Statistical Methods in Finance 2022

The StatFin2022 "Artificial Intelligence in Finance" Workshop aims to provide participants exposure to the Principles, Techniques and Tools of Artificial Intelligence including their applications to quantitative finance.

Date and Venue

28-30 June, 2022
Virtual Platform

Who Can Apply?

• PhD students, Post-doctoral researchers and faculty in mathematics, statistics, business, economics and physics.
• Participants from the industry are encouraged to apply with a paper to be considered for presentation in the contributed paper sessions.
• Bachelors and Masters students can apply as well, provided at least one full course on Statistics is taken by the student.

Prerequisites

Some basic computing knowledge (and coding experience in some language like R/Python) is recommended. Basic concepts of statistics, probability, linear algebra and high School calculus will be very helpful. Creative thinking, learnability and "can do" attitude is extremely important.

Participants will be doing hands-on projects with Python.

Resouce Person:

• Ananya Lahiri, IIT Tirupati
• Sourish Das, CMI

Language & Platform:

• Python
• Google Colab
• GitHub

Indian Standard Time (IST)	28th June, 2022 Workshop Day 1
14:00-15:15	Module 1: Introduction to the landscape of finance and artificial intelligence. Data wrangling: simulating model stock price data; importing real data; creating return / relative return / log return data; creating weighted portfolio, calculating portfolio return; utility function, expected utility; calculating volatility, moments and quantiles; plotting histogram and density; creating other features / variables: Rolling average, Rolling maximum, minimum and order statistic; exploratory analysis.
15:30- 16:45	Module 2: Mean variance portfolio theory and efficient frontier; capital asset pricing model and linear regression; Arbitrage pricing theory and multivariate regression.
17:00- 18:15	Module 3: Time Series modeling for stock price prediction.
18:30-20:00	Module 4: Credit Risk, Discriminant and classification, logistic regression; k nearest neighbor classification, ALtman's Z score
	June 29, 2022 Workshop Day 2
14:00-15:15	Module 5: Supervised learning: Discriminant and classification, logistic regression, k nearest neighbor classification , support vector machine.
15:30-16:45	Module 6: Unsupervised learning: Cluster analysis (customer credit data), principal component analysis (eigen portfolio construction),
17:00-18:15	Module 7: Tree based methods, Risks and Volatility: regression tree, classification tree, bagging, random forest, boosting. Risks and performances, volatility modeling: Portfolio risk: value at risk, expected shortfall. Volatility modeling: GARCH modeling.
18:30-20:00	Module 8: Reinforcement learning: Basics of reinforcement learning, action, reward, policy , Markov Decision Process, Q learning.
	June 30, 2022 Workshop Day 3
14:00-15:15	Module 9: Reinforcement learning: Basics of reinforcement learning, action, reward, policy , Markov Decision Process, Q learning.
15:30-16:45	Module 10: Derivative, hedging, Algorithmic trading:: Derivatives, hedging strategy, derivative trading, algorithmic trading.

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