Features
‘Random Walk' Research Leads to U of G
Save it, invest it or exchange it — following the money is research interest for new economics professor
BY TERESA PITMAN
Why do many lower-income households in the United States save virtually none of their income? In trying to answer this question, economists have developed theories and researched the topic from several points of view. Prof. Alex Maynard, Economics, has weighed in with a recent study that considers one aspect of the issue.
“Some experts in the United States have suggested that programs like Medicaid that provide medical insurance for lower-income households discourage them from saving money,” says Maynard. “The theory is that without Medicaid, they would be more motivated to save for future medical care. Another factor may be that there's sometimes an asset test to get on Medicaid. If people have too much in savings, they don't qualify.”
By comparing savings rates for families in different states with varied Medicaid policies, previous studies found evidence that average family savings declined with Medicaid eligibility. But when Maynard and his co-author re-sliced the statistics by different income groups, they found that Medicaid policies really didn't make a difference for the savings of the lowest-income households.
“The study doesn't answer why they're not saving much money, but it's not because of Medicaid,” he says.
That particular study is easier for economics novices to understand than much of Maynard's other work. He describes himself as an applied econometrician, which means he works with complex statistics designed to analyze economic data and uses them to find solutions and answer questions about economic issues.
This isn't where he expected to end up when he first enrolled in university. Born in Boston, he went to Cornell University, where “they have a less specialized liberal arts structure that requires you to take more diverse courses. Often people don't know their major when they come in.”
In his second year, Maynard took an economics course and found it “very logical, very interesting — I understood things I hadn't understood before.” Economics became his major.
After graduating from Cornell, he went on to complete a PhD at Yale, then worked for the Federal Reserve in Washington, D.C. He eventually realized he was more interested in working in an academic research and teaching setting, so he took a position at the University of Toronto, where he stayed for six years. He then spent a year at Wilfrid Laurier University before arriving at U of G in the fall.
“I was very excited to come here,” says Maynard, who commutes from Toronto, where his wife is a lawyer. “Guelph has a strong economics department — it's a great environment.”
He's also enthusiastic about his ongoing research and enjoys the challenges of econometrics. What's the difference between the type of statistics used in scientific disciplines and the methods employed by econometricians?
Many statistical methods are designed for scientific experiments, where all other influences can be controlled for and where each data point is independent of the others, he explains. In economics, however, researchers can't always conduct randomized studies.
“We can't call the Bank of Canada and ask it to move the interest rate up and down so we can see how other things are affected.”
So studies have to be based on observations of what's going on in the real world, says Maynard, and what's happened in the past can clearly affect what happens in the next round of data. The statistical formulas he uses to analyze the data need to take this into account.
He's especially interested in time series econometrics, studying data that are recorded over time — interest rates, stock returns, currency exchange rates, etc.
“One problem we sometimes encounter with these kinds of data is that they can have a random trend. It's described as a random walk: the pattern of the data on a chart looks like the path of someone who is drunk and walking in a field. This is obviously different from data that show a definite trend or direction.”
One challenge is that it can be difficult at times to tell if the data actually follow a random walk, he says, and careful statistical analysis is needed to distinguish between random walk behaviour and other models.
For example, Maynard applies this to theories about investing in the stock market. One theory, the efficient market hypothesis, says you can't outsmart the stock market. If you know why stocks should go up in price, other people will know, too, and will jump in and buy, causing prices to go up too quickly for you to make a lot of money. If stock prices are higher or lower than they should be, there will be a correction. So the approach for investors who go with this theory is to diversify the stocks they hold and buy and wait.
Another theory is based on the idea that some stocks can become undervalued and that you can discover them by doing research on a company. Buying these stocks would then yield a better reward when the stock rises to its “real” value.
So which theory is right?
Studies from the 1980s and early 1990s suggested that people could predict some of the movements of the stock market based on the market being overvalued or undervalued, says Maynard. That seemed to indicate that value-based investing and market timing would be successful. But recent research he and others have done, analyzing the data with new techniques that allow for random walk behaviour, suggests that the initial ideas about how well the market changes could be predicted were probably overstated.
“It's about using more appropriate statistical techniques to analyze the data,” he says, “and when we did that, we found more support for the efficient market hypothesis than we originally thought.”
He uses similar statistical approaches to study changes in currency exchange rates.
“The area I'm interested in here is the forward premium puzzle or anomaly,” he says. Here's what that's about: with any two currencies, you have both the exchange rate — meaning how they are currently valued against each other — and a forward exchange rate. The forward rate is used by companies that might be planning a big foreign purchase in the near future and want to lock in a price.
“When the market sets the forward rate, it is in a way predicting what it thinks the currency exchange rate will be in the future,” says Maynard. “It might be offering a forward rate that locks in a price above the current exchange rate, for example, because the market thinks it's going up. So it seems as though this might be a predictor of what's actually going to happen to the exchange rates.”
But it's not. “When you look at the historical data, it actually tends to be wrong even about the direction of the exchange rate. When people lock in a higher rate, the rate often goes down. It's very counterintuitive, so that's why we call it a puzzle or anomaly.”
Maynard's research speculated that this result might be simply an artifact of the statistical approach used to analyze the data. With better techniques, he found that although the original statistics were wrong, they got the right result. “It's still a puzzle.”
He'll be continuing these investigations here at Guelph. From his perspective as an American, he believes Canadian universities “are a good deal. You get a very good education and — compared with the United States — for a reasonable price.”
He also notes that in the States, especially on the East Coast, where he hails from, “the best students tend to go to a relatively small number of universities. In Canada, the best students are more spread out, so any school you go to, you'll find bright, interesting students.”