Welcome. You’re listening to the FinTrust Capital Advisors From The Desk Of Podcast: our podcast about markets, life, and things financial. Today’s podcast is an interview with Mary Katherine Glassman and Alan Gillespie, Chief Investment Officer here at FinTrust Capital Advisors. Join them as they review the topic of his Wealth Management Quarterly, Superforecasting.
MK: So, Alan, why title this Wealth Management Quarterly “Superforecasters”?
Allen: As I sat down to write at the beginning of the year, investor demand for market and economic forecast tends to be high and investment firms readily create a supply forecast to meet those demands. These market forecasts are rarely subjected to later scrutiny, and this is unfortunate because most financial forecasts are not very good. Philip Tilloch and Dan Gertner, 2 experts on the topic of forecasting in their book Superforecasting: The Art and Science of Prediction, have repeatedly found that the average forecast is about as accurate as a chimpanzee. And since we all are asked to make financial forecasting in our lives and our investment financial planning teams make forecasts and projections for clients, I’m quite certain that our clients would appreciate that we become well trained and skilled monkeys rather than remain at average chimpanzee levels. So, I thought in this Wealth Management Quarterly we should explore super forecasting, what it involves, and how we might become super forecasters. I also wanted to share with clients how our strategic financial planning and investment processes are designed to improve projections and provide more clarity regarding potential future outcomes to help them in their decision making. And incidentally, the Wall Street Journal for 14 years ran a contest that pitted the darts against the experts and I’m glad to say our 14 years experts actually did add some value, but it was kind of a real time test of this concept.
MK: That’s good to hear. Can you tell us more? What is a Superforecaster?
Allen: So, the professors define a Superforecaster as those that they’ve been able to identify that are consistently ranked in the top 1 to 2% of forecasting accuracy. And interestingly, they found that super forecaster skills stretch beyond even their direct fields of expertise. The Superforecasters seem to have risen above, you know kind of average levels of prediction and foresight.
MK: So, are they smarter than the rest of us?
Allen: No, not at all. And that’s what makes the books and the research around this topic so interesting. The professors have spent decades examining this and what they found is that it’s a skill that can be cultivated. There are certain personality types, though, and analytical methods, however, which are critical to a good forecasting process. In addition, there’s certain conditions which make forecasting accuracy either better or for worse, and statistical terms that the confidence interval around those, and in its own studies on the topic, the Harvard Business School reported that forecasting accuracy can actually be improved by 14% with just an hour of training of business executives on statistical concepts and cognitive biases.
MK: So, you mentioned that some personality traits are kind of they go with better forecasting. So what key personality traits are associated with good forecasting?
Allen: The most predictive was just a commitment to self-improvement, being the forecasters were really committed to becoming better forecasters. So, you know that’s common across any studies of human performance or endeavor, whether it’s athletic or academic or professional. Beyond that, they tend to be open minded people; open to new data, they tend to be curious, they’re willing to research and look for data, they’re focused and above all, they’re self-critical. They’re very humble, and willing to adjust forecast. They do tend to be numbers oriented people. They find ways to quantify information. They tend to be news junkies, not in the sense of just random news, depends what they’re looking at, but news that could influence the outcomes and they’re very open minded when it comes to new information. So, they regulate, update projections, and they have humility to admit errors, and they recognize when things are going off course and they correct their forecast through gradual correction based on the information, not large emotional base changes, right, so. I have a friend, Bruce Yandle, former head of the Clemson’s economics department, he used to jokingly say that he was a good forecaster because he forecasted it often and it turns out there’s a lot of truth in his joke.
MK: Well, I think myself and a lot of our clients would probably call you a Superforecaster. What kind of conditions are necessary for a good forecast?
Allen: The biggest is probably a clear forecasting horizon, right? You want to be able to put things in terms of time. Are you forecasting something to happen within a year or within five years or within 10 years, right? But defining that time horizon really is critical. So, in financial planning and economic forecasting, I personally believe a clear forecasting horizon is probably one of the most important variables, because every asset or asset class has a cycle, they’re going to be times when returns are negative and times when it’s positive and that leads to drawdowns, and there’s times when performance is good. And so, we all know that, and yet I’ve never been in a client meeting or investment meeting where someone says, you know, we really should buy this because the recent performance is terrible, right? I’ve been doing this 20 something years and that’s a very rare conversation. So, when we build models and select investments, we’re interested in these risk questions about how bad can it get, how fast can it get that bad, how long will a recovery take, and where do we think we are relative to cycle? But those are important portfolio questions, but what really determines a client success is do they have that amount of time? Time and the emotional confidence to keep investing through the cycle and in my personal experience with clients over 28 years, those that invest through the cycle tend to do better than those that try and time the cycle. And Bill Kibler in our Anderson Office does a good job of summing this up nicely, that it’s time in the market, it’s not timing the markets that matters.
MK: Can you expand on that a little more for clients?
Allen: Sure. I mean, if you just take daily results, so, the average day stock market has a 53% chance of being up and about a 47% chance of being down. But if you extend that time horizon to just one year, that number goes up to about 67% to 70%, kind of in that ballpark. And if you extend your horizon to 10 years, right, obviously your percentages go up even a little bit more. So, you know your chances of positive outcome, you know, do improve, at least in the stock market as you extend your investment horizon. You know, and historically, you know the example I like to give people is, you know, stocks initially are offering six or 7% returns above cash, and this was a problem we recently had when interest rates got to zero. If the stock market were to crash tomorrow, say you know you bought it at 100 and all of a sudden it’s at 60, in seven years at 6%, you would still be better off than in a cash investment that had earned 0. So, you know, accurately understanding these statistics do change the client conversation because it’s not what will stock market do, but rather do you have at least that amount of time to be in this investment and to see it through and invest through that cycle. So, you know those are the important planning questions, client horizons, life expectancy adjusted for health, things like that are much more important than per se, what that asset will do.
MK: OK, got it. What are some other factors that impact forecasting accuracy?
Allen: You know, Superforecasters try and quantify even things that are soft, what I call soft data questions, right. And so, and they look for clues that others may miss. So, I was recently reading a funny example of this. There was an article that caught my attention because I’m a big Billy Joel fan, and it was on Billy Joel’s fourth marriage. And so, it prompted me to look up the statistics for marriages. And yeah, unfortunately, 41% of first marriages do end in divorce and 60% of second marriages end in divorce. And 73% of third marriages end in divorce and whopping 93% of 4th marriages end in divorce. So, Billy Joel is truly a case of blind hope over experience and in love over logic. What made this story ironic for me is that one of his very first hits back in 1977 was a song titled Movin’ Out so I guess at a subconscious level he knew what he’d be doing at some point in the future and the ex-wives are all fairly worn. So Superforecasters look for subtle hints, and so you might have noticed, hey, he wrote a song called Movin’ Out. Should I marry this guy? Might have been a logical question to ask.
MK: You would think so. What does FinTrust do to improve its clients’ forecasts?
Allen: You know, the first is we heavily discount Wall Street’s forecast because there’s a well-known cognitive bias on Wall Street to overestimate earnings because management teams don’t like to talk to people who are negative on their stock. So, Wall Street analysts forecasts tend to decline throughout the year, so we heavily discount forecasts. You know, we want to use very conservative forecasts because we want things to work out better than we expect, not worse than we expect. We think about a lot of risk control in advance, so we designed well diversified portfolios to weather bad markets and we saw for three market environments, up markets, sideways markets, and down markets and we rebalanced those client portfolios to take advantage of market volatility over time. And an important unique aspect of our rebalancing behavior, however, as we think about inter-class rebalancing different from intra-class rebalancing. So, rebalancing between say, stocks and bonds, we handle differently than rebalancing within stocks, you know stock-oriented strategies.
MK: What does that mean for FinTrust’s investment process?
Allen: You know, over a long period of time, equities offer higher returns than bonds. So, if you’re constantly rebalancing from your stocks to your bonds, you’re effectively selling a high returning asset to buy a lower returning asset. So, you want to do that infrequently. You have to do it from time to time for risk control reasons. But statistically, it’s going to hurt your returns, right? So, you only want to do that if there’s been a real big market move and you need to get back to balance or for risk control policy-oriented reasons. But meanwhile, inside of an asset class, say equity, say international stocks versus domestic stocks being kind of a classic, or small stocks versus large stocks, you want to do those rebalancings more frequently to take advantage of what I call kind of noise. But overtime they’ll have very similar returns, so you’re kind of taking advantage of deviations. Versus you’re staying within that same general return category. So, we do think about those types of rebalancing. For example, in our fixed income portfolios we were pretty defensive, we’ve taken a lot of interest rate risk out now that interest rates are sort of normalized; we’ve gone back to more normal sort of behavior in our bond portfolios.
MK: And my last question for you is we know that biases can impact our thinking. So, are there any examples of Wall Street biases that may play into forecasting.
Allen: Yeah, the big one is the overestimation of earnings, right? You know, just as you go through the year, earnings tend to come down and how that plays out is we’ve actually noticed in the worst market years, they start off down and they go down all year. So, I mean, it’s kind of interesting if you look at 2008, the stock market never traded up at any point in time during the year. If you look at the worst years of the Great Depression, the stock market never traded up at any point in the year ‘73, ‘74, same thing. So, because those numbers tend to come down and sort of cascade on themselves in a bad year, so we pay attention to that and then we try and take advantage of that for clients.
MK: Got it. Any final thoughts?
Allen: You know, forecasting is a hard business, but I truly believe that our clients can have confidence and conviction that our professionals, our processes, and our procedures are aligned with the best practices and core principles outlined in the book Superforecasting: the art and Science of Prediction.
MK: OK. That’s great. Well, thank you so much, Alan, and thank you all for listening. If you have any questions, please reach out and we will see you next time.
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