Regression to the mean is that things are never as bad or as good as they seem. When people overreact to bad or good news, it makes stock prices fluctuate too much. Regression to the mean teaches us that good news is temporary; and so, don’t overreact to it, and that bad news is temporary — and don’t overreact to that either.
What else can we learn from it?
That there’s an element of happenstance — stock prices don’t go down because the almighty computer or God wrote a program that prices should go up or down. It’s the unrelenting [cycles of] good or bad news that eventually turn. But people have a tendency to extrapolate current trends.
Are there other ways that regression to the mean can show up?
When analysts make forecasts for corporate earnings, the ones that are the most optimistic are usually too optimistic, and the ones that are the most pessimistic are usually too pessimistic. When reality sinks in, typically the stocks that analysts are most bullish about end up doing worse than the stocks they were most bearish about.
You foresee more flash crashes like the one in 2010. Why, and what effect will they have on the market?
What has happened and what will continue to happen is that people will be overly impressed by big data and make trades based on coincidental correlations that their computers find — and they will pay a price. But computers don’t really understand what they’re doing. They’re just given instructions — numbers; and they don’t know what’s behind those numbers. They’re just following patterns that they uncover. The problem with patterns is that they’re often coincidental.
Is that what happened in the flash crash of seven years ago?
Yes. When prices started going down, computers began trading like crazy — mostly selling. Prices kept crashing; they called a time-out on the markets, and then prices recovered.
Nothing had happened to the intrinsic value of these companies during that time period. It was just computers blindly following rules.
But why did everything go haywire?
Nobody knows what was in the software to cause that flash crash. There was no common sense to it because computers don’t have common sense. It was just computers operating on rules, patterns and coincidences that they uncovered in the data. So, if a large number of computers are programmed in a similar way to look for similar things, they could all easily be selling at the same time or buying at the same time, causing prices to go crazy for no real reason.
When desktop computers were introduced in the 1980s, many in the older generations were too intimidated to buy one. Now, most people can’t do without a computer, or certainly a smartphone. Is this progress in relation to investing?
The kids of the 1980s have grown up thinking that computers are infallible. But computers aren’t smart. All they can do is very fast calculations and very fast data retrieval. They don’t know what words mean. In the book I’m now working on, I show examples of stock prices being highly correlated with five variables, like the temperature in Bozeman, Montana. The computer can’t reason: “That doesn’t make sense,” whereas a human would say, “That’s rubbish.” Therefore, when it comes to the potential for flash crashes, we’re bound to have trouble because computers are bound to buy or sell based on rubbish.
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