Implementing Pricing Model and Dynamic Asset Allocation [Webinar]

2 min read


About the Presentations

This session has project presentations by two of our esteemed EPAT alumni. First on “Implementing pricing (or market-making) model using Kalman filtering adaptive to market regimes” by Evgeny Tishkin and second on “Dynamic Asset Allocation using Neural Networks” by Mrinal Mahajan.


Project 1: Implementing pricing (or market-making) model using Kalman filtering adaptive to market regimes

This project is aimed to share an example about how to use quantitative techniques of online machine learning in trading strategies development and improvement.

Online learning allows for deeper automation of algorithmic trading in comparison with traditional regular model retraining with subsequent cross-validation. This is because such models can adapt their parameters on the fly while being used in real-time trading simultaneously.

The real production strategies are far more complex than the ones which are used in the project as examples and include much deeper automation of a wider range of parameters. Nevertheless, incorporated online models have universal application and could be applied to different kinds of trading strategies from passive investing to high-frequency trading.

Complete Project: Investing in Big Tech Stocks using online Quantitative Models

Presentation Slides


Project 2: Dynamic Asset Allocation using Neural Networks

The project aims at building an asset allocation strategy for stocks comprising the nifty bank index by leveraging daily returns prediction from a set of dynamic linear neural network models.

The neural network models are dynamic and updated daily at the end of each trading day providing for reactivity to changes in market and stock trends in general. Besides that, the linearity of models accounts for an easy explanation of the results.

Complete Project: Dynamic Asset Allocation using Neural Networks

Presentation Slides


About the Presenters

Evgeny Tishkin (Senior Quantitative Analyst from Russia)

Evgeny Tishkin pic

Evgeny Tishkin has over 10 years of various experience in algorithmic trading and HFT working at several proprietary trading firms and hedge funds as a Quantitative Developer, Chief Software Architect, Quantitative Analyst and Chief Technical Officer.

For one of the algorithmic hedge funds working as CTO, he created a complete low-latency multi-exchange algorithmic trading infrastructure and high-frequency trading framework from scratch.

Evgeny won 2nd place among more than 4000 quantitative researchers from 100 countries around the world in XTX Markets Global Forecasting Challenge in 2019.

Evgeny holds a Master’s degree in Computer Science from Samara State Aerospace University. Additionally, he acquired a speciality “Machine learning and data analysis” from Moscow Physical-Technical Institute.

Mrinal Mahajan (Risk Consultant from India)

Mrinal Mahajan pic

Mrinal Mahajan is a consultant with a diverse set of experience in Portfolio management, fraud modelling and custom analytics projects. He has worked with some of the major consultancies and financial institutions for last 4 years.

Mrinal graduated from IIT Kanpur and have a certified Financial Risk Manager (FRM) certification from the Global Association of Risk Professionals (GARP).


This event was conducted on:
Tuesday, November 16, 2021
8:30 AM ET | 7:00 PM IST | 9:30 PM SGT