Summer Freestyle Workshop

August 24 to September 6 · All times are Moscow time

1 Workshop overview

The Summer Freestyle Workshop is organized around five tracks: Macroeconomics, Finance, Machine Learning, Software Engineering, and Data Management. The workshop combines applied research methods, frontier topics, and hands-on implementation exercises. Meetings are organized as 1.5-hour master classes. Each class is accompanied by a problem set designed to code, estimate a model, prepare a quick report, or complete a related applied exercise.

The workshop follows a survival-based structure. Submission of completed problem sets is required for invitation to the next master class. The final assignment will be a research or project proposal. The most successful and motivated candidates will be offered the opportunity to join ongoing research or software engineering projects. Good luck.

2 Tracks

Macroeconomics (Macro)
VAR, SVAR, BVAR, Markov-switching and time-varying parameter VARs, DSGE modelling, Dynare implementation, and research frontiers in macroeconomics.
Finance (Fin)
Market-based expectations, term structure models, risk premia, commodities futures, non-rational beliefs, asset pricing, bank-runs modelling, and LLMs for banking and finance research.
Machine Learning (ML)
Introduction to machine learning, reinforcement learning, forecasting applications, validation, high-frequency trading research, voice recognition, multidimensional signals, and LLM applications.
Software Engineering (SE)
Web and mobile application development from scratch, including back-end and front-end solutions, social networks, financial and economic analysis platforms, and platforms for experimental studies.
Data Management (DM)
APIs, data access, hidden APIs, data scraping, BeautifulSoup, Selenium, Playwright, automated data collection, and research-grade data pipelines.

3 Schedule

TimeClassInstructor & topic
August 24, Monday
19:00Macro-1
Nadezhda Yurchenko
VAR, SVAR, BVAR
Basics of vector autoregressions, identification of shocks, basic packages in R/Matlab, impulse responses, and counterfactuals. Mastering the BEAR package in Matlab. Short-term neutral interest rate exercise.
20:30Fin-1
Anna Ivanova
Extracting market-based expectations
Term structure models for yield curves. Risk-premia problem. Assessing systemic errors in market-based expectations. Testing out-of-sample forecast corrections.
August 25, Tuesday
19:00SE-1
Pavel Shakhmin and Elizaveta Levshina
Web application development (social network, KRUG)
All stages of web app development from scratch. Required hard skills and packages. Overall description of the project and performance demonstration. Coding details. Basic functions, solutions for back-end and front-end, and practical tips.
20:30ML-1
Michael Ivanov
Gentle introduction to ML
High-level ideas behind ML and different fields in ML. Brief evolution of ML models. Overview of the main model families. Model selection, training and validation. Econometric models represented in ML terminology.
August 26, Wednesday
19:00Macro-2
Anna Pustovalova
MS-VAR, TVP-VAR
Mastering basic packages in R/Matlab for Markov-switching and time-varying parameter vector autoregressions. Issues and potential solutions.
20:30DM-1
George Borisenko
API
Broad understanding of API and examples. Data access. Hidden API. OpenAI API.
August 29, Saturday
12:30Fin-2
Ivan Salkov
Extracting risk premia in commodities futures
Futures risk-premia problem. Hamilton-Wu approach to risk-premia extraction. Oil futures application. Assessing systemic errors in market-based expectations for commodity prices.
14:30DM-2
Ilya Zaitsev
Data scraping with BeautifulSoup
HTML web page structure. Mastering the BeautifulSoup library, with practical tips.
August 30, Sunday
10:00SE-2
Anastasia Golubkova
Mobile application development (social network, KRUG)
All stages of mobile app development from scratch. Required hard skills and packages. Overall description of the project and performance demonstration. Coding details. Basic functions, solutions for back-end and front-end, and practical tips.
12:30ML-2
Andrei Afonin
Reinforcement learning
The main ingredients of modern learning systems: priors, data, feedback, exploration, and reinforcement. How reinforcement turns feedback into specialized behavior, and what trade-offs this creates. Design principles behind modern learning systems.
August 31, Monday
19:00Macro-3
Christina Zhilyaeva
DSGE
New Keynesian DSGE model: derivations. Full information estimation methods: Bayesian approach. Metropolis-Hastings algorithm. Matlab+Dynare implementation.
20:30Fin-3
Kalimzhan Beiseuov
Time-varying overreaction in commodities futures market
Time-varying overreaction across commodities. Introduction to trading strategies performance assessment.
September 1, Tuesday
19:00SE-3
Maria Bondarenko
Web application development (financial and economic analysis platform, SVOD)
All stages of web app development from scratch. Required hard skills and packages. Overall description of the project and performance demonstration. Coding details. Basic functions, solutions for back-end and front-end, and practical tips.
20:30ML-3
Sergei Khatuntsev
ML application for commodities prices forecasting
Brief overview of data structure and model used. Infrastructure development and computing power usage. Out-of-sample testing and validation. Forecasting performance metrics. Avenues for future work. Kaggle competition announcement.
September 2, Wednesday
20:30DM-3
Anna Ivanova
Data scraping with Selenium and Playwright
Issues with basic data scraping methods. Dynamic web pages. Mastering Selenium and Playwright libraries, with practical tips.
September 5, Saturday
11:00Macro-4
Timur Magzhanov
Research frontiers in Macroeconomics
Behavioral biases in macroeconomics. Solving the impossible: when DSGE models hide truth behind complexity. How Taylor expansions matter for optimal central bank policy.
12:30Fin-4
Timur Magzhanov
Research frontiers in Finance
Behavioral biases in finance. Asset pricing under non-rational beliefs. Machine learning and LLMs for asset pricing. Bank-runs modelling and LLMs for banking research.
14:30DM-4
Timur Magzhanov
Research frontiers for data scraping applications
Examples of applied research projects involving automated data collection.
September 6, Sunday
11:00SE-4
Anna Ivanova
Web application development (platform for experimental studies, forecasting.to)
All stages of web app development from scratch. Required hard skills and packages. Overall description of the project and performance demonstration. Coding details. Basic functions, solutions for back-end and front-end, and practical tips. Experimental results coverage.
12:30ML-4
Timur Magzhanov
Research frontiers for ML applications
ML applications for high-frequency trading research. Voice recognition and multidimensional trading signals processing. Reinforcement learning in economics and finance. LLMs in economics and finance.

4 Rules

The workshop is survival-based. Submission of the completed problem set for a class is required for invitation to the next master class. No exceptions, no extensions, no negotiations. The final assignment is a research or project proposal. Good luck.