Group Theory, Chaos and Financial Time Series

In this talk we show how group and chaos theoretic techniques can be used jointly to capture nonlinearity in financial time series in a much more efficient, robust and transparent manner than AI, SDEs and other canonical techniques. We begin by presenting diagnostics of ergodicity and disorder in time series, focusing on mutual information and entropies to capture fully non perturbative and non linear properties of the stochastic process. We remark on the emergence and breaking of symmetry groups in financial markets both empirically and from the SDE perspectives, and show how remarkably well these alone can capture time series dynamics without any input from underlying PDEs or SDEs. We end by establishing a link with neural networks.
Who is this presentation for?
Quants, data scientists, mathematicians
Prerequisite knowledge:
Prior experience with PDEs/SDEs/stochastic processes will be helpful
What you'll learn?

Revant Nayar is the Principal and CIO of FMI Tech, a pioneering quantum-inspired quantitative hedge fund in the Indian and US markets. He began his career researching quantum field theory and string theory at IAS and Princeton, later transitioning to finance. Nayar has achieved notable success, generating Sharpe ratios over 3 and winning several innovation awards in quantitative finance. He has been featured at at prestigious conferences such as Bloomberg, ELS, EMEX and QuantMinds, and leads a significant international econophysics research collaboration, FTERC. Today, Nayar is recognized as a trailblazing quant fund manager and a leader in applying quantum and physics-inspired principles to asset and risk management.