ABOUT THE COURSE: Over the next few decades, machine-learning (ML) and AI will transform not only the finance industry but also other industries that borrow significantly from finance. This program has been carefully designed to help future analysts, traders, brokers, consultants and other industry professionals who are either currently exposed to, or foresee artificial intelligence, machine-learning and data science proliferate their work environment. The operating environment for investment management firms continues to evolve, with technological innovations and shifting investor preferences at the heart of this change. In that context, Artificial Intelligence (AI) is providing new opportunities to both professionals and investors. The objective of this course is to understand the application of Artificial Intelligence and Machine Learning techniques in financial markets, trading, and asset management. This program aims to demonstrate the applications of AI-based models in the finance domain. This includes solving real-life wealth management problems to improve investment decisions with AI.INTENDED AUDIENCE:Management students (Ph.D. and MBA), Commerce students (B.Com., M.Com.), Chartered Accountants, Finance professionals (Investment analysts, banking professionals, accountants, credit analysts), Engineering graduates.INDUSTRY SUPPORT:Financial Analytics, Data Science & Data Analytics, Business Analytics, Banking & Financial Services, Consulting and Advisory firms, Investment Banks. Business analytics: Mu Sigma Analytics, Fractal Analytics, Manthan. Latent View, Tiger Analytics, Absolutdata, Convergytics, UST Global; Equity research firms, Credit rating firms, Investment Banks, Corporate Banking sector, Corporate Finance roles across all corporates (ICRA, ICICI, HDFC, Nomura, Lehman Brothers, SBI Capital Markets, Deutsche bank, HSBC Bank, etc.)
Overview
Syllabus
Week 1:Introduction to financial markets: Risk-Return Analysis in Investment Decisions – Measures of Risk and Return, understanding value of a firm, goals of a firm, cash flow discounting, making investment decisions, valuation of fixed income securities and common stocks, introduction to portfolio theory and asset pricing models, cost of capital.
Week 2:Overview of AI and machine learning models: Probability modelling, inferential statistics, Supervised and Unsupervised learning algorithms, regression and classification algorithms.
Week 3:Introduction to R Programming, R Fundamentals, Exploratory data analysis and data visualization with R. Statistical Analysis with R, Inferential statistics and hypothesis testing with R.
Week 4:Market Microstructure and Liquidity: Order-driven vs. Quote-driven markets, Market efficiency, Risk preferences, Limit order books, market microstructure types, economic theory of choice, interest rate compounding
Week 5:Portfolio construction: Portfolio risk and expected returns for two securities and multiple securities, risk diversification with portfolios, correlation structure, mean-variance framework, portfolio construction with R
Week 6:Portfolio Optimization: Portfolio Possibility curve, Efficient frontier, Minimum Variance portfolios, Introduction to risk-free lending and borrowing, market risk and beta, portfolio optimization with R
Week 7:Asset Pricing Models: Capital Asset Pricing Model (CAPM), Capital Market Line, Security Market Line, Fallings of CAPM, Single-Index and Multi-Index models, Expected Risk and Return with Index models, 3-Factor Fama-French Model
Week 8:Portfolio Management and Performance Evaluation: Portfolio Management strategies, Active vs Passive Portfolio Management, Value vs Growth investing, One-parameter performance measures Timing & Selection performance measures, application of asset pricing models in performance management
Week 9:Introduction to Algorithmic Trading: Technical analysis and trend determination, Dow Theory, Moving averages, Momentum indicators, Classical price patterns.
Week 10:AI and machine learning in Trading execution and portfolio management: Regression and Classification algorithm applications in security analysis, forecasting, and prediction, Case Study examples
Week 11:Advanced time-series regression algorithms: Panel regression quantile regression, ARMA/ARIMA models, Mean reverting trading strategies with vector error correction models and cointegration, model risk management, back testing, model validation, and stress testing with R
Week 12:Advanced time-series algorithms for financial risk-management: Value-at-risk, Expected Shortfall, ARCH/GARCH models, implementation with R
Week 2:Overview of AI and machine learning models: Probability modelling, inferential statistics, Supervised and Unsupervised learning algorithms, regression and classification algorithms.
Week 3:Introduction to R Programming, R Fundamentals, Exploratory data analysis and data visualization with R. Statistical Analysis with R, Inferential statistics and hypothesis testing with R.
Week 4:Market Microstructure and Liquidity: Order-driven vs. Quote-driven markets, Market efficiency, Risk preferences, Limit order books, market microstructure types, economic theory of choice, interest rate compounding
Week 5:Portfolio construction: Portfolio risk and expected returns for two securities and multiple securities, risk diversification with portfolios, correlation structure, mean-variance framework, portfolio construction with R
Week 6:Portfolio Optimization: Portfolio Possibility curve, Efficient frontier, Minimum Variance portfolios, Introduction to risk-free lending and borrowing, market risk and beta, portfolio optimization with R
Week 7:Asset Pricing Models: Capital Asset Pricing Model (CAPM), Capital Market Line, Security Market Line, Fallings of CAPM, Single-Index and Multi-Index models, Expected Risk and Return with Index models, 3-Factor Fama-French Model
Week 8:Portfolio Management and Performance Evaluation: Portfolio Management strategies, Active vs Passive Portfolio Management, Value vs Growth investing, One-parameter performance measures Timing & Selection performance measures, application of asset pricing models in performance management
Week 9:Introduction to Algorithmic Trading: Technical analysis and trend determination, Dow Theory, Moving averages, Momentum indicators, Classical price patterns.
Week 10:AI and machine learning in Trading execution and portfolio management: Regression and Classification algorithm applications in security analysis, forecasting, and prediction, Case Study examples
Week 11:Advanced time-series regression algorithms: Panel regression quantile regression, ARMA/ARIMA models, Mean reverting trading strategies with vector error correction models and cointegration, model risk management, back testing, model validation, and stress testing with R
Week 12:Advanced time-series algorithms for financial risk-management: Value-at-risk, Expected Shortfall, ARCH/GARCH models, implementation with R
Taught by
Prof. Abhinava Tripathi
