FINANCIAL ANALYTICS

Paper Code: 
MFM426
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

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

title

24MFM426

 

Financial Analytics

(Practical)

CO 713.  Interpret financial documents and compute basic financial statistics using R or Excel

CO 714.  Apply various visualization  techniques.

CO 715.  Evaluate the concept of Risk Diversification and management through different portfolio models.

CO 716.   Apply the simulating trading strategies.

CO 717. Comprehend and apply the Option pricing models.

CO 718. 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: 
2024-2025
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