The idea that investing can be treated as a science that accurately predicts swings in the market and the direction of stocks, bonds and other financial assets has long been one of the primary driving forces behind investment practices.
But after two years of extreme volatility, the ongoing pursuit of investing laws as immutable as gravity continues to be viewed by investors with a mix of intrigue and suspicion.
“All these models that try to predict tomorrow’s moves in the market are often valuable, but investing can never be called an exact science,” said Greg Newman, a senior wealth manager at Newman Group in Toronto. “What moves markets in the short term are investors’ perceptions about what an asset is worth at any moment in time.”
Trying to show that investing outcomes can be determined purely through scientific inquiry is not new. For instance, quantitative analysis that employs mathematical formulas and computer models to value stocks and bonds has been around for nearly a century in one form or another. Over the past 50 years, such tactics have gained significant prominence with hedge fund and traditional money managers.
Karl Mergenthaler, an executive director at JPMorgan Chase & Co., said in a 2007 report titled Man vs. Machine that the goal of quantitative managers or “quants” is to take human emotion out of the investing equation. In doing so, they believe better performance can be achieved over time.
Based on Mr. Mergenthaler’s research of investment returns between 2003 and 2007, however, there is only mixed evidence that quantitative methods are any better at realizing positive returns than doing fundamental analyses that are more susceptible to behavioural biases.
“Interestingly, quantitative strategies have generated better risk-adjusted returns,” he said. “Over longer periods of time, the relative performance of quantitative and fundamental strategies varies widely.”
Chad McAlpine, a quantitative research analyst at RBC Capital Markets, said mathematical models used in his field of finance have a solid track record of accomplishing excess returns, but, like other investment schools of thought, they can be susceptible to extreme events such as the subprime and financial crisis that crippled markets in 2007 and 2008.
“Ten or fifteen years ago, you could run fairly simple multi-factor models and get stellar returns, but when you hit periods that weren’t expected, you would get killed alongside everyone else,” he said.
Generally, that was acceptable, he said, as long as the model performed better than the broader market over the long term. That mindset, however, has changed in recent years, largely because markets have become much more volatile and unpredictable in the wake of the financial crisis.
“People are now looking to come up with a model that doesn’t just outperform the market, but one that avoids those events, those black swans that nobody sees coming,” Mr. McAlpine said.
The adjustment, he said, is simple. He continues to work the same quantitative models, but has incorporated an additional decision parameter that tries to account for periods of distress. “It’s basically, when the market is falling, get the heck out,” he said. “It isn’t perfect in predicting adverse future events, but it is the type of mechanism that can be easily built.”
Other quants, meanwhile, are trying to develop metrics that predict future events before they happen. One of the latest examples is a fragility index created by Dave Berger, an assistant professor of finance at Oregon State University, and Kuntara Pukthuanthong, a finance professor at San Diego State University.
The index measures the likelihood of global stock market losses by identifying periods in which shocks may be more likely to spread across global markets.
Global daily returns or losses greater than 1% are both more common when the fragility index is high, according to the research. However, when the fragility index is high, down days on the market are far more common: The probability of a bad day occurring is 33%, or more than seven times higher than when the fragility index is low.
“The probability of a worldwide financial pullback is highest during periods in which many countries share a high exposure to our factor,” Mr. Berger said earlier this year. “The index may have important uses for policymakers, money managers and, ultimately, private investors.”
That may be true, said Mr. Newman, but he doesn’t have much faith in the index’s predictive powers. He said identifying risks that could cause a market pullback is not all that difficult because risks are always present. But so too are opportunities.
“The art is to accurately weigh the plethora of both and determine which way the scales are likely to tilt given the fundamentals and the technicals,” he said.
Mr. Newman primarily uses fundamental research to predict the direction of individual stocks, asset classes and sectors, but does find technical analysis — a mathematical method of investing that includes charts and various statistical measures such as moving averages that require some interpretation — also quite helpful.
If both the technicals and fundamentals are aligned, you can have more confidence that you have been prudent in your decisions, he said. But, assuming you have assessed all the variables correctly, you can only be right for that moment in time because circumstances will eventually change.
“Successful investing is not about perfection. It is about prudently governing one’s behaviour, which over time leads to more success and less failure.”