1.1) Quantitative Methods - Holding Period Return
1.2) Annualised Return
1.3) Quantitative Methods - Continuously Compounded Return
where 10% for example will be 0.1 for Rcc or HPR
1.4) Quantitative Methods - Arithmetic Mean and Geometric Mean
Arithmetic Mean - Sum of all observation values in sample / population, divided by number of observations.
Geometric Mean - Used when calculating investment returns over multiple periods or to measure compound growth rates.
1.5) Quantitative Methods - Geometric Mean Return
1.6) Quantitative Methods - Harmonic Mean
1.6b) Quantitative Methods - Assuming at least some variation in a set of data, order from largest to smallest: Arithmetic Mean, Geometric Mean, Harmonic Mean
1) Largest: Arithmetic Mean,
2) Middle: Geometric Mean,
3) Smallest: Harmonic Mean
1.6c) Quantitative Methods - Relationship between Arithmetic mean, geometric mean, and harmonic mean
1.7) Quantitative Methods - Trimmed Mean (x %)
Exclude highest and lowest x/2 percent of observations.
1.8) Quantitative Methods - Winsorized Mean (x %)
Substitute values for highest and lowest x/2 percent of observations.
1.9) Quantitative Methods - Variance
Average of Squared Deviations from the mean. (Standard Deviation is the square root of variance).
1.10) Quantitative Methods - Target Downside Deviation
1.11) Quantitative Methods - Coefficient of Variation
Measures dispersion relative to mean; allows comparison across data sets.
1.12) Quantitative Methods - Bayes’ Formula
The numerator can be simplified to the probability of A and B (A n B)
1.13) Quantitative Methods - Expected Return
1.14) Quantitative Methods - Probabilistic Variance
1.15) Quantitative Methods - Correlation
Correlation: covariance divided by product of the two standard deviations
1.16) Quantitative Methods - Expected Return of a 2-stock portfolio
1.17) Quantitative Methods - Variance of a 2-stock portfolio
1.18) Quantitative Methods - Roy’s Safety First Ratio
1.19) Quantitative Methods - Normal Distribution - % of observations that fall between…
1.20) Quantitative Methods - Computing Z-scores
Z-score standardises observations from normal distribution; represents number of standard deviations a given observation is from population mean.
1.21) Quantitative Methods - Central Limit Theorem
When selecting simple random samples of size n from population with mean mu and finite variance (sigma squared), the sampling distribution of sample mean approaches normal probability distribution with mean mu and variance equal to sigma squared over n as the sample becomes large.
1.22) Quantitative Methods - Standard error with population variance known.