Attribute Bias

  

Categories: Metrics, Stocks, Investing

You let the computer pick stocks for you. But not randomly...based on a certain set of conditions (like high yield or low risk, etc.), you’re likely to get a bunch of stocks that share more than just the characteristics of being high yield or low risk. They’ll also likely have other things in common, like being in the same industry or being companies that have similar business profiles or management philosophies.

Attribute bias is the tendency for those auto-selected securities to have more in common than you asked for. Let’s say you decide to use the built-in tool on your trading software that gathers a collection of securities that fit a certain profile you’re interested in (high-earning or stable or low risk, etc.). The bots inside your computer return a whole list that fit the profile to a T. When you go to buy some of those securities, you may be getting more than just a group of stocks that all fit the profile of "stable." You might be getting stable stocks that are all in tech or maybe agriculture or companies that have similar leadership styles.

It isn’t necessarily a bad thing, but it can lead to an un-diversified portfolio.

Related or Semi-related Video

Finance: What is Modern Portfolio Theory...4 Views

00:00

Finance allah shmoop what is modern portfolio theory All right

00:07

basic idea Here people Diversification is good Dig it right

00:12

C d i g there that's modern Alright let's goto

00:16

a gn modern like when hunk and invested from their

00:20

cave Well they just invested in good rocks or spears

00:24

and really didn't worry about much else And well math

00:27

hadn't really been invented yet So like who knew that

00:30

If all right well then along came harry markowitz in

00:33

nineteen fifty two who tried to science and math the

00:37

crap out of the stock market What he came up

00:39

with was modern portfolio theory which basically said that there

00:44

was a smarter way to invest than just you know

00:47

putting your life savings into blockbuster because you like the

00:51

logo using all sorts of advanced metrics that we won't

00:54

torture you with here The theory he devised was that

00:58

well rather than throwing your money against the wall to

01:01

see what sticks you could use extensive elaborate data to

01:04

determine the best way to maximize your returns depending on

01:08

how much risk you were willing Teo you know risk

01:11

And there are five key ideas behind modern portfolio theory

01:15

And yes of course we have videos on each of

01:17

these The first is alfa which is kind of like

01:20

how smart you are in the market Then there's beta

01:22

which is about volatility in a broadway The vics we

01:26

got a whole video set on that Then they're standard

01:29

deviation and no that's not some kinky reference to fifty

01:32

shades It's more about how the market diverges from your

01:35

given individual stock pick and volatile things are finally the

01:39

beta then there's our squared it's all about how a

01:42

stock or a given index conforms to a given line

01:46

or expected return ratio Like how close it is how

01:49

proximate is And then finally you have the sharpe ratio

01:53

Thank you bill sharp from stanford university who also talked

01:56

about being smart in the market so that you could

01:59

evaluate your rich turns whether they were smart or just

02:02

a lottery ticket Lucky Oh and we're probably not such

02:05

a wise investment in the beginning even though they turned

02:08

out okay That would be sort of the sharpe ratio

02:10

Yeah all right Well in general mpt skews toward less

02:13

risky investments but it all comes down to risk reward

02:17

Tolerance in the end if for whatever reason you feel

02:20

supremely confident that radio shack is about to make a

02:23

massive come back well you might be able to justify

02:26

taking more risk in loading the dice But to be

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clear radio shack was just a bad example So kids 00:02:33.29 --> [endTime] don't try this at home

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