9 citations · 32 across the 8 of their papers we have counts for
10 papers
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
Maxime Kawawa-Beaudan, Srijan Sood, Kassiani Papasotiriou +2
Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-…
Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
Srijan Sood, Kassiani Papasotiriou, Marius Vaiciulis +1
Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio opti…
Behavioral Sequence Modeling with Ensemble Learning
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavi…
AI in Investment Analysis: LLMs for Equity Stock Ratings
Kassiani Papasotiriou, Srijan Sood, Shayleen Reynolds +1
Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), o…
Ensemble Methods for Sequence Classification with Hidden Markov Models
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
Visual Time Series Forecasting: An Image-driven Approach
Naftali Cohen, Srijan Sood, Zhen Zeng +2
In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach resul…
Visual Time Series Forecasting: An Image-driven Approach
Srijan Sood, Zhen Zeng, Naftali Cohen +2
Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users…
Debiasing classifiers: is reality at variance with expectation?
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl +5
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse r…
Cross-Domain Perceptual Reward Functions
Ashley D. Edwards, Srijan Sood, Charles L. Isbell
In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards eac…