Quantitative Methods

Résumé

Quantitative Methods provides the statistical and time-value toolkit used across the curriculum. Time value of money links present and future cash flows through compounding and discounting, and supports the valuation of single sums, annuities, and perpetuities, as well as net present value and internal rate of return. Rates of return are described using holding-period, arithmetic mean, geometric mean, money-weighted (IRR), and time-weighted returns, plus annualized and continuously compounded measures. Statistical concepts cover measures of central tendency, dispersion (variance, standard deviation, mean absolute deviation), coefficient of variation, skewness, and kurtosis, applied to asset returns. Probability concepts include conditional and joint probability, expected value, covariance and correlation, Bayes' formula, and counting rules (permutations and combinations). Common distributions are the uniform, binomial, normal, lognormal, and Student's t-distribution; the normal distribution underlies confidence intervals and the standard normal (z) transformation. Sampling and estimation introduce the central limit theorem, the standard error of the sample mean, point and interval estimates, and resampling methods such as the bootstrap. Hypothesis testing follows a structured procedure (state hypotheses, choose a test statistic, set significance, decide), covering tests of means, variances, and correlations, plus parametric and non-parametric tests. Simple linear regression explains a dependent variable with one independent variable, producing slope and intercept estimates, the coefficient of determination, and tests of significance. The topic closes with an introduction to big data and fintech techniques used in modern investment analysis.