January 27, 2021  |  Podcast
Can We Talk?
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What do cricket and investing have in common? A lot more than you think. Join William Blair’s Hugo Scott-Gall and Simon Fennell for a conversation with Tim Harkness, sports psychologist and author of “10 Rules for Talking: An Expert’s Guide to Mastering Difficult Conversations,” and hear how his rules can help improve your personal and professional lives—and your cricket game.

Tim’s comments are edited excerpts from our podcast, which you can listen to in full below.

 

In your book you say that talking is very important, and humans do a lot of it, but we’re not very good at it. Why is that?

Tim: We’re good at a particular kind of talking—low-stakes talking, when we find ourselves in agreement. This creates the illusion that we’re better at talking than we really are, because when the stakes are higher or there’s disagreement, the conversation gets dramatically more difficult. That’s when we move out of the zone in which we can simply use our intuition and we need to start applying some strategy.

What role do innate biases play in conversation?

Tim: The athletes I work with often make expert intuitive decisions, which means they’re using heuristics, shortcuts to making decisions. Often, they tend to be accurate. The difficulty occurs when the heuristic is taken out of the context in which it was useful; then it becomes a bias. And we need to use a different form of communication to tackle that bias.

Can that be applied to investing?

Tim: Yes. A significant part of being an investor is the ability to make a judgment. You’re meeting with company managements and trying to evaluate: “Do I trust this person? His intentions, his competence?”

That’s a very human judgment that relies on intuition. It has a complexity that can’t easily be boiled down to a set of numbers.

This is one of the key arguments for why a skilled human investor can beat an algorithm. The algorithm is not capable of making that human trust judgment. But of course, our trust judgments can go badly wrong. This is where we need to back up our heuristics, our intuition, with a more systematic way of making decisions.

Cricket is a highly measurable sport where you’re participating in an activity that is not totally under your control. And you could say the same for investing.

Can you train the athlete to recognize situations under which a variety of elements become the conditioned response?

Tim: One example is the India Premier League (IPL), which uses a cricket format called T20, where the entire match plays out over a very short period of time. The risk scenario can shift dramatically. The player might score a lot of runs; wickets may fall.

Going into the sport, I hadn’t anticipated that, and I had to teach players to respond appropriately. One way of doing that—and I think this applies to many sports—is to anticipate if/then scenarios: “If this happens, then I will do that.”

But one of the players I worked with said that when he was walking out onto the cricket pitch, one of his objectives was to express his inner beauty. What he was saying is that he trusts his intuition.

That makes sense. On the sports field, as we’re subject to pressure, we’re tempted to become more deliberate and more controlled. But in sports, where time is so critical, we’re almost always better off responding intuitively, naturally, and automatically.

Simplifying doesn’t mean we’re making something easier; it means that we’re applying our skill to choose more accurately between a smaller set of options.

Do you want the players to rely on instinct even as the game gets more and more pressured?

Tim: Really great players are intuitively monitoring the intention of their opponents, and they time their actions to occur when their opponents are concentrating on something else—looking down at the ball, for example.

But there’s the interaction between the strategic and the instinctive. The strategic can reduce the number of options the player is considering. In rugby, for example, I worked with a coach who said, “If you’re a player in the back line, you have three options—kick, run, or pass. That’s it.” He doesn’t want players thinking about anything else.

Simplifying doesn’t mean we’re making something easier; it means that we’re applying our skill to choose more accurately between a smaller set of options. I think that’s something that may extend into the business world as well.

How much does that have to do with data? We’re now turning a game into a series of data-driven interactions. Is that making sports more efficient or is human decision-making still very important?

Tim: Different sports lend themselves to data analysis differently. In general, the more frequent, immediate, and unambiguous the feedback from an event, the more you can use data to analyze that event.

The first sport that really introduced the notion of using data was baseball, which involves high-frequency unambiguous events with immediate feedback. The opposite extreme would be soccer, where you have 22 people playing a game for 90 minutes, and on average there are only two or three scoring events in that 90 minutes. That sport still very much depends on human intuition.

A revolution has happened in basketball, where data analysis revealed something that wasn’t discovered for decades of the sport being played. People are going for more three-point shots.

That’s also a nice example of a bias. Nobody likes missing a shot and nobody likes the rendering position, so you only shoot when you get close to the basket. But the difference between a two-pointer and a three-pointer is 50%. So the data tells us to overcome our bias, our reluctance to miss; you’re likely to score more points overall by shooting for three-pointers rather than two-pointers.

Pricing risk isn’t something we as human beings are intuitively good at, so we have to bolster our decision making with strategy and data.

That’s fascinating, because essentially what you’re describing is mispriced risk.

Tim: I haven’t actually used that term myself, but it’s a great one, because you’re trying to weigh risk and reward.

And that’s what we have to think about as investors. Risk is not a negative word. You can take positive risk. And for us to outperform we have to take some risks. We just want to be intelligent about how we do that, and find what we believe are mispriced risks.

Tim: And this is where biases come in. Pricing risk isn’t something we as human beings are intuitively good at, so we have to bolster our decision-making with strategy and data.

Which sport do you think is most similar to investing?

Tim: I’m going with cricket, because you’re continually trying to balance your reward/risk ratio throughout the match, and the price of risk is continually changing.

I was actually once asked, “What is the link between cricket and investing?” Cricket is a highly measurable sport where you’re participating in an activity that is not totally under your control. And you could say the same for investing.

That combination makes it psychologically stressful, and when we’re stressed, our biases start to get tripped, and we need to be careful about what system we’re using to make decisions.

When it comes to risk, we tend to be binary thinkers. We tend to think of high risk and low risk instead of degrees of risk.

Do you think you could take a cricket player in the IPL and tell him you’ve analyzed the data and he’s mispricing risk? Will he listen or will he stick with his immediate decision-making?

 Tim: Some people are fully committed to their approach and they’re not interested in changing it. These tend to be people who are moving toward the end of their careers.

One of the challenges is that when it comes to risk, we tend to be binary thinkers. We tend to think of high risk and low risk instead of degrees of risk. But once people accept that if they judge risk more accurately, they could be responding more correctly to different situations, they start to get interested.

Can you do the same with investors? Can you say, “With full data we can see when your decision-making is challenged”?

Tim: Rick Di Mascio, who is the CEO of Inalytics, wrote a paper called “Selling Fast and Buying Slow,” in which he showed that there are circumstances where investors sell poorly. And in his experience, people are very interested in understanding those areas that they’re not aware of.

In another podcast, we interviewed Tom Ricketts, owner of the Chicago Cubs. And he believes you can’t just assemble the dream team on paper and expect it to work. There is something else.

Tim: I completely agree.

Do you think that there’s still scope for genius, for a team to outperform its potential due to spirit and wanting to win for each other? Does that still matter?

Tim: Yes. Last year I got a call to go to India to work with the IPL, on a team called the Delhi Capitals, which had done so badly in competition the year before that they’d changed their name.

I had a couple of assumptions when I went into this environment. The first was that Ricky Ponting, an imminent former player from Australia they had brought in to coach them, would carry across the dominant, hyper-confident, aggressive personality we’d seen from him as a player.

The second was that working with cricketers was going to be like working with golfers. Golf is a skill-based, high-pressure sport, and a big part of coaching golfers is helping them maintain their technical skill in the face of emotional pressure.

I was wrong on both counts. First of all, Ricky, to my slight annoyance as a South African cricket fan, was an absolutely lovely guy. He was funny. He was kind. He was sincere. He was considerate.

The second thing I didn’t see coming was how important team spirit is in cricket—how important it is for these individual players to feel that they’re emotionally supported by their teammates.

It turned out to be a really big part of the success of the team, which finished joint first in the league.

In this current climate, where we’re sitting in separate rooms, connected only electronically, we have to work harder to build those emotional connections that generally happen around our shared values and shared interests.

When working with teams that are underperforming, what’s your playbook for creating team spirit, improving morale?

Tim: First, I look at what errors are being made, because sometimes they’re obvious ones that are easy to fix. If there’s something we’re not good at, these tend to be repeat errors. A repeat error can feel overwhelming to fix because you think, “I’m just getting this wrong all the time.” But the fact that it’s repeated means that you get to practice improvement again and again. The same applies to our interactions in the workplace.

Something else I think is really important is that we need, as human beings, a chance to talk about what matters to us, about our values. My cousin has a saying: It’s very hard to hate somebody after you’ve met them. When we get close to people, we tend to like them because we’re social beings.

And in this current climate, where we’re sitting in separate rooms, connected only electronically, we have to work harder to build those emotional connections that generally happen around our shared values and shared interests.

 

 

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Glossary

INDICES
The Bloomberg Barclays U.S. Aggregate Bond Index is a broad-based benchmark that measures the investment grade, U.S. dollar-denominated, fixed-rate taxable bond market, including Treasuries, government-related and corporate securities, mortgage-backed securities (agency fixed-rate and hybrid ARM pass-throughs), asset-backed securities, and commercial mortgage backed securities.

The MSCI ACWI IMI Index is a free float-adjusted, market capitalization-weighted index that captures large, mid, and small cap representation across developed and emerging markets.

The MSCI ACWI ex-US IMI Index is a free float-adjusted, market capitalization-weighted index that captures large, mid, and small cap representation across developed and emerging markets, excluding the U.S. The Value and Growth Indices are a subset of the Index that adopt a framework for style segmentation in which value and growth securities are characterized using different attributes. Multiple factors are used to identify value and growth characteristics.

The MSCI ACWI Small Cap Index is a free float-adjusted, market capitalization-weighted index that captures small cap representation across developed and emerging markets.

The MSCI Emerging Markets Index is a free float-adjusted, market capitalization-weighted index that is designed to measure the equity market performance of emerging markets.

The MSCI World Index is a free float-adjusted, market capitalization-weighted index that is designed to measure the equity market performance of developed markets.

The Russell 2000 Index is a market capitalization-weighted index designed to represent the small cap segment of the U.S. equity universe.

Index performance is for illustrative purposes only. The indices are unmanaged, do not incur fees or expenses, and cannot be invested in directly.

TERMS
Alpha is a measure of an investment's return in excess of the market's return, after both have been adjusted for risk.

Beta is a measure of the volatility of an investment relative to the overall market, represented by a comparable benchmark.

Half-life is a statistical measure of the time required for the discrepancy between price and value to contract by half of its starting value. Fundamental value estimates are based on the Dynamic Allocation Strategies team's proprietary research.

P/E Ratio is a measure of valuation which compares share price to earnings per share, calculated using estimates for the next twelve months.

Standard deviation is a statistical measurement of variations from the average.

QUANTITATIVE MODELS – FACTOR DEFINITIONS
The William Blair Earnings Trend Model captures information about short- and medium-term changes in analyst estimates in an attempt to anticipate future estimate changes and stock performance. The score combines measurements of earnings revisions, momentum, and earnings surprise.

The William Blair Valuation Model combines varying metrics used to characterize the relationship between the stock’s trading price and its intrinsic value. By going beyond using only one or two measures, the model attempts to build a more holistic version of a stock’s worth vis-a-vis the market. The score combines measurements of earnings/cash flow based, asset-based, and model-based factors.