Financial Analytics Notes
Subject Overview
The fifth Business Analytics group elective, covering financial data wrangling and time series basics, volatility modeling and risk measurement, regression models and credit risk/
Subject at a glance
4
Units
25
Topics
4
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Role of analytics in finance
- types of financial data
- data wrangling and cleaning
- exploratory data analysis
- time series components
- decomposition
- stationarity
- ACF/PACF
- introduction to ARMA/ARIMA
- 1
Clean price data
- 2
Compute returns
Log or simple
- 3
Explore
Plots, summary stats
- 4
Test stationarity
ADF test
- 5
Fit ARMA or ARIMA
- 6
Check residuals
Study guide
This unit covers analytics in finance, financial data types, wrangling, exploratory analysis, time series components, decomposition, stationarity, ACF and PACF, ARMA and ARIMA. Clean a real price data set.
- ARCH/GARCH volatility models
- volatility clustering
- statistical distributions and Value at Risk estimation
- hypothesis testing in finance
- Monte Carlo simulation for risk analysis and option pricing
Parametric VaR
Z × σ × portfolio value
Historical VaR
Loss at the chosen percentile of past returns
GARCH(1,1)
σ²ₜ = ω + α ε²ₜ₋₁ + β σ²ₜ₋₁
Monte Carlo
Simulate many paths, read off the loss distribution
Study guide
You study ARCH and GARCH, volatility clustering, distributions, Value at Risk, hypothesis testing and Monte Carlo simulation for risk and option pricing. Simulate a simple price path.
- Regression models and factor models in finance
- forecasting financial variables
- logistic regression for credit scoring
- ROC curves and confusion matrix
- Benford's Law for fraud detection
- K-means clustering for customer segmentation
Credit scoring
Logistic regression
Probability of default
Model quality
ROC curve, confusion matrix
AUC, accuracy
Fraud detection
Benford's Law
Suspicious digit patterns
Customer segmentation
K-means clustering
Groups of similar clients
Study guide
You study regression and factor models, forecasting, logistic regression for credit scoring, ROC curves, confusion matrices, Benford's Law for fraud and K-means for segmentation. Build a simple credit scoring model.
- Portfolio returns
- risk and performance metrics (Sharpe ratio)
- Markowitz mean-variance framework and efficient frontier
- stress testing and backtesting
- visualization and reporting of financial insights
Portfolio return
Σ wi Ri
Portfolio variance
wᵀ Σ w
Sharpe ratio
(Rp − Rf) / σp
Efficient frontier
Highest return for each level of risk
Study guide
You study portfolio returns and risk, the Sharpe ratio, Markowitz and the efficient frontier, stress testing, backtesting and reporting. Plot an efficient frontier for a few assets.
Why Financial Analytics Matters
Quantitative finance and risk analytics are among the highest-paying MBA paths, and Python and R skills are in demand.
How to Prepare for Financial Analytics
- Work with real stock and credit data.
- Learn VaR and Monte Carlo simulation.
- Build a small portfolio analytics report.
Financial Analytics: Frequently Asked Questions
What is Value at Risk?
VaR estimates the maximum loss a portfolio is likely to suffer over a period at a given confidence level.