Autoregressive

  

Categories: Metrics, Stocks, Bonds, Investing

Autoregressive models are used by analysts and statisticians (so you know it must be fun) to try to predict future securities prices based on a history of previous prices. While this may not always be a dependable way to forecast ("past performance is no guarantee of future results" after all), they can also take into account trends, cycles and moving averages.

It's not an in-depth way to review a company (it's like trying to judge the quality of a baseball team by yesterday's score), so you may not want to rely on an autoregressive model to choose a stock. But in addition to analyzing a company’s financials, you could use the model to decide at what price you will buy and sell. An example of an autoregressive chart can be found here.

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Finance: What is Regression Analysis?7 Views

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Finance allah shmoop what is regression analysis Regression and elses

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no it's not a therapy session in which your psychiatrist

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tries to figure out why you've gone back to using

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passive fires It's simply this the process by which a

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siri's have different independent variables are compay haired to a

00:21

dependent variable to see which might have the greatest effect

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on the value of the dependent variable All right Well

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okay That's The theory of it anyway But what about

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some practical examples Well what are these graphs And what

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do they tell us Well let's take pete the pizza

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joint guy How does he know what's bringing in customers

00:40

Is it his new burrito pizza or the virtual skee

00:44

ball machines he put in the back Well we can

00:46

use some math here to find an equation Usually a

00:49

linear one Linear regression Very fine Mathematic sport That best

00:53

matches the pattern in the data Then we can see

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how close the points are to that line and that

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you know will solve our burrito pizza Steve all conundrum

01:02

and help pete manage his business better Well the closer

01:06

that data points are to the line the more likely

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there's some kind of link between the independent and dependent

01:12

variables well it doesn't mean one variable causes another It

01:16

just means they're linked somehow Like what about the link

01:20

between ice cream sales and drownings Death that's a morbid

01:23

connection but see how cloaks the data points are to

01:26

that special line So yeah there's absolutely some meaningful link

01:30

between ice cream sales and drownings deaths greater ice cream

01:33

sales on a given day is always linked to mohr

01:36

drowning deaths on that day Why what's the linking factor

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Flavor of ice cream of the amount of sugar in

01:43

the ice cream Too much in ice cream fat and

01:46

crap and stuff accessibility to public swimming pools Well clearly

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ice cream isn't some insidious killer drowning people who get

01:53

in the water without waiting the records that you know

01:56

one hour But there is a link between those two

01:58

variables Think about it As it turns out hire isis

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scream sales happen on hotter days so heat or sunshine

02:05

is the linking factor Mohr people go swimming on hotter

02:10

days when more people swim while they're going to be

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more drowning possibilities anyway so i scream sales in drowning

02:16

Deaths are linked but ice cream sales don't cause drowning

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death Got it No causal link there Similarly check out

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how the points in this graph are not really close

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to the line at all There's no link between your

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shoe size and your g p a you know unless

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you buy huge shoes build a mini computer that fits

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in the extra space in your shoes and use that

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to help you you know cheat Don't do that by

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the way Always cite shmoop anyway back to pete the

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owner of zaza pizza Pete almost has more customers lately

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than he can handle while the lightning is striking Pete

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wants to find a way Teo you know bottle it

02:50

The thing is he's made to significant changes to his

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restaurant and he's not sure which one is more responsible

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for the influx of people tossing money of him Is

03:00

it the virtual skee ball machines Or is it his

03:03

new burrito pizza Is there in fact any link at

03:06

all Well it could be both that are responsible but

03:09

that's beyond pete skill and this course to determine he

03:12

can only compare one at a time to the increased

03:14

Revenue so pete picks different days and plots the number

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of burrito pizza orders against the total money made that

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day Notice how the data points seem closely to follow

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an imaginary line there fromthe lower left to the upper

03:27

right In general we can see that low burrito pizza

03:30

order numbers are paired with lower daily revenues Also hi

03:34

burrito pizza orders are paired with higher daily revenues high

03:39

against high low against low will the closer the points

03:42

are too that imaginary line the more likely it is

03:45

that the independent variable in this case burrito pizza sales

03:49

is at least related in some meaningful way to the

03:52

dependent variable like it's the pendant on sales of total

03:56

daily revenue under our tea i eighty for their or

03:59

phone or computer or whatever you're using first week pop

04:02

up our data into the list by pressing the stat

04:04

button Then enter we put in the ex data in

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list one there l won and the y data enlist

04:10

to l two Now we press the second key and

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the mod key to get out of that menu If

04:15

we don't get out of that menu well we're just

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begging to screw the pooch here so get out Get

04:19

out now we bash stat move over to the cal

04:22

commend you and choose option for which is lean wreg

04:25

a x plus be all right That's in texas shorthand

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for linear regression Yeah on the menu it brings up

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moved down to calculator and then press enter if you're

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cal doesn't show the r squared and our values Well

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you need to hit youtube in search for how to

04:41

turn on stat diagnostics t i eighty four there's a

04:45

bunch of important info in the results that we need

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to check out most importantly for pete's sake is the

04:50

value of our the closer that our value is toe

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one or negative one The closer the points are two

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best fit that possible line Well the closer they are

05:00

value is toe one for graphs with positive slopes or

05:03

negative one for graphs with negative slope the stronger the

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link between the independent independent variables there right That link

05:10

is called a correlation right They correlate it doesn't mean

05:13

higher daily revenues are absolutely caused by burrito pizza lovers

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but it does suggest there somehow correlated and that correlation

05:21

is strong anyway The a and b values that you

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see on the display happen to be the slope And

05:25

why intercept of the equation in the best possible line

05:28

pete can use these to predict daily revenues if he

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knows the number of burrito pizza sails in a day

05:33

But that's a different video Pete still needs to know

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if virtual skee ball is so exciting that it might

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be more responsible for daily revenue jumps He also plotted

05:42

the number of times virtual skee ball was played in

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a day versus those same daily revenue figures Well guess

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what The points look like a cloud instead of having

05:51

any obvious linear pattern Well if we pop that data

05:54

into the cal can run the same linear regression process

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again we get a very different our value We can

06:00

also just see that the points aren't that close to

06:02

the line that our value is not close toe one

06:05

at all In fact it's cozying up to zero like

06:08

it's Ah you know frat boy and zero is well

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every girl within a forty meter radius when they are

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value is sniffing around zero like that Well it means

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there's some kind of very weak correlation between the independent

06:20

and deep and it variables We can't stress enough that

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this is in proof of any kind of cause no

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matter how weak between the two variables just that some

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kind of correlation exists and that it's weak pete has

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some evidence that the increase daily revenue is almost all

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about the burrito pizza and only a tiny bit due

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to the virtual skee ball crowd But this is a

06:40

big but pete does not have proof they are Value

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just suggests that there's some kind of link between the

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two variables Not that a change in one variable causes

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a change in the other Still with that significant of

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a difference in our values pete is pretty safe in

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thinking burrito pizza is probably more important in driving higher

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revenues than virtual skee ball Pete used a regression analysis

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on the two different variables he thought might influence his

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bank account the most any decisions he makes killing forward

07:08

should probably be menu focused as opposed to you know

07:11

attraction focused and still he can't forget the virtual skee

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ball entirely It is probably a teeny bit responsible for

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the increased mullah in pete's case the correlation between the

07:20

variables was positive which means that as burrito pizza sales

07:24

or virtual skee ball plays increase well so does daily

07:28

revenue there also negative correlations here is well where as

07:32

one variable increases the other variable decreases Case in point

07:36

carla's customs right next to pete's place carla has customs

07:40

takes broken down golf carts and file suits them up

07:43

They recently made three distinct changes to their builds and

07:46

have noticed a huge decrease in the time it takes

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one of their cards to complete the forty r dash

07:51

will car lot I wanted to figure out which change

07:54

might have been the most responsible for the decreased time's

07:57

Carlota plotted forty yard dash times versus the size of

08:01

the rims that these things right here they're diameter and

08:04

got an r value of negative point one seven nine

08:08

when she ran a linear regression of the data then

08:11

forty yard dash times versus the cylinder diameter there and

08:14

got in our value of negative point six to eight

08:18

when she ran a linear regression of that data then

08:21

the forty yard dash times versus the nitrous oxide concentration

08:24

Is what she ran and she got in our value

08:27

of negative point nine four eight when she ran a

08:29

linear regression of the data Well guess what The simple

08:32

fact here all three plots have some kind of linear

08:35

relationship It does mean that there's some kind of correlation

08:38

between each of these three variables rim size cylinder diameter

08:43

and nitrous oxide concentration you know in the forty yard

08:46

dash time of the golf carts with her mostly electric

08:49

But we won't get technicals here since all the grafts

08:52

have negative slopes and the correlation with nitrous oxide is

08:55

the close to the values to negative one The nitrous

08:57

oxide concentration has the strongest correlation to decrease forty yard

09:02

dash times like it's bad for speed reduced nitrous oxide

09:05

in your golf cart it's important to remember that carlotta

09:08

can't say that the nitrous oxide concentration is the direct

09:11

cause of the faster times All she knows is that

09:14

there's a link or a correlation between them Still with

09:17

further experimentation carlota could establish a causal relationship Carlotta explored

09:22

the relationship between three different variables and their possible effect

09:25

on the time to run the forty yard dash using

09:27

regression analysis She determined all three variables had some kind

09:30

of negative correlation of the times To run the course

09:32

as the nitrous concentration or the rim sides or the

09:35

cylinder diameter increased well the forty yard dash times decreased

09:39

Clearly the nitrous concentration had the strongest correlation Carla should

09:44

probably focus on that concentration for the greatest decrease in

09:47

times She knows she can't ignore the rim size nor

09:50

can she ignore the cylinder diameter as they all contribute

09:53

Toe overall Golf cart forty r dash speed times Right

09:57

regression analysis will never tell us which variable is the

10:00

actual cause It just kind of gives us it's along

10:03

the way it's best to make decisions informed by all

10:06

the variables that are correlated to the dependent variable And

10:09

as kelly clarkson famously saying you know this independent variable 00:10:13.231 --> [endTime] something like that miss independent variable

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