B.Com. (Investment Management) Data Driven Investment Strategy Syllabus - Mumbai University
This is the SY BCom IM syllabus under NEP 2020, in force from the academic year 2025-26. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.
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Syllabus for Data-Driven Investment Strategy
Module I: Fundamentals of Quantitative Methods
- Unit 1: Introduction to Quantitative Methods
- Importance of Quantitative Methods in Investment Management
- Types of Data: Time Series, Cross-Sectional, Panel Data
- Data Visualization Techniques for Financial Analysis
- Introduction to Financial Modeling and Excel Tools
- Unit 2: Descriptive and Inferential Statistics
- Measures of Central Tendency and Dispersion
- Probability Distributions: Normal, Binomial, Poisson, Lognormal
- Hypothesis Testing and Confidence Intervals
- Correlation and Regression Analysis in Finance
Module II: Time Series Analysis and Forecasting
- Unit 3: Time Series Analysis for Financial Markets
- Concepts of Stationarity and Random Walks
- Moving Averages and Exponential Smoothing Techniques
- Autoregressive (AR) and Moving Average (MA) Models
- Application of Time Series Analysis in Stock Price Prediction
- Unit 4: Forecasting Models in Finance
- ARIMA and GARCH Models for Volatility Forecasting
- Monte Carlo Simulations for Investment Decision-Making
- Economic Indicators and Macroeconomic Forecasting
- Case Study: Predicting Stock Market Trends
Module III: Quantitative Risk Management & Portfolio Optimization
- Unit 5: Risk Measurement and Management
- Value at Risk (VaR) and Expected Shortfall (CVaR)
- Portfolio Risk Metrics: Standard Deviation, Beta, Drawdowns
- Hedging Techniques Using Derivatives (Futures & Options)
- Scenario Analysis and Stress Testing
- Unit 6: Portfolio Optimization Techniques
- Modern Portfolio Theory (MPT) and Efficient Frontier
- Capital Asset Pricing Model (CAPM) and Factor Models
- Mean-Variance Optimization and Sharpe Ratio Maximization
- Black-Litterman Model for Portfolio Allocation
Module IV: Machine Learning and Computational Finance
- Unit 7: Quantitative Trading and Algorithmic Strategies
- Introduction to Algorithmic Trading and High-Frequency Trading
- Statistical Arbitrage and Pairs Trading Strategies
- Sentiment Analysis Using Text Mining in Finance
- Backtesting and Performance Evaluation of Trading Models
- Unit 8: Emerging Trends in Quantitative Finance
- Role of Artificial Intelligence and Machine Learning in Investments
- Blockchain and Cryptocurrencies – Quantitative Perspectives
- ESG Investing and Quantitative Methods for Sustainable Finance
- Applications of Python, R, and MATLAB in Quantitative Finance
Reference Books
- 1 Investment Management – V.K. Bhalla
- 2 Quantitative Techniques for Investment Analysis – J.R. Varma
- 3 Investment Analysis and Portfolio Management – Prasanna Chandra
- 4 Quantitative Methods for Finance and Investments – Umesh Kumar
- 5 Financial Econometrics: Models and Methods – Chiranjit Mukhopadhyay
Reproduced from the University of Mumbai syllabus for B.Com. (Investment Management) under NEP 2020, in force from the academic year 2025-26. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.