Artificial Intelligence (AI) for Investments

By Prof. Abhinava Tripathi   |   IIT Kanpur
Learners enrolled: 1148
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.)
Course Status : Upcoming
Course Type : Elective
Duration : 12 weeks
Start Date : 23 Jan 2023
End Date : 14 Apr 2023
Exam Date : 29 Apr 2023 IST
Enrollment Ends : 30 Jan 2023
Category :
  • Management Studies
Credit Points : 3
Level : Undergraduate/Postgraduate

Page Visits

Course layout

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

Books and references

1. Machine Learning in Finance by M. Dixon, I Halperin, and P. Bilokon, Springer, 1st Edition
2. Advances in Financial Machine Learning, Marcos Lopez, Wiley, 1st Edition
3. Machine Learning for Asset Managers, Marcos Lopez, Cambridge University Press, 1st Edition
4. Machine Learning for Algorithmic Trading, Stefan Jansen, 2nd Edition, Packt
5. Elton & Gruber, “Modern Portfolio Theory”, Wiley, 9th Edition
6. Reilly, Frank,K., “Investment Analysis and Portfolio Management,” 5th Edition, Dryden.

Instructor bio

Prof. Abhinava Tripathi

IIT Kanpur
Prof. Abhinava Tripathi is a Faculty of Finance and Accounting at Indian Institute of Technology, Roorkee. He has completed his Ph.D. degree from Indian Institute of Management, Lucknow. He has done his B-Tech. from Indian Institute of Technology, Roorkee and MBA from Indian Institute of Management, Kozhikode. He has more than 5 years of industry experience in investment banking, corporate banking, credit rating, and project finance advisory firms. His current research focuses on the subject of market-microstructure and liquidity in financial markets. Prof. Abhinava Tripathi has published research papers in international refereed journals, including the Journal of Asset Management, Studies in Economics and Finance, Finance Research Letters, and Managerial Finance.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 29 April 2023 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kanpur. It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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