Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

CAIA > Reading 2.8 > Flashcards

Reading 2.8 Flashcards

1
Q

Test statistic formula

A

(Estimated value - hypothesized value) / standard error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

In Ordinary least squares (OLS) regression, what is beta, alpha and residual?

A

Beta = slope
Alpha = vertical intercept
Residual = distance from line to the actual value (data point)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What issues should an analyst be concerned about during regression analysis?

A

1) Outliers
2) Autocorrelation
3) Heteroskedasticity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What is heteroskedasticity?

A

Error term’s variance is correlated with an explanatory variable

OR

SD of a variable is not constant over time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What are leptokurtic distributions?

A

Fat - tailed distributions. = have more probability of extreme (rare) events

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

When does OLS generate unbiased estimates of A&B?

A

If error terms are:
1) normally distributed
2) uncorrelated
3) homoskedastic

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What is homoskedasticity?

A

Error term / Variance of residual / SD

is constant

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

When is null rejected?

A

If p value < significance level (1/5/10%)

OR

T-test > critical value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

When is null not rejected?

A

p value > significance level

OR

T test < critical value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What is P value?

A

Measure used to determine the likelihood that an observed outcome is the result of chance.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

How is p value interpreted?

A

1) Measures presence of a relationship

2) the larger the t test the smaller the p value

3) reject null if p value less than significance level

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

How is t statistic interpreted

A

1) reject null if t test is larger than critical value

2) the larger the t test the smaller the p value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Main critical values (1,2,5,10%)

A

1% conf level = 2.575

2% сonf level = 2.33

5% conf level = 1.96

10% conf level = 1.645

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What does OLS do?

A

Finds estimates of a&b that minimize sum of squared error terms (residuals)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

9 sampling & testing problems

A

1) selection bias
2) self selection bias
3) survivorship bias
4) data mining
5) data dredging
6) backtesting
7) backfilling
8) cherry picking
9) chumming

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Data mining

A

Trying to find patterns in very large data sets.

Usually just finds false patterns that are random.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Back testing

A

Retroactively testing your investment strategy using historical data and seeing if produces good returns. Prone to cherry picking because the analyst is already aware of the data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Data dredging

A

Conducting multiple tests on same data and selecting only the results that show statistically significant findings

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

What p value indicates that the t test is statistically significant

A

The smallest one

OR

If its smaller than the significance level

20
Q

Type 1 error in hypothesis testing?

A

False positive = false rejection of true null = the null should NOT be rejected because it true.

Alpha = probability of this type of error

21
Q

Type 2 error in hypothesis testing?

A

False negative = test fails to reject a false null = null is false and SHOULD be rejected.

Beta is the probability of this error

22
Q

How to treat HETEROSKEDASTICITY?

A

Use weighted least squares.

It weighs the data that has high error term volatility.

23
Q

Luck in returns is defined as?

A

Ex ante alpha - ex post alpha

24
Q

Steps in the statistical hypothesis test

A

1) state null and alternative
2) design statistical test for the hypothesis
3) perform test using sample data
4) reject/fail to reject hypothesis

25
Q

Refers to the probability that null is rejected when true (type 1 error)

A

Statistical significance

26
Q

1 - alpha is referred to as

A

Specificity of the test

27
Q

Probability of type 2 error =

A

Beta.

28
Q

1 - B in hypothesis testing =

A

Power of test

29
Q

Which statistical issues incur during regression analysis that analyst SHOULD be worried about?

A

Many strategies produce extreme results

=>

Which has a large influence on regression estimates

30
Q

Buying securities in a recently outperforming sector without considering broad sector metrics is an example of?

A

Cherrypicking

31
Q

Jabir Wasem, an analyst at Betafund, is analyzing the exposure of a company’s stock to market return Auctuations using the ex-post single-factor CAPM-based regression equation. Which of the following is NOT likely to be part of this analysis?

A) The regression is run with an intercept.
B) The independent variable is the return on the market portfolio less the riskless rate of retumn.
C) Beta estimates the linear sensitivity of the stock return to changes in the market return.
D) The dependent variable is the return on the stock less the riskless rate of return.

A

C. Because in this example Beta measures the EXCESS return in EXCESS of market return. Not EXPECTED return.

32
Q

Is difference of ex ante alpha of 0.0007% economically significant?

A

No

33
Q

How can heteroskedasticity be detected?

A

Plot the residuals (E) against time

OR

Against independent variables

To see if a pattern emerges.

34
Q

What does plotting the dependent variable against each independent variable do in relation to heteroskedasticity?

A

Results in the original scatter plot of returns does not help identify heteroskedasticity

35
Q

What is a simple linear regression?

A

It describes the relationship of ONE dependent variable and ONE independent variable

36
Q

Which measure explains the percentage of total variance in the dependent variable that can be explained by the regression model?

A

R squared

37
Q

What does b represent in a simple linear regression?

A

B = change in (Ri - Rf) per unit change in (Rm - Rf)

38
Q

Which type error is more serious?

A

Type 2 error

39
Q

Analysis of Long term returns of HFs often results in flawed conclusions because?

A

Because of survivorship bias. Unsuccessful funds close down and stop reporting results, so successful funds are overrepresented

40
Q

Beta drivers are? Example of beta invesmtments?

A

Capture risk premiums by bearing systematic risk (beta)

Ex: ETFs

41
Q

Alpha drivers are? Examples of investments

A

Aim to outperform the market, use alternative invesments.

Ex: PE, HFs

42
Q

Testing for null results in to 2 scenarious:

A

Rejected or NOT rejected.

Accepted and other terminology is not applicable

43
Q

Issues with estimating RELIABLE alpha

A

1) non-normal returns
2) outliers
3) biased testing
- not random
- procedures are not specified before testing

44
Q

Issues with estimating RELIABLE beta

A

1) true / false correlation
2) correlation / causality

45
Q

How does expense ratio impact alpha?

A

Alpha is decreased by the amount of the expense ratio

46
Q

What is the confidence interval in hypothesis testing?

A

Indication of a parameter’s true value

47
Q

What is significance level of a hypothesis test?

A

Probability that the null is rejected when true

CAIA (14 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions