VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls

1 code implementation • EMNLP 2020 • Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, Rajiv Ratn Shah

Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting.

Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts

1 code implementation • 3 Dec 2022 • Arshiya Aggarwal, Jiao Sun, Nanyun Peng

These fixed prefix templates could themselves be specific in terms of styles or linguistic structures, which may lead to unreliable fairness conclusions that are not representative of the general trends from tone varying prompts.

Fairness Text Generation

An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls

1 code implementation • NAACL 2021 • Ramit Sawhney, Arshiya Aggarwal, Rajiv Ratn Shah

In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world{'}s biggest stock indexes, the S{\&}P 500 index.