ABOUT THE COURSE:Over the next few decades, Data Analytics 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 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, Data Analytics is providing new opportunities to both professionals and investors. The objective of this course is to understand the application of Data Analytics in financial markets, trading, and asset management, also called Financial Analytics. This program aims to demonstrate the applications of data analytics in the finance domain. This includes solving real-life financial markets problems with data science.INTENDED AUDIENCE: Management students (Ph.D. and MBA), Commerce students (B.Com, M.Com.), Chartered Accountants, Science (B.Sc., M.Sc.), and Engineering students (B-Tech, M-Tech) Finance professionals (Investment analysts, banking professionals, accountants, credit analysts), Data ScientistsPREREQUISITES: Week 1: Artificial Intelligence (AI) for Investments (NPTEL);INDUSTRY SUPPORT: Data Science and Business analytics: Mu Sigma Analytics, Fractal Analytics, Manthan.Latent View, Tiger Analytics, Absolutedata, 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: Fundamentals of R Programming and Introduction to Business Statistics: Data Visualization and Wrangling, working with data frames, processing large data, Statistical Inference, Hypothesis Testing, and Confidence Intervals, Application with RWeek 2:Time-Series Analytics: Introduction to Stationarity, ARMA/ARIMA Modelling, ACF/PACF, Model Building and Goodness-of-Fit, Modelling Non-stationary process, Cointegration and VECM Models, Time-series forecasting, Implementation in RWeek 3:Portfolio Analytics: Portfolio Optimization with two securities and multiple securities, Construction of efficient frontier and market portfolio, Portfolio performance evaluation and construction of market portfolio, Asset Pricing Models, Implementation in RWeek 4:Application of Regression: Introduction to regression modelling, Simple and Multiple Linear Regression, Assumptions of classical linear regression model and its violations, issues of heteroscedasticity, multicollinearity, autocorrelation, Application with asset pricing models, and implementation with RWeek 5:Risk Analytics: Introduction to Volatility Modelling, Historical volatility models, ARCH/GARCH Models, VaR/CvaR models, Implementation in R Week 6:Logistic Regression: Linear probability models, Logit Model and Probit Models, ROC curve, classification matrix, Maximum Likelihood Estimation, Finance Use case and implementation in RWeek 7:Panel Data Regression: Introduction to Panel Models, Fixed effects, Random effects, First difference, LSDV estimators, Hausman test statistics, Finance Use case and implementation in RWeek 8:Quantile Regression: Introduction to quantile regression, regression quantiles, optimization scheme with quantile regression, theoretical underpinnings, Finance use case with R implementationWeek 9:Markov Regime Switching Regression: Introduction to Markov Process, Transient and Recurrent processes, absorption probabilities, Convergence, Finance use case and implementation in RWeek 10:Financial Markets Data Visualization with GGPLOT: Basics of GGPLOT, Layering, Facet wrap, aesthetics, geometric objects, Use case with R implementationWeek 11:Technical Analysis: Trend Analysis and Indicators, Bollinger bands, trendlines, candle stick charts, Dow theory, classical patterns, Momentum Indicators, R implementation Week 12:Fixed Income securities: Bond fundamentals, G-Secs, Duration, Convexity, application in portfolio management, Use case with R implementation
Taught by
Prof. Abhinava Tripathi
