Arvind Menon

Lausanne, Switzerland

Welcome to my Page! I am a 2nd year Master’s student at École polytechnique fédérale de Lausanne, in Data Science.

Research Interests: Deep Reinforcement Learning, Portfolio optimization, Systematic stratgies, Convex and Non-Convex Optimization, Time Series forecasting.

In January 2024 I finished my 6 month internship at Garda Capital Partners as a Quantitative Analyst. I got to work on many different projects, to name a few:

Made a Deep Reinforcement Learning Model which performs portfolio construction for a bag of strategies.
Constructed a Risk Aversion Index and a rule-based trading strategy based on the index.
Developed trend following strategies based on Emergent Market IRP Signals.
Made monthly forecasts of the US Manufacturing PMI using the latest time series models.

Prior to embarking on my Master’s journey, I worked as a Software Developer at Adobe in the Digital Experience business unit. I also hold a Bachelor’s degree in Engineering Physics from Indian Institute of Technology Madras.

I am currently looking for internship oppurtinies to work as a Quantitative Analyst or Quantitative Researcher for my Master's Thesis for a period of 6 Months starting August or September 2024 as part of my Master's degree at EPFL. I aim to leveraging my academic background, practical experiences, and passion for data science to contribute meaningfully as a Master’s Thesis student.

Thank you for visiting my page, and I look forward to sharing more about my journey and insights with you.

Publications

arXiv

Online Estimation and Optimization of Utility-Based Shortfall Risk

Menon, Arvind S., A., Prashanth L., and Jagannathan, Krishna

2021

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Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, where samples from the underlying loss distribution are available one-at-a-time. We cast the UBSR estimation problem as a root finding problem, and propose stochastic approximation-based estimations schemes. We derive non-asymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent based algorithm for UBSR optimization, and derive non-asymptotic bounds on its convergence.
arXiv

Q-means using variational quantum feature embedding

Menon, Arvind S, and Puri, Nikaash

2021

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This paper proposes a hybrid quantum-classical algorithm that learns a suitable quantum feature map that separates unlabelled data that is originally non linearly separable in the classical space using a Variational quantum feature map and q-means as a subroutine for unsupervised learning. The objective of the Variational circuit is to maximally separate the clusters in the quantum feature Hilbert space. First part of the circuit embeds the classical data into quantum states. Second part performs unsupervised learning on the quantum states in the quantum feature Hilbert space using the q-means quantum circuit. The output of the quantum circuit are characteristic cluster quantum states that represent a superposition of all quantum states belonging to a particular cluster. The final part of the quantum circuit performs measurements on the characteristic cluster quantum states to output the inter-cluster overlap based on fidelity. The output of the complete quantum circuit is used to compute the value of the cost function that is based on the Hilbert-Schmidt distance between the density matrices of the characteristic cluster quantum states. The gradient of the expectation value is used to optimize the parameters of the variational circuit to learn a better quantum feature map.