Quantitative Methods for Finance & Investment Training Workshop

Course overview

The Quantitative Methods for Finance & Investment Training Workshop equips participants with practical mathematical, statistical, econometric, and analytical skills for evaluating financial performance, analysing investment opportunities, measuring risk, and supporting evidence-based financial decisions.

The programme covers descriptive statistics, probability, statistical inference, regression analysis, financial mathematics, investment return calculations, portfolio risk and diversification, portfolio optimisation, time-series analysis, investment appraisal, and financial risk measurement. Participants learn to apply quantitative methods to real-world financial datasets and interpret the results for investment planning, portfolio management, corporate finance, and risk assessment.

Course objectives

• Apply descriptive statistics, probability, statistical inference, and hypothesis testing to financial and investment datasets.
• Develop financial analysis reports, quantitative models, and interactive dashboards using Excel, SPSS, Stata, and Power BI.
• Apply quantitative methods to portfolio diversification, asset allocation, investment appraisal, valuation, and financial risk measurement.
• Use regression, time-series analysis, and econometric techniques to investigate financial relationships and evaluate investment-related trends.
• Calculate and interpret investment returns, volatility, covariance, correlation, and risk-adjusted performance measures.

Who should attend

Course content

• Module 1: Foundations of Quantitative Finance and Investment Analysis Role of quantitative methods in finance, applications in investment decision-making, financial datasets and variables, measurement scales, quantitative versus qualitative analysis, fin
• Module 2: Descriptive Statistics for Financial Data Mean, median, mode, range, variance, standard deviation, percentiles, quartiles, skewness, kurtosis, frequency distributions, outlier detection, distribution analysis, financial performance summaries, a
• Module 3: Probability Theory and Financial Distributions Probability rules, conditional probability, expected values, random variables, probability distributions, normal and lognormal distributions, binomial distributions, expected return, variance, cova
• Module 4: Statistical Inference and Hypothesis Testing Sampling methods, sampling distributions, standard errors, confidence intervals, hypothesis formulation, significance testing, t-tests, chi-square tests, comparison of financial performance between g
• Module 5: Financial Mathematics and Investment Return Analysis Simple and compound interest, present and future value, discounting, compounding periods, effective interest rates, annuities, perpetuities, loan amortisation, holding-period returns, annuali
• Module 6: Correlation, Covariance and Financial Relationships Measuring relationships between financial variables, Pearson and Spearman correlation, covariance matrices, interpreting positive and negative relationships, diversification benefits, correlat
• Module 7: Regression Analysis for Finance and Investment Simple and multiple linear regression, dependent and explanatory variables, coefficient interpretation, goodness of fit, statistical significance, residual analysis, regression assumptions, financi
• Module 8: Time-Series Analysis and Financial Trends Time-series data preparation, trend analysis, moving averages, seasonality, stationarity, autocorrelation, time-series decomposition, regression-based forecasting, introductory ARIMA modelling, and anal
• Module 9: Investment Risk and Return Measurement Expected returns, historical returns, variance, standard deviation, downside risk, systematic and unsystematic risk, beta, correlation, tracking error, risk-return trade-offs, and interpretation of investm
• Module 10: Portfolio Theory and Diversification Portfolio return calculations, portfolio variance, covariance and correlation, asset allocation, diversification, risk contribution, minimum-variance portfolios, efficient frontier concepts, investor risk p
• Module 11: Asset Pricing and Investment Performance Evaluation Capital Asset Pricing Model (CAPM), risk-free rates, market risk premiums, beta estimation, expected return, Jensen's alpha, Sharpe ratio, Treynor ratio, benchmark comparisons, risk-adjusted
• Module 12: Investment Appraisal and Capital Budgeting Net present value (NPV), internal rate of return (IRR), payback period, discounted payback, profitability index, cost of capital, project cash flows, investment scenario analysis, comparing investment
• Module 13: Financial Risk Measurement and Stress Testing Value at Risk (VaR) concepts, historical simulation, parametric risk measurement, Monte Carlo simulation concepts, sensitivity analysis, stress testing, scenario analysis, market risk factors, inte
• Module 14: Quantitative Financial Analysis Using Excel Financial formulas, descriptive statistics, return calculations, covariance and correlation matrices, regression tools, probability calculations, investment appraisal, portfolio risk calculations, se
• Module 15: Statistical and Econometric Analysis Using SPSS and Stata Data preparation, descriptive statistics, hypothesis testing, correlation analysis, regression modelling, time-series analysis, stationarity testing, econometric interpretation, diagnos
• Module 17: Integrated Quantitative Finance and Investment Capstone Participants analyse a realistic investment dataset, calculate returns and risk measures, investigate financial relationships, compare investment alternatives, conduct portfolio analysis,

General notes

• The workshop integrates Microsoft Excel, IBM SPSS Statistics, Stata, and Microsoft Power BI. Excel supports financial calculations and investment modelling, SPSS facilitates statistical analysis, Stata supports econometric modelling, and Power BI enables interactive financial reporting and visualisation. The curriculum reflects established quantitative finance topics, including portfolio mathematics, expected return and variance, regression, portfolio optimisation, asset pricing, and investment risk measurement.
• Investment Portfolio Evaluation: Compare the returns, volatility, correlations, and diversification benefits of several investment assets using Excel, SPSS, and Power BI.
• Stock Market Risk Analysis: Analyse historical share prices and returns, estimate beta, evaluate risk-adjusted performance, and compare results against a market benchmark.
• Loan and Credit Portfolio Analysis: Examine loan performance, interest income, repayment patterns, and selected risk indicators using statistical analysis and visual reporting.
• Capital Investment Appraisal: Evaluate competing projects using NPV, IRR, discounted cash flows, sensitivity analysis, and alternative investment scenarios.
• Financial Market and Economic Analysis: Investigate relationships between interest rates, inflation, exchange rates, and selected financial indicators using correlation, regression, and time-series methods.
• Expected Practical Outputs Participants will work towards producing a financial data analysis workbook, statistical summary report, investment return calculator, probability and risk analysis, correlation matrix, regression analysis report, time-series analysis, CAPM and beta estimation worksheet, portfolio risk and diversification analysis, investment appraisal model, sensitivity analysis, financial risk assessment, portfolio performance dashboard, and an integrated quantitative investment report.

Frequently asked questions

Schedule & fees