President-elect Donald Trump’s stunning win over Hillary Clinton was surprising indeed, but was it a black swan event?
Dr. Renaud “Ron” Piccinini, a risk authority and black swan expert, talked with ThinkAdvisor about the election, risk management and the DOL fiduciary standard rule. Basically, the answer is “No,” Piccinini says, a black swan did not take flight with Trump’s election.
Misleading polls and the unknown of whether “non-likely” voters would show up to cast ballots explain why last-minute predictions of an overwhelming Clinton victory were wrong, Piccinini says.
Co-founder of the risk consultancy, PrairieSmarts, the PhD wrote his doctoral dissertation on what are now known as black swan events three years before the bestseller, “The Black Swan” (2007), by Nassim Nicholas Taleb, was published.
Black swans are major, unpredicted events that typically wreak havoc.
Trump’s triumph over Clinton was not unpredictable. “Though the underdog won, a black swan event would have been if a third-party candidate, such as Gary Johnson or Jill Stein, had been elected,” Piccini says.
In our interview, the risk expert discusses why the polls got it wrong, while focusing broadly on more personal financial industry risks: the ones that face advisors as a result of the DOL fiduciary standard rule.
The regulation throws a harsh spotlight on need for risk management of both the client’s portfolio and the advisor’s practice. This entails reducing portfolio risk as well as accurately communicating risk level so that client expectations jibe with it.
First phase of the rule’s implementation begins in April of next year, though in light of Trump’s stated intention to decrease regulation, it might be eased or even stands a remote chance of repeal.
Today’s advanced computing power—especially cloud-based processing and storage – makes financial risk mitigation easier and more effective than in the past. Not long ago it cost millions to determine the risk metrics now available in software tools from companies such as PrairieSmarts. The firm works with advisors to determine and reduce portfolio risk, and how to communicate it to clients.
Before launching his firm in Omaha, Nebraska, in 2012, Piccinini, 39, developed risk models at TD Ameritrade and First National Bank of Omaha. Here are further excerpts from our interview:
THINKADVISOR: Do you believe that Donald Trump’s victory was a black swan event?
RON PICCININI: No, I don’t think it was a black swan event at all, even though the election result surprised some analytics groups.
What misled the pollsters, whom the media and voters were relying on for accurate information?
Polling in today’s age is a lot more difficult than it was 20 years ago – because of technology, cell phones, and so on. But if you look at “enthusiasm proxies,” such as yard signs and rally attendees, Trump clearly had the advantage. These same variables were predictive of Obama’s win. Two previous successful populist campaigns [like Trump’s] in Europe were, for example, Berlusconi in Italy and of course, Brexit.
What’s the reasoning behind the black swan theory, which goes back centuries?
One way of modeling the world is that if all swans in your sample are white, you can infer that no black swans exist because every swan you ever witnessed was white. However, if you observe that there are black horses, white horses, white geese, grey geese and so on, you may think that there are probably swans of different colors. Therefore, you should widen the possibility of [just] how bad things can go and not be surprised if they go there.
How much credence do you give to the black swan theory?
If, using the standard model, you define something that cannot be predicted as a black swan, then it’s a black swan event. But I would certainly welcome an example where you could not have assigned a decent probability to any market price move that we’ve witnessed in the last 50 years.
Supposing a black swan scenario of “how bad” things can go for financial advisors due to the DOL rule, what’s the biggest risk?
Losses in a portfolio that are behind the client’s expectation. With the rule, [not] documenting client expectations [poses a] new risk. Make sure that portfolio losses [would be] commensurate with clients’ expectations. Determine if they’re comfortable with that level; and if not, find a remedial solution.
Are there certain types of retirement investments that are best for FAs to recommend, given the rule?
Higher fee products have their place, but lower fee products are an expedient way to reduce legal risk. The DOL spin is that it’s got to be in the best interest of the client, and can you prove that.
There are several practice risks, including regulatory risk and reputational risk. Clients can take advisors to court, and there’s potential for class action suits. Please discuss the necessity for proper risk communication with clients.
If you lose your reputation and referrals, the next big thing is losing your clients. Then come fines and lawsuits. If you make sure clients are okay with the level of risk taken and you very aggressively document everything, you’re going to reduce practice risks to manageable levels. But if you can’t prove that you’ve had that discussion with your clients or they’re under the wrong impression as far as potential losses go, that’s where you’ll run into trouble.
Many financial advisors are discombobulated about the rule. Many consumers believe that brokers have been doing a tap dance with them because of the suitability standard. How should FAs proceed?
A lot of advisors we’ve talked to view this as an opportunity to shine. They say that now is the right time to change things, if they need to, because they want to provide value for clients. How correctly you’ve communicated risk to them depends on the information you’ve estimated. If you didn’t know certain risks in a portfolio, you [probably] didn’t [miscommunicate them] on purpose. Of course, there’s such a thing as willful blindness, and that could be a problem. But we’ve never encountered it with our clients.
The word “risk” has a negative connotation to many investors. How should advisors explain it?
Risk is not necessarily negative. Here’s the thing: It’s how much risk you take. If you take no risk, standard finance theory says you get no reward. Investing is the art of getting paid for the risk you take. So you need to use the right risk model.
What is quantitative risk modeling, and why is it important?
It’s using a computer to find estimates of risk and what the risk probabilities are in a portfolio in an automatic way. If, for example, you want to find out what reasonable limits we can assign to how much the S&P 500 can move overnight, finding those limits through a computer is quantitative risk modeling.
How, then, can advisors make practical use of quantitative risk modeling?
If you know how much a portfolio can lose in any given time period and are trying to communicate risk to the client by telling them how much their portfolio could lose in a day, a week, a month or a quarter, quantitative modeling removes the vagueness and emotions. A good quantitative risk model is a very effective tool in your arsenal.
What did you specifically study for your dissertation?
I went through the assumptions of the standard asset pricing model and showed that correlations are not constant – they change quite a bit. So you have “fat tails”; that is, the probability of something bad happening is higher than stipulated by the old model, [which states] that nothing is happening too far away from the mean — the idea that you’re never going to run into somebody who’s 50 feet tall.
What potentially horrific events – black swans — might FAs be imagining?
Cyberterrorism, nuclear terrorism. But as far as the market goes, with the aging population and interest rates near zero, people will spend less over the next five years, and the new generation is broke. The job market isn’t that great. People have a lot of debt. There could be deflation and a scenario where the S&P 500 goes the way of the Nikkei index in the 1990s. So if you lose a lot of money in retirement accounts, I’m not sure that’s going to be very good for the industry. A lot of retirement money is in stocks right now.
You’ve said that if traders and investors don’t focus on risk management, pitfalls can lead to career-ending losses. Please elaborate.
At the end of the day, investing is like poker: If you don’t bet, you can’t win. But if you lose all your chips, then you can’t bet. So don’t lose all your chips. If you lose 50% of your money, you have to make 100% back to return to square zero. Therefore, you might get discouraged and not invest anymore.
Classic risk models underestimate risk, you’ve pointed out. Why?
The answer lies mostly in the history of computer power. We stand on the shoulders of giants: When the guys who invented portfolio theory wrote their papers, computing power was zero, so they had to use models they could work with pen and paper. In 2016, we have computational power to get far better risk estimates. There’s no way [originators of portfolio theory] could have done with pen and paper, or even with early computers, the math that’s required nowadays. It’s impossible.
How much has computing power to conduct risk management advanced over the last decade or so?
Back in the day, if you wanted the risk metrics that we can propose right now, it probably would have cost a couple of million dollars. Even 10 or 12 years ago, you couldn’t do a millionth of what we’re doing now. In 2004, when I wrote my dissertation, I used a stack of servers seven or eight feet high and ran them for 16 days. Now we do stuff in the cloud that’s a million times more complex, and we do it in under 250 milliseconds.
How else can advisors to use risk management with clients?
People, on average, spend a lot of time on stock or index selection but not a lot on how much of something they should own. If you want to buy, say, IBM, you still have the question: What’s the reasonable amount of IBM that I should buy if the stock is going up? [To make decisions like that], we’re helping people determine their risk parameters. That’s what separates those who are successful investors from people who aren’t.
I’m curious: You were born and bred in Strasbourg, France. At 22, you moved to Nebraska. Why Nebraska?
It was a black swan event!
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