Search Results for author: Arnav Wadhwa

Found 4 papers, 1 papers with code

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

1 code implementation EMNLP 2020 Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.

 Ranked #1 on Stock Market Prediction on stocknet (using extra training data)

Decision Making Stock Market Prediction

Quantitative Day Trading from Natural Language using Reinforcement Learning

no code implementations NAACL 2021 Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah

It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.

reinforcement-learning Stock Prediction

FAST: Financial News and Tweet Based Time Aware Network for Stock Trading

no code implementations EACL 2021 Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah

Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.

Learning-To-Rank

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

no code implementations COLING 2020 Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah

Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.

Language Modelling Sentiment Analysis

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