FINANCIAL ANALYTICS (Practical)

Paper Code: 
25MFM426
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This course will develop understanding on financial statements, statistical analysis, portfolio optimization, trading strategies, and options pricing models using computational finance tools and techniques.

Course Outcomes: 

Course Outcomes (Cos):

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

title

25MFM426

 

Financial Analytics

(Practical)

CO637: Interpret financial documents and compute basic financial statistics using R or Excel

CO638: Apply various visualization techniques.

CO639: Evaluate the concept of Risk Diversification and management through different portfolio models.

CO640: Apply the simulating trading strategies.

CO641: Comprehend and apply the Option pricing models.

CO642: Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study

Learning activities for the students:

Self-learning assignments,

presentations

.Class test, Semester end examinations, Quiz, Assignments, Presentation

 

12.00
Unit I: 
Introduction to Financial Analytics

Introduction: Meaning-Importance of Financial Analytics, Documents used in Financial Analytics: Balance Sheet, Income Statement, Cash flow statement, Elements of Financial Health: Liquidity, Leverage, Profitability.
Financial Statistics: Concept and mathematical expectation, Probability, Mean, SD and Variance, Skewness and Kurtosis, Covariance and correlation, Financial Returns, Capital Asset Pricing model.

12.00
Unit II: 
Financial Securities

Financial Securities: Bond Investments, Stock Investments, Securities Data Sets and visualization, Securities data set importing and cleansing, Plotting multiple series, adjusting for stock splits & Mergers, generating prices from log returns.
Application of Sharpe Ratio using R

12.00
Unit III: 
Portfolio Optimization

Markowitz means - variance optimization: Optimal Portfolio of two risky assets, Data mining with Portfolio optimization.
Gauging the market Sentiment: Markov Regime Switching model, Reading the market data, Bayesian reasoning, Beta distribution, Prior and posterior distributions, Momentum graphs

12.00
Unit IV: 
Simulation Trading Strategies

Simulating Trading Strategies: Foreign exchange markets, Chart analytics, Initialization and finalization - Bayesian Reasoning within Positions, Entries, Exits, Profitability, Short term volatility, The State Machine

12.00
Unit V: 
Option Pricing

Binomial Model for Options: Applying computational finance, Rsik Neutral Pricing and No Arbitrage, High Risk Free Rate Environment, Put Call Parity, From Binomial to Log-normal.
Black - Scholes model and option - Implied volatility: Black - Scholes model: Concept and applications, Derivation - Algorithm for Implied volatility.
*Case studies related to entire topics are to be taught.

Essential Readings: 

• Mark J. Bennett, Dirk L. Hugen, Financial Analytics with R, Cambridge University Press
• Vikas Raj, Business Analytics and Financial Planning, TV18 Broadcast Ltd

References: 

Suggested readings
• James, E.R. (2017). Business Analytics. UK: Pearson Education Limited

E-RESOURCES:
● https://www.jigsawacademy.com/blogs/business-analytics/
● https://nptel.ac.in/courses/106106182

JOURNALS:
● Journal of Financial Economics
● Journal of Finance

Academic Year: 
2025-2026
  • Printer-friendly version
  • PDF version