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Welcome to

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the MoneyTree Investing Podcast.

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Stock Market Wealth, Personal

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Finance, Val you stops, invest

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in your life. Hello,

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smart money to your podcast listeners. Welcome to

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this week show. My name is Kirk Chisholm, and I I'll

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be your host. So

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today, we have Annie Duke on the

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show. How are you doing today, Annie?

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I'm doing pretty well. How are you? Good.

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Well, glad to have you in the show. I'm kind

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of a fanboy for your books. I think they're just

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really phenomenal at getting

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to the heart of these mental model

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concepts that I think are really hard for people

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to grasp and you do it with such eloquent

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stories. So maybe you could tell the

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listeners a little bit about your back ground

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and how you got to where you are now?

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I started off my adult life as an academic.

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I was studying cognitive

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science at the University of Pennsylvania. I did five

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years' worth of graduate work there with the

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intention of becoming a professor. But

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life had other plans for me. I got

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sick in the last year of graduate school and actually

0:58

ended up in the hospital for couple of weeks.

1:01

And it happened to be right when I was supposed to go out

1:03

for my job talk, so it became clear

1:05

that I needed to take some time off in

1:07

order to heal up.

1:10

So took a leave of absence, you

1:12

know, it's planning to go back after a year

1:15

to sort of button everything up and go back out on

1:17

the job market. But was during that year

1:19

that I frankly just kind of needed money

1:21

because I didn't have my fellowship. I was taking

1:23

a lead from school, and so I didn't have an

1:25

income source. And I started playing poker during

1:27

that year. That well, this is fine. I'll do

1:29

this in the meantime in order to support myself.

1:32

And I ended up finding success pretty

1:34

quickly and kept going and did

1:36

it for eighteen years. To some people

1:38

know how kind of weird that was at the time

1:40

this was in the nineties before Poker

1:42

was on television or

1:45

on the Internet. And so at

1:47

that time, people really

1:49

kind of thought about it as, you know, what

1:51

I would generally go under, is it sin

1:53

or an addiction or that

1:55

category. So most of

1:58

my conversations when people heard I played poker

2:00

ended up somewhere in the gambler's anonymous.

2:03

Have you thought about getting more synonymous? And I would try

2:06

to explain it's like investing, but,

2:08

you know, it was just a very strange thing

2:10

to do. The reason why I got introduced to us

2:12

is because my brother was had already been playing for ten

2:14

years and, you know, supporting himself

2:17

with that endeavor. So anyway, I started

2:19

playing poker. I did pretty well at it. I

2:22

won a world championship and

2:24

the tournament champions and the NBC

2:26

national heads up champion. Chip, at some point,

2:28

I was the winning women in the history of the

2:30

game, not anymore. But then again, I did

2:32

retire in two thousand twelve, so there's been ten years

2:35

for people who'd be. In two

2:37

thousand two, when was about eight years into

2:39

when I was really playing poker exclusively

2:41

at that point. I got asked to come speak to a group

2:43

of options traders. About

2:46

how poker might inform their thinking

2:48

about risk. I gave a talk

2:50

about how your risk

2:52

attitudes get distorted by whether you're

2:54

winning or losing in a game,

2:57

which is more kind of emerging of the

2:59

sort of behavioral science, behavioral economics

3:01

with sort of what you see at a poker

3:04

table. And when I get

3:06

in that talk, I kind of remembered a few things. One

3:08

is I really love teaching. And so

3:10

I really got energized by that one to

3:12

get back to it. And the other is that also

3:14

love academics. You know, I had laughed

3:16

for a variety of reasons and found something else that

3:18

I was really good at. You know, but that

3:20

sort of diving into those types of conversations

3:23

was really fun for me. So started

3:25

developing a speaking career where I was

3:27

speaking on this sort of intersection between

3:29

cognitive psychology and

3:32

poker eventually started getting asked

3:34

to do some consulting with sort of trying

3:36

my hand at that to see what I would think about that.

3:38

And in two thousand twelve, I

3:40

retired from Poker in order to focus on

3:42

that full time. As well

3:45

as wanting to found this

3:47

nonprofit, the alliance for decision education

3:49

where we're trying to bring decision into

3:52

every K through twelve classroom. I did that in

3:54

two thousand fourteen. And then really

3:56

wanted to write this book thinking in bats, which

3:58

was essentially a book that was

4:00

sort of a compilation of the talks that I'd been

4:02

given that I really wanted to be

4:04

able to get out to the world in broader

4:06

form And I did that and

4:09

that got published in two thousand eighteen, and then

4:11

I followed it with how to decide and

4:13

now quit, which just came out in October

4:15

of twenty twenty two. So

4:18

just started really thinking about, like, writing in

4:20

that space, which I think was this way for me

4:22

to get back to academics

4:24

in a way that, like, I really loved and I really

4:26

enjoyed And now, I've come

4:28

full circle because I teach Executive Ed

4:30

at Wharton, so I'm back at the University of Pennsylvania.

4:33

I do research with Phil Teddlock and Bart Miller's

4:35

in psychology and also Marie Schweitzer, who's

4:37

ever at wharton, on a variety

4:39

of different sort of things under, you

4:41

know, judgment and decision making. The work

4:43

that I do specifically was felt teleconference in

4:45

forecasting, and I recently

4:48

enrolled as a graduate student

4:50

there to finish my PhD. You

4:52

obviously have a wide range of things that you've done.

4:54

And I wanted to touch on one of them you mentioned,

4:57

which you remember playing poker. And

5:00

what I wanted to ask is, at a high

5:02

level, like, how does Poker relate to

5:04

investing? To me in your books, it seems

5:06

you illustrated very well as to, you

5:08

know, why it's important, but maybe you can share that

5:10

with the

5:11

audience. Let me just start with this. Poker

5:13

is investing as trading. So

5:16

you're down the hand and you

5:19

have some model of the world. In this case,

5:21

you have a model of your opponents

5:23

at the table, and you

5:25

understand statistically what

5:28

the probability is that that hand is gonna

5:30

win on its own merits or

5:32

not given the number of people that are in the

5:34

hand or the number of people that are at the table.

5:36

And then you're figuring out things

5:39

about what's the probability? I could bluff

5:41

and still win so and so forth.

5:43

So what you're really doing is a forecast at that

5:45

time. It's very similar to

5:47

a thesis. I have this hand. I know certain

5:50

things about this hand. I have a model of my opponents.

5:52

And the question is if I invest the dollar here?

5:55

Do I feel like I'm getting a positive return?

5:57

And I'm thinking about what my expectations

6:00

are for what I might see in the future, which

6:02

would be akin to, like, fundamentals. You

6:04

know, there's also the interestingly enough, there's things

6:06

that have to do with the macro environment that you're in,

6:08

which would be broadly, is it like a loose game or

6:10

a tight game? Which is

6:12

a similar way to think about those things.

6:15

Yeah. So then you're making a forecast of

6:17

whether you think the hand is worth playing. You're

6:20

making predictions about how your opponents

6:22

are gonna behave. You're tracking

6:25

the new cards that get turned

6:27

face up, which is the influence of luck

6:29

on the outcome. And

6:32

then very much just like investing,

6:35

you either win or lose the hand But

6:37

on that one in duration, it's

6:39

not particularly informative. So

6:42

you have some of the same challenges in terms

6:44

of closing feedback loops that you have in

6:46

investing, which is

6:48

that over the long run,

6:50

your outcomes are gonna tell you a lot about

6:52

the quality of your decisions. But in

6:54

the short run, it's not. Alright, I

6:56

lost a hand, but why? Did I lose

6:58

it? Because I played poorly. Did I lose it? Because all

7:00

those judgments that I made were wrong. Did I overestimate

7:03

the chances I could bluff? Did I have poor

7:05

model of my opponent? Those kinds

7:07

of things that would be like a judgment issue.

7:10

Or was it just that I got bad luck? Because

7:12

there's volatility. And I think that

7:14

that creates this very unique problem

7:17

in poker that applies to investing as

7:19

well where you're making decisions

7:21

to put positions on, under

7:23

conditions of uncertainty, where,

7:25

you know, very little in comparison to all there

7:27

is to be known and the outcome in the invest

7:30

is gonna be heavily influenced by luck.

7:32

And if you win or lose on a particular

7:35

position, what does that mean?

7:37

I don't know. So I think that

7:40

how you think about poker

7:42

and how you approach that problem, what

7:44

are the weaknesses in human judgment

7:47

that that particular environment exposes,

7:50

what are the ways that you might address those weaknesses

7:53

in human judgment? So and so forth,

7:55

I think those actually pretty much directly

7:57

map on to investing. The difference

8:00

in investing is that you

8:02

have more time and you don't necessarily

8:05

have to make your decisions alone, which

8:07

makes it a little bit easier of a problem. But

8:10

nonetheless, still incredibly difficult.

8:12

Well, you mentioned a few points I want to pull

8:14

out there. One was resulting, which

8:17

is determining whether your

8:19

success or failure was due to luck or

8:21

skill or something else. Can you

8:23

kind of expand on that topic? Because I think that's

8:25

something that is hard for people

8:28

to grasp because you see the winners and you just

8:30

say, oh, that was really a smart decision. Right.

8:33

So resulting in particular is something

8:35

called outcome bias. So I call it results

8:37

thing, the academic term is outcome bias.

8:40

And it's that we will

8:43

take the outcome and directly map

8:45

it on to the quality of the decision. So

8:48

if we're seeing another investor, we're

8:50

looking at a fund, and we see the fund

8:52

had a good year, we'll think it's a good fund.

8:54

And if we think the fund had bad year, we'll

8:56

think it's a bad fund. And you

8:59

can see that very directly, actually,

9:01

in the decisions of

9:04

allocators in terms of who they're allocating

9:06

their money to. And we know that this is an error that

9:09

occurs. We're the managers who

9:11

have a really good year the

9:14

allocators will stick to and often give

9:16

more money to. And the managers who

9:18

have kind of a bad year, they'll ditch.

9:21

And then we know what happens. Right? Like, that ends

9:23

up progressing back to the mean. Mainly

9:25

because, like, it's just kind of not enough data.

9:27

And that's where you start to get into this resulting

9:30

problem. Right? Which is just because someone

9:32

did well doesn't mean that they're super

9:34

smart. So I'll give you a very

9:36

simple example of this particular concept.

9:39

I think that anybody who was

9:41

managing an individual's money

9:44

between, say,

9:47

fall of twenty twenty one

9:49

and May of twenty twenty

9:51

two. I'm guessing that whoever's

9:53

portfolio there are managing did amazing.

9:56

But does that make them a good money manager?

9:59

Because we can see it's like, well, it depends. Right?

10:01

It depends. How did everybody else do?

10:04

Like, we kinda need to know what beta is. Like,

10:06

we need to know a whole bunch of other stuff. How

10:08

did they make their decisions? Were they asking

10:10

their uncle what to invest in? Like, what were

10:12

their thesis? Like, we don't know any of

10:14

that just because someone did really well.

10:16

It doesn't mean that they were making good decisions,

10:19

but we think it does. And

10:21

likewise, when someone is doing poorly,

10:24

we think it's because they made bad decisions. And

10:26

that is also not necessarily true.

10:29

So this is generally the resulting problem,

10:31

and it causes us to learn

10:34

very bad lessons from

10:36

the experience that's available to us. Because the

10:38

experience that's available to us is, like, people

10:41

do well or they do poorly. And

10:44

then how do we judge that? So that's kind

10:46

of what happens when we're, like, looking at other people.

10:48

What's interesting though is when we look at ourselves,

10:51

we actually engage in something called self

10:53

serving bias. And I'm sure that you've

10:55

heard this before, when someone

10:58

wins in a position, they made great

11:00

decisions. When someone

11:02

lost, like, what could I do?

11:04

The pandemic hit, or the

11:06

macro environment went south, or

11:09

you know, I got unlucky. There were things that

11:11

I couldn't possibly have foreseen. Like, all these

11:13

ways to sort of offload it off of yourself.

11:16

So we kind of have a twofold problem.

11:18

Right? As we're sort of examining the

11:20

things that are occurring in the world. We're doing this

11:23

kind of one to one course expendance between outcome

11:25

quality and decision quality, that's really bad.

11:27

But when we're thinking about ourselves and this is something

11:30

you say in poker a lot, we'll tend

11:32

to attribute the good stuff to our own skill

11:35

and the bad stuff to luck.

11:37

And neither of those patterns is correct,

11:40

all of it can occur. I can make

11:42

a bad decision, get a bad outcome. I can make a bad

11:44

decision, get a good outcome. I can make a

11:46

good decision, get good outcome. I can make a good decision

11:48

and get a bad

11:49

outcome. And in the short run, certainly the outcome

11:51

itself. Doesn't really tell us very much.

11:53

We see that a lot with the

11:55

financial advisory world where if the

11:57

market goes up, hey, I was a genius and

11:59

the market went down and

12:00

Oh, that was the market. There's nothing we could do.

12:02

You just gotta hold on. There's nothing we

12:05

could have done. I actually just had a discussion with

12:07

someone the other day where I was trying to get money

12:09

moved from place a to place b or telling them that they

12:11

need to move their money from place a to place

12:13

b. And they said to

12:15

me, this was just for purposes of

12:17

getting it to the person who was managing the

12:20

funds as opposed to a different place. The

12:22

person that was having a discussion with said, Well,

12:24

I don't wanna move it from there because that

12:26

money's been doing really well there. And

12:29

I was like, so is everybody else's?

12:31

I think this conversation was in the summer.

12:33

Before everything started crash or

12:35

the spring, whatever. I was like, end.

12:37

But it's that real desire to attribute

12:40

the good performance to something particular about that

12:42

person. And what I said is they may or

12:44

may not be good. That's actually not why I want you to

12:46

move the money. But the fact that they've done well

12:48

over the past few months is meaningless.

12:50

Every human being who just parked

12:53

their money in the S and P has

12:55

made a gazillion dollars. But then

12:57

the flip side is, every

13:00

person who's had their money parked in the S and

13:02

P recently has lost a good billion dollars.

13:04

Does that mean it was a bad decision? No,

13:06

not necessarily. It depends on what

13:08

your goals are and what your values are, and we need

13:10

to understand all of those things before we can figure

13:12

out. Just in the same sense, it would be silly

13:14

if I lost a hand of poker to you for you to

13:17

assume you're a better player than

13:18

me. Who knows? Maybe you are, but

13:20

we'd have to play a lot more.

13:22

Or I could just take my win and just hanging

13:24

over you forever. That's

13:26

that's that's that's that's that's totally

13:29

unfair. That's good. That's

13:32

a good segue into your new book about quitting.

13:34

So I haven't fully read the book,

13:37

but I flipped through the concepts, and I think

13:39

a lot of them were really interesting

13:42

in the fact that a lot of us look

13:44

at winners and,

13:47

you know, they say, oh, they're really resilient

13:49

and they gutted it out and they kept at

13:51

it and that's why they

13:52

won. Why did you decide to write this book,

13:54

quit? What was kind of the reason behind it?

13:56

Because was very frustrated about

13:59

somewhat what you just said. I was

14:01

frustrated about this kind of lore

14:04

that we have around grit. The

14:06

greatest of magical quality that

14:09

causes success in the

14:11

sense of, look, It's

14:14

totally true that somebody who has succeeded

14:16

stuck to it. I do not deny

14:18

that, but that does not mean if you stick

14:20

to it, you'll succeed. Those two things

14:22

do not mean the same thing. They aren't equally

14:25

true. One is true and the other is not.

14:27

Because one is you know, you know

14:29

the outcome, so you kind of know the person stuck

14:31

to it. And the other is, does

14:33

that mean you should stick to it? That's

14:36

the question that we need to ask ourselves And

14:39

the answer is, well, that's absurd.

14:42

You need to have grit as a quality to

14:44

get you to stick to things that are worthwhile. Even

14:46

when they're hard. But grit

14:49

for grit's sake gets you to stick to things

14:51

that aren't worthwhile just because you think

14:53

that like winners never quit. And

14:56

I think that we can all remember, like, the early

14:58

American idol where they were to the bad singer's

15:00

auditioning. And

15:03

you know, Simon Cowell would say, like, this

15:05

is not for you. You're not gonna become a professional

15:07

singer, and they will go out of the room and say

15:09

to the camera, like, he's wrong. I know if

15:11

I just stick to it, I'll succeed. And

15:14

we're all looking at the what? No.

15:16

That's not true. But what we hear

15:18

is, like, the stories of

15:20

the artist who went

15:22

to twenty different labels and

15:24

finally got their album deal,

15:27

and then they succeeded. Then we

15:29

hear these sort of survivor stories and

15:31

we forget about the thousands

15:34

of people trying to do the same

15:36

thing where time is

15:38

just wasting away. So

15:41

I just felt like someone needed to have conversation

15:43

with grit. Not with Angela Duckworth,

15:45

because Angela Duckworth, in large part, I think

15:47

agrees with me about this, that

15:49

it's not so much stick to it and you'll succeed.

15:51

It's try a bunch of stuff.

15:54

If something is working for you, if it's

15:56

worthwhile, if it's something you're really passionate

15:58

about, stick to that, but they literally

16:01

quit everything else. And

16:03

I think that we really forget about the quote

16:05

everything else part because if you're

16:07

gonna be successful, it's

16:09

relatively important actually not to stick to things

16:11

that aren't working. Because every minute that

16:14

you stick to something that's not working is

16:16

time and effort and attention and money. That

16:18

you could be turning towards something that

16:20

would be working. So that is actually

16:22

the road to success. In the simplest

16:25

sense, like, what if you only

16:27

held every single stock that you ever bought?

16:30

That was your strategy. I buy a stock that

16:32

hold it forever. Because grid is amazing.

16:34

And if I stick to it, then I'll succeed. You

16:37

know, you're smiling because you can see that that's

16:39

completely absurd. Except that

16:41

literally that's the advice that we give people.

16:44

Just stick to it. Don't quit. If

16:46

you quit, it's a lack of character. And

16:49

I just thought someone should say that out

16:50

loud. Well, Wall Street loves that because

16:53

they want you to buy and they never want you to sell.

16:55

So they would love it if you had a portfolio

16:57

full stocks like

16:58

that. What Wall Street wants.

17:00

Right? Is well, not what Wall Street wants.

17:02

What an investor wants? Who is

17:04

not an active investor? Is

17:07

to index the S and P

17:09

five hundred. But here's the thing.

17:12

The S and P five hundred itself changes.

17:14

If you look at the companies that were in that index,

17:17

fifty years ago, they're different than the companies that

17:19

were there today. So anytime

17:22

that you quit something that, you know, that you stop something

17:24

that you started, that's quitting. So

17:26

when a company gets taken out of that index,

17:29

and a new company gets put in it, that's

17:31

a form of quitting. I'm quitting

17:33

this and putting this in. So,

17:36

you know, and that's true, obviously, like, if you

17:38

by an ETF, if you whatever. Like,

17:40

there's lots of coin happening in there. So

17:42

we can separate out this strategy of,

17:44

like, long hold, which fine

17:46

and understand

17:47

that, like, what you're holding still changes?

17:49

You know, there's a concept a few people

17:51

have talked about, which is a survivorship bias,

17:53

like what you're saying. Which is, you

17:55

know, we look at history and all we see are

17:58

the survivors. We don't see the

18:00

quitters. So we don't have a lot of information

18:02

to go back and look through that and say,

18:05

how is this a successful decision?

18:08

So how have you been able to kind of look

18:10

at that and make that

18:11

judgment? Because it's hard if you can't

18:13

find the data? And to analyze it's even

18:15

harder. Well, I mean, obviously, one thing

18:18

you can look at is base rates. Here's

18:20

a simple base rate. Like, of the people

18:22

who raise a seed round, how

18:24

many of those successfully raise

18:27

a series a. You can say, the

18:29

people who raise a seed round, how many of

18:31

those successfully raise a series

18:33

c? So then we can start to get a

18:35

sense of how many people are kind

18:37

of like getting shoved aside along

18:40

the way. Right? And we can start to see sort of the

18:42

negative space then, people that you don't

18:45

hear about. I read something on

18:47

Twitter or somebody saying it took me fourteen

18:49

months to raise my first round.

18:51

So what I tell you is don't ever give up.

18:54

So it's a very classic example of survivorship

18:57

bias. Right? Like, I stuck to it for fourteen

18:59

months, and then it worked out for me.

19:01

So therefore, that is what you should do

19:03

because apparently sticking it for fourteen

19:06

months works. But what I would want

19:08

to know as a decision maker is what's the average

19:10

time? To actually raise your round,

19:13

what are the things that you need to do in

19:15

order to kind of hit that mark? Right?

19:17

So, like, do you have someone who's coaching you

19:19

on the deck? For example, whenever you figure out

19:21

what those inputs are. And then

19:24

if I'm well beyond the average time, I should

19:26

take that signal that there's an appetite in the

19:28

market for it. And just because one

19:30

person did it doesn't mean

19:32

it's correct to do. Let me

19:34

try to give you a sense that might be clearer.

19:37

There are people who have been on top of

19:39

Mount Everest, and there's been a huge

19:41

blizzard that's rolled in. And they have

19:43

still submitted and they've lived. But

19:45

does that make it a good decision? If

19:47

you're in the middle of a snowstorm on

19:50

the top of Mount Everest, should you keep going to

19:52

the summit just because somebody happened to

19:54

somehow survived that situation. I

19:56

think you can see there very clearly the answer

19:58

is no. Of course you shouldn't.

20:01

The probability of death is too high. So

20:03

if we take somebody like a founder

20:05

who's pitching, and let's say the

20:07

average time to raise a series a is nine

20:09

months, whether a seed series, So

20:11

let's say that the average time to raise your first

20:13

round is nine months. I'm making that up, by

20:15

the way. I'm just making that number up. But

20:17

let's say that that were the case. You say

20:20

Okay, so what's the most

20:23

above nine months that I'm willing to do this,

20:25

assuming that I'm doing all the things I'm supposed

20:27

to do? And you can kind of set that

20:29

deadline for yourself and then say, if it's beyond

20:31

that point, then they signal that there is an appetite

20:33

in market for it. And

20:35

Here's the key. What else could I be

20:37

using this time for? I wanna

20:39

be an entrepreneur. I wanna develop

20:41

a world changing product that's gonna

20:43

create some sort of amazing unicorn.

20:46

I'm getting very strong signal from very

20:48

smart people that this is not the thing for

20:50

me to develop. And at some

20:52

point, you have to make the conclusion that

20:54

I'm wasting my own intellect on this

20:57

and that I should switch because maybe

20:59

I can switch and develop something else. Maybe

21:01

I can start something new that actually is going

21:04

have a higher probability of

21:06

working. So you could just kind of think about

21:08

it this way. If you knew you were gonna keep

21:10

going a year after that time,

21:12

trying to raise money for a startup

21:15

that you've gotten very strong signals from

21:17

the market is not worth it. Do you

21:19

think that that's a better use of that

21:21

year than taking that year to think

21:23

about another company you could start? And

21:25

another product? That you could

21:27

start taking the learnings from the last one

21:30

into this

21:31

one. And it seems to me pretty obvious

21:33

that the second is the better choice.

21:35

It's interesting as you kind of relate a few

21:38

concepts there. One of them is,

21:40

how do you know what's the best use of

21:42

your time versus stick

21:44

or quit. The question I would ask

21:46

is and I think this is really the

21:48

the important one is, what kind of

21:50

mental models can people use to determine

21:53

whether they should stick or

21:55

quit. Because know in my life, I've had

21:57

many examples that any rational

22:00

person would just look at and just say that's

22:02

silly. You've done this too long. And I've stuck

22:04

with it and been successful at it. But I've had

22:06

a feeling, you know, that, hey,

22:08

this is gonna work. I just have to stick with

22:10

it long enough. And I have other times I'm like, this

22:13

isn't gonna

22:13

work, and I'm gonna cut bait. So how

22:15

should people think about those? Look,

22:17

if we're completely objective, that would just be

22:19

expected value. Question. So

22:23

even in a case where you stuck to something and you thought

22:25

it would work, what you're saying is that I think the

22:27

thing I'm doing is positive, expected value.

22:29

Meaning, for every minute that I put into

22:31

this, I'm gonna get positive return on

22:33

that time spent. But that

22:35

does not mean that it wasn't right for you.

22:37

It wouldn't be right for you to quit because it's

22:40

expected value of the path that

22:42

you're on in comparison to other paths that

22:44

you might take. So one of the things that

22:46

we need to understand is that when we start something,

22:49

we tend to get really stuck in it. There's

22:51

a variety of reasons for that. There's a lot of

22:53

sort of cognitive debris, like cognitive

22:55

forces that make it very hard for us to walk

22:58

away from things. One of the most common

23:00

is the sunk cause fallacy, which makes

23:02

us think that, well, if I walk away, I'll waste

23:04

it all the time that I put into this already. And

23:06

I imagine that you have had that feeling under

23:09

those circumstances quite often. We

23:11

know that what we're trying to do is just get straight

23:13

to expected value, which is, and

23:15

continue doing this or I can take all that

23:17

time and I can put it into something else.

23:20

And do think that the thing that I'm switched

23:22

to is gonna have a higher expected value

23:24

than the thing that I'm doing? So

23:27

expected value in a very simple sense is

23:29

just like, if you're choosing between stock

23:31

a and stock b, you should choose the one that has the

23:33

highest return. Taking into consideration

23:36

your risk tolerance. And that's what you're trying

23:38

to do in terms of the paths that you've taken life. So

23:40

even the places where you stuck to it and worked

23:42

out, it still might have been better for you

23:44

to quit because it might have been

23:46

that you could have gone and taken all that time and energy

23:48

and put it into something else that had higher

23:51

expected return. But we're very

23:53

reluctant to quit things for a variety of reasons,

23:55

not the least of which is sunk cost, and not

23:57

the least of which is we try to get to certainty

23:59

that it's not gonna work out or will before

24:01

a willing walk away. Now, let

24:04

me just say that expected

24:06

value is not an easy way

24:08

to think about things. It's pretty abstract.

24:10

There's all sorts of cognitive biases. They're

24:12

going to distort the way that we think about expected

24:15

value and cause us to come up with very bad equation.

24:18

So instead, what I like

24:20

people to do is use something

24:22

called kill criteria. So

24:25

kill criteria is basically this.

24:27

What are the conditions that I might see at some point

24:30

in the future? What are the signals that I could see at

24:32

some point in the future that would tell me that I ought

24:34

to walk away? Now, we

24:36

could think about, like, why are we thinking about

24:38

kill criteria in this way? So, why

24:40

is that kill criteria? Well, I

24:42

think that we have the intuition that when we start

24:45

something, which we're starting

24:47

under conditions of uncertainty and luck,

24:50

which means that we're going to discover new

24:52

information after the fact by definition

24:54

that when that new information is bad news,

24:56

we will stop doing what we're doing. So

24:59

if we're climbing a mountain and a blizzard

25:01

comes upon us, Obviously, we will

25:03

turn around. If we're running marathon

25:05

and we break our leg, obviously,

25:07

we're gonna stop running. If

25:10

we have a thesis about an

25:12

investment. And then the world tells

25:14

us that our thesis was bonkers.

25:17

That obviously we're going to get

25:19

off the trade. You know, I take a job.

25:21

I have also ideas about what the culture is gonna

25:24

be, how much I'm gonna enjoy the work, so and

25:26

so forth. And then I learned new information that

25:28

don't like it very much. So

25:30

I think that we all think that under those circumstances,

25:33

we'll walk away and quit. And the answer is

25:35

no, we won't. Actually, we will

25:37

escalate our commitment to the cause. We

25:39

will increase how much we're committed to the cause.

25:42

think one place you can see this on the world stage

25:44

right now very clearly is

25:46

the war in Ukraine where

25:48

Russia was supposed to come in and take Ukraine in

25:51

three days. And obviously,

25:53

they aren't winning. There's lots

25:55

of signals that they are not winning that war,

25:57

but I think we all know that Putin isn't going

25:59

anywhere. Not unless he absolutely has

26:01

to. And in fact, he is escalating

26:04

his commitment to the war. He is spending more

26:06

money. He is sending more missiles.

26:08

He is scripted three hundred thousand

26:11

more relatively untrained soldiers

26:13

to send into that war. So there's

26:16

a good case where we can see that kind of escalation

26:18

of commitment. Unless you think that's

26:20

just Putin, we can think about our response

26:22

to losing the Vietnam War, which was incredibly

26:24

similar. Alright. So as

26:27

goes war, does go you holding

26:29

a stock. Because I think we all know

26:31

it's like you buy a stock at fifty, it's trading

26:33

at forty, it's trading at forty because everything

26:35

you thought was gonna be true about the world is no

26:37

longer true. And what do you say when that happens?

26:40

Well, now it's really cheap. I gotta hold it to get

26:42

my money back. Okay.

26:44

So given that we know

26:46

that when we sort of, let's say,

26:48

come across those signals, when they unfold

26:51

in real time, we're not gonna particularly good

26:53

listeners. What we wanna do is actually

26:55

set a list of those signals in advance.

26:58

And then say, when I see those signals,

27:01

then I ought to walk away. So you could

27:03

imagine, for example, like, I had an

27:05

investment. It's three

27:08

months from now and the investment has gone quite

27:10

poorly. What exactly do I think? Like,

27:12

why do I think that that occurred? What

27:14

were the early signals that

27:16

I imagine I'll see that

27:19

would tell me that this

27:21

investment is not gonna turn out the way that

27:23

I had hoped. So you can set those out.

27:25

So I'll give you a simple example of a kill

27:27

criteria. So let's say that

27:30

a year ago, I decided

27:32

that I was gonna invest in Bitcoin.

27:35

And I had a thesis about why I

27:37

was investing in Bitcoin, which was

27:39

that I thought that it would be my opinion

27:41

was that it would be uncorrelated with inflation.

27:44

And it would be a hedge against

27:46

market caps. I'm buying it not because

27:48

of the technology or because of the philosophy or

27:51

anything like that. I'm saying, look, this is gonna

27:53

be a good hedge against chaos

27:55

and inflation. So I'm

27:58

not saying whether that was a good thesis

28:00

or a bad thesis. I don't have a strong opinion

28:02

on that. And if somebody told me that

28:04

was their thesis at the time, I would be like,

28:07

okay, I believe you, if that's

28:09

what you really believe. So now

28:11

you have that thesis. You would then write

28:13

down what the kill criteria are,

28:16

which is when should I get out of this? Right?

28:18

When should I quit? And those kill criteria

28:21

would be, in this case, very detailed,

28:23

like, if inflation goes up by this much and I

28:25

see whatever strength correlation

28:27

between Bitcoin's price, and inflation,

28:30

in this case, it will be negatively correlated.

28:33

Right? So I see inflation going up and Bitcoin

28:35

going down, where it's correlated to

28:37

a certain degree. Then I have to

28:39

sell. Likewise, if the stock

28:41

market goes down and bitcoin is going

28:43

down with it, and I see that consistently occurring

28:46

over some period of time, that

28:48

I must sell. So you're

28:50

being very specific about what those fill

28:52

in the blanks would be. And when

28:54

you see those, you would have to sell. So

28:56

that would be an example of a co criteria.

28:59

Otherwise, what happens and I've seen

29:01

this with people and myself where

29:03

they invested in, say, Bitcoin

29:06

for one reason. And then when

29:08

it goes down, they'll pivot.

29:11

They'll say, oh, no. Well, I'm investing it for the

29:13

tech. Right? Like, I believe in what it will

29:15

do in the future. Or sometimes

29:17

there's the lovely now it's really

29:19

cheap. Like, you're laughing, but how many

29:21

times you heard people say that? It's

29:23

hard not to. It's hard not to say yourself.

29:26

Right. Oh, now that's really cheap.

29:28

And that's what happens when we sort of leave it

29:30

to ourselves on the fly. Whereas

29:32

if we make these commitments in advance, we'd

29:35

like, look, this is what happens. And

29:37

and by the way, you can have it let's say

29:39

that your thesis assumes that the fundamentals are

29:41

gonna within certain band. Let's say, like, interest

29:43

rates are gonna sit within certain band.

29:46

You can basically set out, well, if if interest

29:48

rates go below that band, what will I

29:50

do? And if interest rates go above

29:52

the band, what will I do? So

29:54

sometimes the answer might be anytime they're

29:56

outside of the band would sell. Sometimes

29:59

it might be like, look, if I have an inflation

30:01

hedge on, if it goes above the band,

30:03

I assumed I would be doing pretty well.

30:05

And I would certainly hold them maybe even

30:08

increase. Right? If it's below

30:10

the band, maybe I would get off that hedge.

30:12

Depending on why I was thinking why I was

30:14

doing what I was doing. So these

30:16

kinds of things are incredibly important

30:19

for helping you to be better at decisions about

30:21

when you stop the things that you start.

30:24

I love that criteria because I think that's

30:26

important. It's why people do trade journals

30:28

and set out buy and sell criteria

30:31

before you even do a

30:32

trade. Trading's hard enough.

30:34

So interestingly enough, so with the people that

30:36

I've worked with, the journaling is amazing.

30:39

It tells you what the buy and sell criteria

30:41

are. Very clearly laid out

30:44

thesis. And I think that what people

30:46

do is they make the assumption that having done

30:48

that work, that because implied

30:51

in that work is what

30:53

the conditions would be in the future that might

30:55

make you increase your position,

30:58

hold it or decrease your position that

31:01

when that stuff occurs, that will happen.

31:03

But you actually do need to do an extra step

31:05

when you're doing a trade journal. You need to

31:07

not just think about what conditions

31:09

would I buy or sell? Like, what's my thesis, so

31:11

on so forth? But you do have to say,

31:14

what does this imply about what the future

31:16

is gonna look like? And if the future does

31:18

not look like that in these particular

31:20

ways, then this is what my

31:22

reaction to this will be. I

31:25

know it sounds like distinction without a difference.

31:27

I've said, this is my thesis. These are the

31:29

conditions under which I would buy. These are the conditions

31:32

under which I would sell in terms of when I'm gonna

31:34

put the trade on. So doesn't that

31:36

mean that I'll be paying attention to those

31:38

things in terms of taking the trade off? And the

31:40

answer is no. You won't. Not

31:42

unless you specifically list them

31:44

as kill criteria or exit criteria. You

31:47

need to write those things down. It's really

31:49

good if you share them with somebody else. And

31:52

say when I see this, then I'm gonna

31:54

exit. And you can see that with the Bitcoin

31:56

example, someone can have a very detailed

31:58

journal about their reason for buying or

32:00

shorting or whatever Bitcoin,

32:03

and they can have like a super detailed journal.

32:05

But then when it is correlated with inflation,

32:08

are they selling? No, because they're already

32:10

in it. But if at the time

32:13

that you wrote down, like, why am I making

32:15

this trade? You also had a very specific

32:17

set of criteria that would tell you when you were to

32:19

exit. You're just much more likely to

32:21

be hit rationally in the face of that decision.

32:24

Trading's hard to begin with. You know, one

32:26

of the things I was thinking about before the interview

32:29

was you know, I'm from Boston. So

32:31

Tom Brady is kind of a staple up here and

32:33

you think about decisions like athletes

32:35

that are their identities tied

32:37

up with toughness. And here

32:39

you have somebody who's trying to

32:42

continue his career much longer

32:44

than maybe his family would have liked or

32:46

you can't read his mind, but I would

32:48

imagine with people like athletes where

32:50

being tough is a part of who you are,

32:53

it's really tough to know when to leave

32:55

at the

32:55

top. So what should people like that

32:57

do to think about quitting?

32:59

First of all, I just wanna say, like, some of this

33:01

is what your own values are. So,

33:04

Tom Brady may be okay with having

33:06

a terrible last season. I

33:08

don't know if he does or not.

33:11

What I do know is that the human tendency

33:13

is to wanna have that terrible season before

33:15

you leave because the

33:17

feeling of what if But what if I played

33:20

one more time? Maybe would have gotten a would

33:22

have been the seventh Super Bowl? Quite a few.

33:25

Six or seven. You know, that feeling

33:27

is so hard for us. In the same sense of,

33:29

like, if you sell a stock and then it goes up afterwards,

33:32

how painful is that? So in that

33:34

particular case, it's, like, particularly painful because we

33:36

can see happen, but even in the hypothetical

33:38

and the counterfactual, we're like, but maybe if

33:40

I played that last season, I could have got another

33:42

ring. Maybe that would have been the year that I

33:44

got another ring. I think that those

33:46

things are really hard, so we wanna sort

33:48

of butt up against the certainty that we have no

33:51

other choice. That's kind of for

33:53

ourselves. Richard Daylor said to me, like,

33:55

nobody wants to quit until it's no longer a

33:57

decision. So, like, when he has such a bad

33:59

season that the team doesn't even want him on it

34:01

anymore, well, then, okay, it's pretty easy to walk

34:03

away at that point because you're not wondering,

34:06

like, well, what if I had stayed? But the other

34:08

thing that you're getting out of that is other people aren't

34:10

wondering what if you had stayed. So we

34:12

can go to like a Barry Sanders type of

34:14

situation. How mad

34:16

are people at Barry Sanders? Like, still today?

34:19

They're like, what? Why?

34:21

Because for him, I don't

34:23

think that he wanted CTE. I know

34:25

that that was part of his decision making.

34:28

You know, he felt like his body was taking

34:30

a battering so and so forth. And so

34:32

he decided, like, look, I'm great. I'm just gonna

34:34

quit now when I'm great. Before I start,

34:36

like, the horrible decline and maybe I can't ever

34:38

walk again because I have two new replacements

34:40

that don't work very well. But people

34:43

are super mad. Because he did it,

34:45

again, it's an expected value equation.

34:47

And value is in there, not just in terms of, like,

34:49

money, but also what are your own values? Like, what am

34:51

I gonna get out of it? And what he determined was that

34:53

moment in time as he looked at what was

34:55

gonna happen over the future, that the cost

34:57

outweighed the benefits. And so he

35:00

quit. That's fine. Now, Tom

35:02

Brady may be okay with

35:04

playing until he sort of falls into

35:06

being somewhat mediocre. That may

35:08

be okay with him. I don't know. But I

35:10

know that the tendency is to do that thing

35:12

rather than the Barry Sanders thing.

35:14

And that more of us would be better off if we

35:16

kind of did the Barry Sanders thing. So

35:19

how do you actually get to that? Well, the way

35:21

that I kind of think about it is this use

35:23

of kilo criteria is very helpful for this because there's

35:25

some point at which, well, Tom

35:27

Brady decided to retire. Then

35:29

the question is, how do you think about whether you should

35:31

come back or not? Or you're

35:34

in a job and you have that thought, like, oh,

35:36

I'm not happy to maybe I should quit. Or

35:38

you're an employer. You're like, oh, I'm not happy with

35:40

this employee. Maybe I should let them go.

35:43

Or you're in a relationship, you're like, oh, I've really

35:45

got this nagging feeling. I'm unhappy. Maybe I

35:47

should leave. So we all have

35:49

those moments. Right? And the thing that we know is

35:51

that in those moments, we're not gonna make particularly

35:53

good decisions about whether we should stay or whether

35:55

we should go. So what you can do is in that

35:57

moment, you could say how long am I okay with status

35:59

quo. Let's say that you're thinking

36:02

about retiring from football and

36:04

you say, I feel like maybe

36:06

I need a little bit more information I leave,

36:08

I've got to think about how long am I okay with the

36:10

situation as I am in now, and maybe you say I want

36:12

to give it one more season. Okay, that's fine.

36:15

Then you say, what could I be seeing

36:17

and can be very specific, like in terms of

36:20

my stats, you know, my productivity on

36:22

the field? In terms of my

36:24

body. What are the things and you

36:26

could talk to a medical doctor? What are

36:28

the things that you could be seeing doctor that

36:30

would tell you that maybe I shouldn't be playing another

36:33

season after this. And you can write

36:35

those things down. You can think about things

36:37

that are subjective, like, what's my level

36:39

of happiness? What's my expectation for

36:41

how I should benchmark that? So you can

36:43

figure out whatever the set of things are

36:45

that would tell you either that this

36:48

is worth continuing or it's not worth continuing

36:50

anymore, and you can commit to that.

36:52

Because otherwise, what can happen is you're like,

36:54

oh, I'm gonna give it one more season and then it's

36:56

the end of the season. It's like tomorrow is always

36:58

tomorrow. Right? And you're like, oh, but then I'll give it

37:00

another season. I'll give it another and even though

37:02

you really hit those co criteria. So

37:05

you can do that with an employee as well.

37:07

How am we okay with having somebody who's performing?

37:10

Up to this level in the seat, what

37:13

are the things that I would expect to see in terms

37:15

of showing me that it's been turned around? What

37:18

are the things that I expect to see that would

37:20

tell me that this isn't worthwhile anymore?

37:22

So let's say you say can deal with this situation

37:24

for six more weeks before I initiate a search.

37:27

And you figure out, like, what are the signals that

37:29

would tell me that this employee is still not performing?

37:31

What are the signals that would tell me that they've turned

37:33

it around? What are the things that I need to do to help

37:35

them and support them? To see if they

37:38

can get there. Then you're just much more

37:40

likely to get to those decisions more

37:41

quickly, which is way better for everybody.

37:44

No. I couldn't agree

37:46

more. You just mentioned a lot of different

37:48

topics, relationships, employees.

37:51

Right. And it has a thing about decision making,

37:54

Kirk. It's everything.

37:56

Hard to when the emotions get involved. Well, and

37:58

this is what I'm trying to do. Let's get the emotion

38:00

out of it. Right? Because obviously in that moment when

38:02

you're like, I wanna quit. That's an emotional

38:04

moment for you. You're gonna be a good at making that

38:06

decision. In that moment, I don't think so.

38:09

So if you just step back and say, like, how long am

38:11

I okay with the status quo? You know,

38:13

maybe with a job that you have, it's another quarter.

38:16

I hope that you're hearing this concept of a

38:18

deadline. So you always wanna have a deadline

38:20

that's going along with these. I call it dates and

38:22

dates. What's the date and what's the state

38:24

of the world? So sometimes

38:26

the deadline is, like, I can't quit

38:28

until I have six months of runway. So

38:31

I'm gonna work to build up six months of

38:33

runway, and that's

38:35

my date. That's the stop time. And then

38:37

at that time, what are the things that tell me that

38:39

I should stick it out? What are the things that would tell me

38:41

that I ought to quit?

38:43

Great. Well, Annie, I really appreciate

38:45

you joining us in the show. My brain is full,

38:47

so you. Well,

38:50

I'm glad it filled your brains. Yes.

38:52

Thank you. There's so many mental models here

38:54

that you can use for investing, for life,

38:56

for pretty much really anything that

38:58

applies to your

38:59

life. So appreciate you sharing anywhere

39:01

can people find more about you and some of your

39:04

works The best place to find me is annaduke

39:06

dot com, which is my website. You can

39:08

find video, you can contact me there,

39:10

get old versions in my newsletter,

39:13

so and so forth. I'm on Twitter

39:15

for the moment at Andy Duke, but

39:18

you can also find me on the normal other

39:21

social channels, and I hope that people will check out

39:23

the alliance for decision education, which

39:25

is the nonprofit that I co founded to

39:27

bring decision education to every k through twelve

39:29

classroom. This

39:32

is a public service announcement. Hi.

39:34

My name is Kirk. I'm with the government and I'm here

39:37

to help. Doesn't that sound reassuring?

39:39

Well, let's talk about how they're helping.

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The government needs your help to make our money worthless

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39:54

Sure can. Just ask Zimbabwe, Venezuela,

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Greece, Germany, Hungary,

40:01

Greece, Yugoslavia, that

40:03

I mentioned, Greece, as a loyal

40:05

listener to the show, I want to reward

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40:35

I wanna get into the panel portion of the show

40:37

here where we have our Vrayon Barb Freeburn. Hey,

40:39

Barb.

40:40

Hi. So happy to be here.

40:43

Glad to have you here. Also, we have a ring

40:45

on Megan Gorman. Hey, Megan.

40:46

Hey, Kirk. How are you doing today?

40:48

Doing great. Well, let's just dive right in. So

40:50

Meghan, what were some of your thoughts here from

40:52

the interview? Listening to the interview

40:54

made me wanna buy her book and actually did.

40:57

I thought she was fascinating. And

41:00

she was really, really good at

41:02

laying out some of the hardest

41:04

challenges we deal with in working

41:07

with clients. And specifically, I

41:09

would say in terms of things

41:11

like factoring in luck

41:13

versus skill, I think that

41:16

particularly in very good markets,

41:18

people overestimate skill

41:21

and underestimate luck. And I thought she laid

41:23

that out really really well with decision

41:25

making. So for me,

41:28

it was a lot of confirmation of

41:30

what I experienced in working

41:32

with clients. And I really just

41:34

thought she added a thoughtful approach.

41:37

And the other thing that I just did a takeaway and

41:39

this I probably probably won't talk about, but What

41:41

I also found fascinating about her is she's

41:43

had, like, five careers. And

41:45

I liked that about her because I think that that

41:48

is much more normal

41:49

today. So that was my takeaways.

41:51

Yeah. I think the luck for skill that's one

41:53

of my favorite concepts I like to teach, you

41:55

know, is a different screen luck for skill and

41:57

the fact that we always blame ourselves

42:00

for the skill, and we blame luck

42:02

for the bad luck, but the good luck is

42:04

skill. So so that's

42:06

one of my favorites. It happens all the time with clients

42:08

over the years. So, Barb, what about you? What were some

42:11

of your takeaways?

42:12

I liked so much that she

42:15

laid out the way to make decisions in

42:18

a statistical strategy.

42:21

So she made things that are kind

42:23

of back of the napkin decisions.

42:26

She gave a framework for which

42:29

we can make decisions. So

42:31

expected value means

42:33

what are the likely outcomes that

42:35

something is gonna happen. So you can

42:37

actually quantify it. Not

42:40

perfectly, of course, but

42:42

you can look at it more scientifically

42:46

than you normally would. And then

42:48

I also loved the kill concept.

42:50

When I used to be a stock picker, I

42:53

would automatically write

42:55

down after I have to analyze the

42:57

stock pick and bought it, I would

42:59

write down the reasons to settle.

43:02

And I go back and review it.

43:04

And I trained myself to

43:07

sell the losers when they looked like

43:09

they were meeting criteria.

43:12

And I think that's a really good

43:14

strategy for anyone who is buying

43:16

stocks or mutual funds.

43:18

Let's dig in little bit, Barb. I think this is

43:20

one of those areas that you could

43:22

go crazy thinking about it, but you put down

43:24

reasons to sell. So how do you think

43:26

about it when you're, you know, buying a position?

43:28

What does that look like for you?

43:31

I was a fundamental investor and

43:33

I still am and I still use fundamental

43:36

strategies. And what that means

43:38

is you use ratios,

43:41

sales measures, earnings

43:43

measures, price earnings ratio,

43:45

cash flow, all different sorts

43:47

of ratios. You compare them,

43:50

decide if they equate

43:52

to creating an

43:54

investment that is a

43:56

fairly valued investment, an

43:59

undervalued or an overvalued.

44:02

And then you also look at the

44:04

analyst reports, you look at

44:06

the annual reports to

44:08

see what are the growth prospects. So

44:10

if the company's doing well, what do

44:12

they have projected in the future that's

44:15

going to enable them to continue

44:17

to grow? So that's the

44:19

type of information that I used to

44:21

buy. Typically, I won't

44:24

buy a stock that is way overvalued.

44:27

It might be a great company, but

44:29

if the price is too high, I

44:31

have a lower likelihood of getting

44:33

my money back or getting a good return.

44:36

So that's what I do on the buyout, but

44:39

my thesis don't always work out.

44:41

As is the case with any

44:44

sort of investor. Sometimes

44:46

you're wrong. And so after

44:48

I've made that buy, after I've laid

44:50

out the reasons I have bought the stock,

44:52

and I write those down. And

44:55

I do that with mutual funds as well

44:57

because I'm constantly analyzing whether

45:00

I've got the best EPS in my portfolio

45:02

or not. So I have

45:05

reasons to sell. Let's say they don't

45:07

meet their growth metrics. Let's

45:09

say the competitive environment gets

45:11

too tough or let's

45:14

say that there's something

45:17

that I anticipate that could

45:20

happen, that could go wrong, that could

45:22

cause the company to do

45:24

poorly That might

45:26

be a reason to sell. And typically

45:29

when a stock drops in value, I

45:31

do not wait for it to come back to

45:33

even. That is like one

45:35

of the most typical biases for

45:38

investors. If they have a loss, they think,

45:40

well, I'll sell it, but only if it goes

45:42

back to even. What I do is

45:44

I look at that stock and I say, when

45:46

I buy this today based on the

45:48

information I have, and if the

45:50

answer is

45:51

no, then I will sell it and take the

45:53

loss. That's a good philosophy,

45:55

I think, having that kill criteria. Are you

45:57

mainly focused on the fundamentals for the

45:59

kill criteria? Are you also looking at

46:01

other things like price and I

46:04

guess comparable

46:05

opportunities? Howard Bauchner: Yes,

46:07

I look at all of those things. Because

46:10

fundamentals are only one part of the picture.

46:12

You have to look at the

46:14

drivers of the growth. I mean,

46:16

that's the biggest thing. And then you have to look

46:19

at the market environment. What's

46:21

going on? I mean, look

46:23

at AOL. You know, we all

46:25

thought AOL was amazing. Look

46:27

at the beta max if you wanna

46:30

date yourself. That was incredible

46:33

technology. So

46:35

you have to look at so

46:37

many different factors and you have to

46:40

try and be scientific look, we can't

46:42

be scientific. We never

46:44

have all the information. So

46:46

you do the best you can with the information

46:49

you have, and then you don't be afraid to sell.

46:51

Don't be afraid to take a loss. You're gonna

46:53

take losses sometimes. And

46:55

typically, if you use sound

46:57

principles that you have laid out in

47:00

advance, your investment winners

47:02

will trump your losers.

47:05

Yeah. That's great advice. So Megan,

47:07

getting back to you, when you're working with clients,

47:09

how do you handle the kill criteria

47:12

that we're talking about with clients?

47:14

I think in all of this, you

47:17

to control the controllables. There

47:20

are so many factors to

47:22

investing that are beyond your control,

47:24

things like I talked about in terms of luck.

47:27

And it's sort of funny in watching clients

47:29

over the years. I have some clients

47:31

who have amazing timing and

47:33

then I have other clients that consistently have

47:36

bad timing. So what you're trying

47:38

to really do and Barb, I think, laid

47:40

this out quite well, is create

47:42

a repeatable and disciplined process.

47:45

And even like when I work with clients,

47:48

our meetings have a repeatable and

47:50

disciplined agenda. It's

47:52

very clear how I walk clients through

47:54

things. And that's how we want them

47:56

to be looking at their portfolios. Because

47:59

their portfolios, it's too easy

48:02

to make a short term decision on

48:04

something just because of a short

48:06

term blip in performance. So

48:09

a lot of the work we have to do is focusing

48:11

on what is our long term goals

48:13

what are we trying to achieve? Why is

48:15

investing? Real investing as dull

48:18

as watching paint dry? You know,

48:20

and really focusing on Okay.

48:22

Here is the due diligence process that

48:25

allowed us to go into this holding,

48:27

and this is the due diligence process that

48:29

would allow us to judge if this is

48:31

a holding to keep or get rid of,

48:33

just like Barb walked us through.

48:36

And I will tell you that the

48:38

people who do the best with this

48:40

are the ones who don't get very emotional.

48:42

And I think that that's really interesting about

48:44

poker players is that they're very

48:46

good at checking their emotions at the

48:48

door. The group of clients that I

48:50

have that are constantly looking particularly

48:53

in this marketplace, they're panicking

48:55

and just making bad decisions and

48:57

you're trying to hold them in place. And

48:59

I'll give you quick example. I had

49:01

someone who had an opportunity

49:04

to exercise some stock options and

49:06

is now sitting in cash. And has been

49:08

worried about going into the market. And

49:11

one of the things that we have spent

49:13

time talking about is

49:15

that today, and this might sound crazy,

49:18

but today there's lower risk in the market

49:20

than there has been a year ago. And going

49:22

through data points that would make you

49:25

start to see that this entry into the market

49:27

is not a bad time to start

49:29

doing it. But this person,

49:32

this person I've worked with for years, has

49:34

consistently always let their emotions

49:36

overtake them. And even in going

49:38

through a disciplined process and looking at

49:40

data, they become too overwhelmed

49:43

to make a decision, which is why

49:45

I have had to express to them, I'm concerned about

49:47

meeting they're meeting their goals. So

49:50

I really, really encourage people to have

49:52

a discipline system, control the controllables,

49:55

But the things you can't control like the market,

49:57

don't look. Just let it go.

49:59

And that makes a big, big difference.

50:02

And I'll just say one thing about process.

50:04

And that is when you study some of

50:06

the best leaders, some of the best American

50:09

presidents and their leadership skills, A

50:11

lot of them are poker players. Eisenhower,

50:14

one of the best poker players out there

50:16

and consistently played not just when

50:18

he's in the military, but when he was leading World War

50:20

two and as a president. And I think it's

50:22

very telling that his decisions

50:24

were very firm. He was very clear.

50:26

And he stuck to strategy and you could make an

50:28

argument he had one of most successful presidencies.

50:31

So looking at the people who are able to cut

50:33

emotion off and stick to process is

50:35

really really key.

50:37

You mentioned one thing which is

50:39

we have lower risk today than last

50:41

year. Could you expand on

50:43

that? Because I think it's a great concept.

50:45

What has happened over

50:47

the past year is all the key

50:49

fundamentals about interest rates inflations

50:51

have flipped on their head and the market has reacted.

50:54

And by the way, if the market hadn't reacted

50:56

the way it had, I would be concerned. The

50:58

market should be down with all the key fundamentals

51:01

changing. But today, when

51:03

you look at the market, what

51:05

is interesting is, one, equities are

51:07

far more reasonably valued than they were a year

51:09

ago. Fixed income is paying income,

51:11

cash is paying yield. And so

51:13

today, if you go into the market,

51:16

could the market still go down? Yes,

51:18

completely. But it likely

51:21

won't go down as much as it would have gone down

51:23

if you went in a year ago. Meaning things

51:25

are more reasonably priced, the market has

51:27

digested the fed rate increases, the

51:29

market is digesting how we're managing

51:32

inflation. And so therefore, your

51:34

risk is lower of a great

51:36

drop. Doesn't mean it won't happen, but

51:38

again, you might not see a thirty percent

51:40

drop, you'd see a ten percent drop. So

51:42

it's about patience in this.

51:45

And so that's why think today entering

51:47

the market is not a bad point in time.

51:49

You probably get some things that are more of a deal.

51:52

And if you're really, really nervous,

51:55

what I've always found is that the

51:57

repeatable and disciplined process here is

51:59

dollar cost averaging. It's boring,

52:02

it's hokey, but it actually

52:04

works, and it helps you manage your

52:06

emotion as you try to enter the market.

52:09

And I don't know, Barb, looks like you want to say something

52:11

as I was speaking. So I'd love to hear your

52:13

point of

52:13

view. What are my thoughts like that,

52:17

you're like my husband who like

52:19

reads my mind and says,

52:21

what are you nervous about? I know

52:23

you're thinking about something now.

52:25

Meghan, you're amazing. You know me

52:28

too well. So, yes.

52:31

First of all, I wanted to talk about valuations.

52:34

Which is what does it mean

52:36

that the market is cheaper? We

52:38

use ratios to help us

52:41

determine if the price

52:43

of a market. Just one simple

52:45

value that I talk about all the time,

52:47

which is the price earnings ratio.

52:50

That is the

52:52

amount of money, you

52:55

will pay for one dollar

52:57

of earnings of a company. So

53:00

if the price earnings ratio was twenty

53:02

five, you're gonna pay twenty five dollars

53:04

for one dollar of earnings

53:06

of the company. If the price earnings

53:09

ratio is fifteen, you're gonna

53:11

pay fifteen dollars for one dollar

53:13

verdicts. I'd rather pay

53:15

fifteen than I would twenty

53:17

five for the same company,

53:19

for the same dollar of earnings. So

53:22

that's what Megan is talking about. That's

53:24

one of the metrics, not the only

53:26

metric but it's really

53:29

important and a lot of analysts

53:31

use that as a way to

53:34

determine If you're getting a bargain

53:36

or you're getting a fair price or

53:38

if you're overpay. So

53:40

that was one thing going through my mind,

53:42

the other is interest rate.

53:45

Now, many of us listening

53:48

may be over fifty. I

53:50

don't know. I'm not sure. Kirk

53:52

can probably tell me But if

53:54

you're getting up there in the second

53:57

half of your life, you

53:59

may want to be a little more conservative

54:01

with your investment portfolio which

54:03

means you may not wanna have as greatest

54:05

stock allocation. Now the past

54:08

ten years, I mean,

54:10

it was devastating for any

54:12

of us that had bonds or cash.

54:15

You couldn't even get one percent

54:18

yield on a money market account,

54:20

you get like point zero

54:22

one percent. Now,

54:25

oh my gosh, I just got a message from

54:27

my high yield cash account at my bank,

54:30

they're paying four percent So

54:33

now is a time that you

54:35

wanna say. K? The average

54:37

return on stocks over the

54:40

long term is eight to nine

54:42

ten percent. With that

54:44

return, you have a lot of risk

54:47

which means you may have drawdowns

54:49

of ten, twenty, thirty percent

54:51

in a year. The average

54:54

return on bonds over

54:56

long periods of time is about

54:58

five percent, but you typically

55:01

don't have those draw downs. So

55:04

if you're at the point where you're getting

55:06

a little sick and tired of that volatility,

55:09

you also want to preserve some

55:11

of your cash and not so

55:13

much grow at all, then

55:15

you wanna seriously be

55:17

looking at

55:19

bond type investments. Treasury

55:23

bonds, corporate bonds

55:25

that are paying higher yields. You can

55:27

lock in higher yields. And

55:29

you will smooth out the volatility

55:32

of your portfolio and lock

55:34

in a basic cushion of returns.

55:38

So that's what was going through

55:40

my mind. And the other thing I'll

55:42

say about it too with cash being where it's

55:44

at right now, everybody's got pretty

55:46

cheap money on their mortgages. What I

55:48

like about where cash is today, which to your

55:50

point, I'm seeing it yields between three and a

55:53

half to four and a half percent depending on

55:55

the type of money market or cash management

55:57

strategy, it's really great leveraging.

56:00

Why pay down mortgage debt? There's no reason

56:02

to. You can earn your interest

56:04

for the debt on cash, and

56:06

that's such a low risk investment.

56:09

So to some degree, For me

56:11

out there listening, the market we're in right now is pretty

56:13

exciting. There's a lot of opportunities

56:16

out there and a lot of the challenges

56:18

have started to work through the system. And

56:21

never forget when you look at eight

56:23

of the past nine recessions since

56:25

nineteen sixty, the

56:27

market halfway through the recession or so

56:29

has started to see through the recession

56:32

and start to return even when data

56:34

like unemployment continues to raise. The

56:36

one aberration just everybody knows is

56:38

the two thousand one recession, and that is

56:40

because at the tail end of it, there was a terror

56:42

attack, which basically through

56:44

everything into another challenge.

56:47

So I'm excited about this market

56:49

and the more I talk to clients, the

56:51

more we start going through data, think

56:53

it's really important to show people data to help

56:55

with that repeatable discipline

56:57

process. People are getting excited

56:59

about what's coming up ahead. I couldn't agree

57:01

more. I think there's definitely more opportunity

57:04

this year than last year. It's interesting.

57:06

You can actually look at assets again like

57:08

fixed income and say, hey.

57:11

Hey, wow. There's fixed income actually makes sense

57:13

right now as opposed to last year, which

57:15

as Barb said, think it was point one percent. think

57:17

that was if you're lucky. Someplace

57:20

actually charge your money. They put money with them.

57:22

But I did wanna get into cognitive biases.

57:25

I mean, we all live with these every single

57:27

day of our lives. And when it comes

57:29

to investing, these are extremely important.

57:32

If you don't understand how they apply, and

57:34

how you can combat them. It actually can

57:37

severely negatively impact your performance

57:39

in your portfolio. So, Barb, let's

57:41

start with you. What are some cognitive bias

57:44

or some of the favorite ones or maybe even

57:46

some of your white

57:46

whales, if you will, when it comes to cognitive biases?

57:49

Well, the first one I talked

57:51

about before was anchoring, which is

57:54

I'm not gonna sell because I'm gonna wait for

57:56

my price to get back. I've talked about that.

57:58

The other one that is huge that

58:00

I have seen in play for the last few years

58:03

is called the herding bias, which

58:05

means if other people are doing it, I should

58:07

do it too. Which is

58:09

a recipe for disaster. Because

58:12

if you're following the crowd, you

58:14

are getting swept up into a frenzy

58:17

and you would typically end up buying

58:19

high. So be aware

58:22

of as many investors, including

58:24

Warren Buffett, have said, don't

58:26

be greedy. When others

58:29

are greedy, you wanna

58:31

be cautious. And when

58:33

other people are cautious, you

58:35

wanna be greedy. So as a

58:37

note to Meghan's client today,

58:40

now is absolutely the time

58:42

to get back in the market. You may

58:44

not hit the bottom but

58:46

you are certainly going to be better off than

58:48

if you wait for the readback. So

58:50

those are two biases. I'm going to

58:52

give you another one, which is called

58:55

the overconfidence bias, and

58:57

that wraps up in the discussion we had

58:59

at the beginning of the podcast, which is

59:02

are good investors lucky or

59:04

smart, and sometimes you

59:06

don't know the difference. Overconfidence

59:09

bias is when the S and P five

59:11

hundred is reaching twenty five

59:13

percent a year, and you think you are

59:15

the greatest investor ever.

59:18

First of all, it doesn't

59:21

take any skill to

59:23

invest in the S and P five hundred,

59:25

which by the way, I think it's a very good

59:27

barometer of how

59:30

to get good returns. It's a good investment.

59:32

The market has done very well. But

59:35

don't confuse the fact of

59:37

matching returns with an advancing market

59:40

with being a good investor. And

59:42

on the flip side, and Annie mentioned this

59:44

too, Sometimes you'll have a losing

59:47

investment and that doesn't mean you're a

59:49

bad

59:49

investor. So overconfidence

59:52

means that you know more than you do.

59:55

So I think those three are enough

59:57

to get you started. What's

59:59

interesting today is it's never been easier

1:00:01

to invest in the market. I always tell my team

1:00:03

when I was a kid you had to, like, look things

1:00:06

up in the newspaper. Like, it was a

1:00:08

much more complicated system. But part

1:00:10

of the challenge is with being so easy to

1:00:12

access the investment market is

1:00:14

it's way too easy to access information.

1:00:17

And then information bias is one

1:00:19

of the biggest challenges I find. Because

1:00:22

what happens we're all gonna get information

1:00:25

whether we're watching TikTok videos,

1:00:27

CNBC, Wall Street Journal.

1:00:29

But It's really how

1:00:32

you prioritize your evaluation of

1:00:34

them. And that's one of things that

1:00:36

really you have to work on,

1:00:39

which is which noise are you gonna tune

1:00:41

out and which noise are you gonna

1:00:43

focus on? So one of the things I would tell

1:00:45

anyone who's working with advisers Part

1:00:47

of the benefit of working

1:00:49

with an adviser is they

1:00:51

should be taking you through economic data

1:00:54

coming from the top tier economists

1:00:57

and explaining to you to give context. Because

1:01:00

at the end of the day, investing is a science.

1:01:02

And that data really

1:01:05

can sort of clear the air

1:01:07

and give you real perspective where

1:01:09

if you're listening to some TikTok

1:01:12

video or somebody on CNBC, you

1:01:14

have to remember the reason they're putting that information

1:01:16

out there is to get you to watch, to tune in,

1:01:18

to sensationalize. And so maybe

1:01:20

that data, while there are nuggets of truth

1:01:23

in it, shouldn't be overemphasized versus

1:01:26

economic data. That has

1:01:28

reasoning and data behind it

1:01:30

and the science of investing. So

1:01:33

really, really think about where

1:01:35

you're going to get your information and be

1:01:37

wary of information

1:01:38

bias. Hey, Megan.

1:01:40

I've gotta jump in here. Because

1:01:43

that is one of my pet peeves. There's

1:01:46

so many people out there. They're dynamic.

1:01:48

They're interesting. They're certainly more

1:01:51

glamorous than I am,

1:01:53

but maybe they got rid

1:01:55

of us hundred thousand dollars of debt

1:01:58

and so they think they're an expert in

1:02:00

invest state. What you

1:02:02

can do now to make sure you're

1:02:04

listening to people who are smart

1:02:07

is learn how to distinguish between

1:02:10

someone who has credentials,

1:02:15

experience, knowledge

1:02:18

in investing. Go to their LinkedIn

1:02:20

profile. What is their

1:02:22

college degree? Their master's degree.

1:02:25

And what are their certifications? Are

1:02:29

they skilled in

1:02:31

investing? Almost

1:02:33

any information that you'll get off

1:02:35

of your Fidelity, your Schwab,

1:02:37

your Vanguard websites

1:02:40

is really good information. You

1:02:43

can trust that information. If

1:02:45

you look at the Federal Reserve side,

1:02:48

you're gonna get good information. If

1:02:51

you look at the CFA site,

1:02:53

which is the certified chartered

1:02:55

financial analyst website, you're

1:02:58

going to get good information. So

1:03:00

make sure before you dive in and

1:03:03

follow somebody who got rid

1:03:05

of a bunch of dead and is really cute

1:03:07

on

1:03:07

TikTok, make sure

1:03:09

they know what they're talking about.

1:03:12

Totally agree. Well, as we kind of get ready to wrap

1:03:14

it up here, final thoughts from you, Megan.

1:03:16

I think her book I can't wait to read it.

1:03:18

I think that the point she lays out is really

1:03:21

important. But I think just to emphasize,

1:03:23

most people don't realize when they

1:03:25

look at the spectrum from skill to

1:03:28

just random luck, investing is sort

1:03:31

of in the middle there. It takes skill

1:03:33

but you have to have moments where things

1:03:36

do work in your favor. You know, you've invested

1:03:38

in a fund and it's a small

1:03:40

that fun, and small cap has a great year

1:03:42

and great run. And so as

1:03:44

a way to make sure that you're

1:03:46

running your portfolio the right way, focus

1:03:49

on a disciplined approach on

1:03:51

how you run it, how you make choices

1:03:53

in it, when you decide to buy,

1:03:55

when you decide to sell, and be

1:03:57

wary of information bias. Have

1:03:59

your key places that you turn

1:04:01

to for economic data and

1:04:04

stick with that. And remember, it's incredibly

1:04:07

boring to be a good investor. It is

1:04:09

not exciting. It is not mean stocks.

1:04:11

It is just boring. I'm

1:04:13

Megan Gorman. You can find me at forbes dot

1:04:15

com. Kirk, thanks for having me on.

1:04:17

Thanks

1:04:17

for coming on Megan. And Barb, final thoughts

1:04:20

from you. Diversification. Nobody

1:04:22

knows the future. Small

1:04:24

cap value, huge outperformer

1:04:27

this year. Yeah. I've had that in my

1:04:29

portfolio for decades. It's

1:04:31

done not much, but give

1:04:33

me a a couple points in dividends.

1:04:36

This year, it's gone through the roof. So

1:04:39

don't try and pick the right sector. Don't

1:04:42

try and pick the right asset. Have

1:04:44

a smartly diversified portfolio

1:04:47

that you have created with

1:04:50

a sane and

1:04:53

smart strategy. And

1:04:55

then just stick with that. If

1:04:57

you wanna play around, choose a small

1:04:59

percentage of your investable

1:05:01

assets, five percent, maybe go up

1:05:03

to ten percent. But most

1:05:05

of us do not give the time

1:05:08

or the discipline or the

1:05:10

knowledge to be active

1:05:12

traders. And if you do,

1:05:15

well, more power to you. But

1:05:17

in general, the data's pretty clear

1:05:21

that just sticking with

1:05:23

a diversified US

1:05:25

and global stocks bonds,

1:05:29

a little bit of cash, and

1:05:31

just keeping your asset allocation

1:05:34

in line with your risk tolerance and

1:05:36

your financial goals That will

1:05:38

take you a long way successful

1:05:40

investing. I'm Barb Friedberg.

1:05:43

You can find me on YouTube

1:05:45

at roboadvisor pro's and

1:05:47

at Barbara Freiberg Personal Finance.

1:05:51

Thanks for coming on, Barbara. And that's the show for

1:05:53

this week. Thank you again for joining us MoneyTree

1:05:55

Investing podcast. My name is Kirk Chisholm, Wealth

1:05:57

Manager of Innovative Advisor Group.

1:05:59

We don't just manage your wealth. We make your life

1:06:01

better. You can find more about me at

1:06:03

innovative wealth dot com, and of course, you can find

1:06:05

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1:06:08

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