What does the random walk theory state? correct answers Financial theory stating that stock market prices evolve according to a random walk and thus cannot be predicted
What does the EMH state? correct answers States that share prices reflect all available information instantly. Implies that inv...
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What does the random walk theory state? correct answers Financial theory stating that stock
market prices evolve according to a random walk and thus cannot be predicted
What does the EMH state? correct answers States that share prices reflect all available
information instantly. Implies that investors cannot consistently generate superior returns to the
market except through luck
Three-different definitions of 'All available information' correct answers Weak-form (past prices)
Semi-strong-form (past prices + financials)
Strong-form (all public and insider information)
What is the difference between technical and fundamental analysis? correct answers Technical -
past prices
Fundamental - financial data (annual reports etc.)
What are the advantages of using log returns? correct answers They are additive, meaning multi-
period returns can be computed easily.
What does it mean for two events to be independent? correct answers The probability of A is not
affected by the occurrence of B
What is a discrete RV? correct answers If X can only take a finite number of values it is discrete.
What is a continuous RV? correct answers If X can take any value in some interval along the real
line, it is a continuous RV
What is the difference between a probability mass function and a probability density function?
correct answers The PMF is suitable for discrete distributions whereas the PDF is appropriate for
continuous distributions
What is the difference between a PDF and a CDF? correct answers The CDF represents the
likelihood of an accumulation of events happening. The PDF is designed to evaluate the chance
of a range of values.
Is the CDF of a discrete distribution derivable? correct answers No. The CDF of a discrete
distribution often involves kinks, making it impossible to find a derivative for.
Assuming share prices are a continuous variable, what is the probability of Woodside share price
to be $40 next month? correct answers Zero. The probability of a particular value of a continuous
random variable is zero.
Define expected value, both mathematically and in simple words. correct answers The expected
value of a random variable (say, X) is the average of all possible values of Xweighted by the
, probabilities of observing each of these values. It is the first moment of the distribution, which is
a measure of central tendency of a distribution.
Mathematically:𝐸(𝑋) ={∑ 𝑥𝑖 ∗ Pr (𝑋 = 𝑥𝑖)𝑛𝑖=1, 𝑖𝑓 𝑋 𝑖𝑠 𝑑𝑖𝑠𝑐𝑟𝑒𝑡𝑒
∫𝑥𝑓(𝑥)𝑑𝑥∞−∞, 𝑖𝑓 𝑋 𝑖𝑠 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠
What do we mean when we say a normal distribution is fully characterised by its mean and
variance? correct answers We mean that we can fully draw the distribution if we know th exact
values of the mean and variance of a random variable. Notation-wise, if a random variable
follows a normal distribution, we write R~N(u,var). This implicitly indicates we need the two to
determine the exact distribution.
What does kurtosis measure? correct answers how flat or peaked the distribution is compared to
a normal curve. How fat the tails are. Intuitively, the chance of extreme events happening in
relation to the mean.
What is the skewness and kurtosis of a normal distribution? correct answers Skewness is zero
(perfectly symmetrical) and kurtosis is 3.
What is the difference between error term and residuals in regression? correct answers The error
term is a population concept while the residual is an estimate of what the error term is. The error
term is unobservable as we don't have access to the population so we must observe the residuals
to get an idea of how it behaves.
What are the 5 OLS assumtpions to produce high-quality estimators? correct answers -Linearity
in parameters
-Random sampling
-No perfect collinearity
-Zero conditional mean
-Homoskedasticity
What does it mean for an estimator to be unbiased? correct answers An estimator is unbiased if
its expected value is equal to the true (unobservable) value we are trying to estimate.
Formally: E(Estimator) = true value
What does it mean for an estimator to be consistent? correct answers An estimator is consistent
if, as the sample becomes larger and larger, the estimates get closer and closer to the true value.
Intuitively, a consistent estimator becomes better at guessing what the true value is the more
information we feed into it.
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