FINANCIAL ANALYTICS

Paper Code:

MBB426

Credits:

Contact Hours:

60.00

Max. Marks:

100.00

Objective:

The course will enable students to apply financial analytics techniques, including portfolio optimization, market sentiment analysis, trading strategy simulation, and option pricing models, using tools like R and computational finance principles.

Course Outcomes:

Course

Learning outcome

(at course level)

Learning and teaching

strategies

Assessment Strategies

Course Code

Course Title

24MBB426

Financial Analytics

(Practical)

CO755: Read financial documents and

compute basic financial statistics using R.

CO756: Import data sets and apply various visualization techniques.

CO757: Recognize and relate the concept of Risk Diversification and management through different portfolio models.

CO758: Apply the simulating trading strategies.

CO759: Comprehend and apply the Option pricing models.

CO760: 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:

Data Mining and Market Sentiment

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:

Simulating 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:

Binomial Model for Options

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, TV12 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/106106122

JOURNALS:

●https://vciba.springeropen.com/

●https://appliednetsci.springeropen.com/ ●https://epjdatascience.springeropen.com/

Academic Year:

2024-2025