CFA - Exams| Questions with 100% Verified
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Hypothesis testing - ✔️✔️the hypothesis testing process requires a statement of a
null and an alternative hypothesis, the selection of the appropriate test statistic,
specification of the significance level, a decision rule, the calculation of a sample
statistic, a decision regarding the hypotheses based on the test, and a decision
based on the test results
null hypothesis - ✔️✔️is what the researcher wants to reject. the alternative
hypothesis is what the researcher wants to prove, and it is accepted when the null
hypothesis is rejected
two-tailed test - ✔️✔️results from a two-sided alternative hypothesis (=,does not =)
one-tailed test - ✔️✔️results from a one-sided alternative test (>,<)
test statistic - ✔️✔️is the value that a decision about a hypothesis will be based on
= (sample mean - hypothesized mean) / std error sample mean
Type I and II errors - ✔️✔️is the rejection of the null hypothesis when it is actually
true, while a Type II error is the failure to reject the null hypothesis when it is
actually false.
the significance level can be interpreted as the probability that a test statistic will
reject the null hypothesis by chance when it is actually true (i.e., the probability of
,a Type I error). A significance level must be specified to select the critical values
for the test.
hypothesis testing - ✔️✔️compares a computed test statistic to a critical value at a
stated level of significance, which is the decision rule for the test
the power of a test - ✔️✔️is the probability of rejecting the null when it is false. the
power of a test = 1 - P(type ii error)
a hypothesis about a population parameter is rejected when... - ✔️✔️the sample
statistic lies outside a confidence interval around the hypothesized value for the
chosen level of significance
Statistical significance does not necessarily imply economic significance. -
✔️✔️Even though a test statistic is significant statistically, the size of the gains to a
strategy to exploit a statistically significant result may be absolutely small or
simply not great enough to outweigh transactions costs.
the p-value - ✔️✔️the p-value for a hypothesis test is the smallest significance level
for which the hypothesis would be rejected. for example, a p-value of 7% means
the hypothesis can be rejected at the 10% significance level but cannot by
rejected at the 5% significance level.
For two independent samples from two normally distributed populations... -
✔️✔️the difference in means can be tested with a t-statistic. When the two
population variances are assumed to be equal, the denominator is based on the
variance of the pooled samples, but when sample variances are assumed to be
unequal, the denominator is based on a combination of the two samples'
variances.
,paired comparisons test - ✔️✔️is concerned with the mean of the differences
between the paired observations of two dependent, normally distributed
samples. A t-statistic, t=d/s, where s=s/sq rt n and d is the average difference of
the n paired observations, is used to test whether the means of two dependent
normal variables are equal. values outside the critical t-values lead us to reject
equality.
the test of a hypothesis about the population variance for a normally distributed
population uses a chi-square test statistic - ✔️✔️(n-1)s^2 / population variance, df
= n-1
the test comparing two variances based on independent samples from two
normally distributed populations uses an F-distributed test statistic - ✔️✔️F = s21 /
s22, where s1 is the variance of the first sample and s2 is the (smaller) variance of
the second sample
Parametric tests - ✔️✔️like the t-test, F-test, and chi-square tests, make
assumptions regarding the distribution of the population from which samples are
drawn. Nonparametric tests either do not consider a particular population
parameter or have few assumptions about the sampled population.
Nonparametric tests are used when the assumptions of parametric tests can't be
supported or when the data are not suitable for parametric tests.
Underlying all of technical analysis are the following assumptions: - ✔️✔️-prices are
determined by investor supply and demand for assets
-while the causes of changes in supply and demand are difficult to determine, the
actual shifts in supply and demand can be observed in market prices
, -prices move in trends and exhibit patterns that can be identified and tend to
repeat themselves over time
Technical analysts... - ✔️✔️use charts to identify trends and patterns in prices over
time. A line chart is a continuous line that connects closing prices for each period.
Bar charts and candlestick charts show the open, high, low, and close for each
period. Volume charts often accompany price charts. Point-and-figure charts
indicate significant changes in the direction of price trends.
In an uptrend, - ✔️✔️prices are reaching higher highs and higher lowers. An
uptrend line is drawn below the prices on a chart by connecting the increasing
lows with a straight line.
In a downtrend, - ✔️✔️prices are reaching lower lowers and lower highs. A
downtrend line is drawn above the prices on a chart by connecting the decreasing
highs with a straight line.
Support and resistance - ✔️✔️are price levels or ranges at which buying or selling
pressure is expected to limit price movement. Commonly identified support and
resistance levels include trendlines and previous high and low prices.
The change in polarity principle - ✔️✔️is the idea that breached resistance levels
become support levels and breached support levels become resistance levels.
Technical analysts - ✔️✔️look for recurring patterns in price charts. Head-and-
shoulders patterns, double tops, and triple tops are thought to be reversal
patterns at the ends of uptrends. Inverse head-and-shoulders patterns, double
bottoms, and triple bottoms are thought to be reversal patterns at the ends of
downtrends.
