MindMap Gallery CFA-level one quantitative analysis mind map
CFA Level 1 Quantitative Analysis Mind Map, a comprehensive analysis of the CFA Level 1 quantitative analysis knowledge system; The significance of quantitative analysis: Use various mathematical models to analyze and predict what price a financial product will reach in the future. Make risk and return proportional through mathematical models. In addition to the study of this subject that must be completed in the CFA exam, quantitative analysis is also widely used by many multinational groups and international funds.
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This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Quantitative Methods
basic topics
time value of money
Introduction to interest rates
Application of three interest rates
required rate of return
discount rate
opportunity cost
The composition of interest rates
Nominal interest rate = Nominal risk-free interest rate Risk Premium
effective annual interest rate
How to convert to EAR
Continuous compound interest
Present value, future value and annuity
The relationship between PV and FV
PV and annuity
Calculator use
Descriptive Statistics
statistics
Statistical Concepts and Market Returns
basic concept
population & sample
measurement scales measurement scales
Central Tendency Central Tendency
mean mean
arithmetic mean arithmetic mean
Predict the future period
weighted mean weighted average
geometric mean geometric mean
Predicting multiple periods in the future (Multil-period)
harmonic mean harmonic mean
Used to calculate the average cost of buying a stock at a fixed price over a period of time
Median and Mode Median&Mode
Quantile
Dispersion
measures
Range Extremely Poor
Mean absolute deviation mean absolute difference
variance variance
Standard deviation standard deviation
Chebyshev's Inquality
Coefficient of Variation Coefficient of variation
sharp ratio
Skewness & Kurtosis
Skewness
Right deviation
Frequent small losses, a few extreme gains (small losses with multiple frequencies, large gains with small frequencies)
Left deviation
Frequent small gains, a few extreme losses (small gains with multiple frequencies, large losses with small frequencies)
kurtosis
probability theory
basic concepts
Relationship among Events Relationship between events
Mutually exclusive events Mutually exclusive events (cannot occur at the same time)
Exhaustive events Traverse events, complementary, activities cover all possible results
Independent events independent events
Dependent events Dependent events
Types of Probablily Probability types
Empirical probability Empirical probability (the past predicts the future)
Priori probability prior probability (pushing past)
Subjective probability Subjective probability (I think...)
Unconditional & Conditional Probability Unconditional Probability & Conditional Probability
Odds Odds
Algorithms of Probability
Multiplication Rule
Addition Rule
Two Important Rules
Total Probability Rules
Bayes’ Formula
Application of Probability in Investment Application of Probability in Investment
Expected Value
Probability Weighted Variance
Covariance covariance
Cov(X,Y) = E[(X -EX) (Y - EY)]
Correlation correlation coefficient
Permutation and Combination
Labeling grouping
Permutation
Combination
distributed
basic concept
discrete discrete/continuous continuous distrbution
Probability function
Probability density function
Cumulative probability function
Discrete Distributions Discrete Distributions
Discrete Uniform Distribution Discrete Uniform Distribution
Binomial Distribution Binomial Distribution
X~B(n,p)
Bernoulli random variable Bernoulli random variable
Expected value for Bernoulli random variable=P
Variance for Bernoulli random variable = p(1-p)
Expected value for binomial random variable = np
Variance for binomial random variable = np(1-p)
Continuous Distributions Continuous Distributions
Normal Distribution
Properties
Standardization
Lognormal Distribution
Other Topics
Continuous Uniform Distribution
Students T-distribution
Shortfall Risk & Safety-first Ratio
Simulation
Monte Carlo Simulation
historical Simulation historical simulation
inferential statistics
Sampling and Estimation
Sampling
Sampling Method
Simple random sampling Simple random sampling
Stratified random sampling stratified sampling
Sampling error Sampling error
Sampling Biases Sampling Biases
Data-mining bias data mining
Sample selection bias sample selection bias
Survivorship bias Survivorship bias
Look-ahead bias
Time-period bias time interval bias
Estimation
Central Limit Theorem Central Limit Theorem
Properties of an Estimator The good properties of an estimator
Unbiasedness (most important)
Consistency
Efficiency
premise and conclusion
premise: 1. Simple random sampling 2. The sample size is large enough
Standard Error
Standard error of sample mean Standard deviation of sample mean
Point Estimate &Interval Estimate Point Estimate and Interval Estimate
Point Estimate
Interval Estimate
Confidence interval (1-α) confidence interval
Significance level (α) significance level
Factors on Width of Confidence Interval
judgment
Hypothesis Testing
steps and Basic Concepts
Steps of Hypothesis Testing
Null hypothesis & Alternative Hypothesis alternative hypothesis
Key Concepts in Hypothesis Testing
Two-tailed Test vs One-tailed Test
Type I error & Type ll error
Type I error: Rejection
P (Type I Error) = Significance level α
Type II error: pseudo error
P(Type II Error) = β
Power of test = 1 – P(Type II Error) = 1 – β
Hypothesis Test
Single Mean
population mean with known variance
population mean with unknown variance
Other Situations
Difference of Means
Variances are unknown but assumed to be equal, T distribution, df=n1 n2-2
Mean Differences
The variance is unknown, assumed to be unequal, T distribution, df=n-1
Single Variance
df=n-1
Equality of Two Variances
Correlation
Bilateral inspection
Parametric Tests vs. Nonparametric Tests Parametric Tests vs. Nonparametric Tests