Financial Analytics

Financial data types and sources; data cleaning and preprocessing; descriptive analytics; pivot tables and dashboards in Spreadsheet; financial data visualisation using charts; introduction to data-driven financial decision-making; applications of analytics in fraud detection, risk profiling, and pricing analytics.

Financial modelling concepts; Spreadsheet/Python/R-based modelling; computation of returns, portfolio returns, and risk measures; regression-based financial analysis; factor models; model building for investment decision-making; introduction to model validation techniques.

Risk measurement techniques; variance, standard deviation, beta; portfolio risk-return analysis; diversification principles; stress testing and scenario analysis; CAPM-based analytics; interpretation of financial risk indicators for decision support.

Time-series data structure; trend, seasonal, cyclical components; moving averages and exponential smoothing; least squares forecasting; trend projection models; application of time-series techniques in stock prices, returns, and macro-financial variables.

Introduction to Autocorrelation and Autoregressive Integrated Moving Average (ARIMA); model selection and validation; comparative forecasting methods; financial interpretation of forecasts for strategic decision-making. Practical Activities 1. Creating bar charts, pie charts, line charts, and dashboards using Excel for financial data visualization. 2. Calculating stock returns, mutual fund returns, and portfolio returns using spreadsheet formulas. 3. Portfolio risk analysis measures such as average return, standard deviation, and diversification using sample investment data. 4. Time-series forecasting using Excel for moving averages and trend analysis to forecast sales, stock prices, or exchange rates.