Papers by Arnav Wadhwa
Quantitative Day Trading from Natural Language using Reinforcement Learning (2021.naacl-main)
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| Challenge: | Existing approaches to stock prediction are not optimized to make profitable investment decisions. |
| Approach: | They propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. |
| Outcome: | The proposed method outperforms state-of-the-art in terms of risk-adjusted returns on two benchmarks: Tweets (English) and financial news (Chinese) |
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading (2021.eacl-main)
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| Challenge: | Existing methods for stock movement prediction are limited and do not account for the fine-grain temporal irregularities in the release of large volumes of text. |
| Approach: | They propose a hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. |
| Outcome: | The proposed method outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. |
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
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| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
| Outcome: | The proposed model can be used to forecast stock movements on real-world S&P 500 index data and English tweets. |
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020.coling-main)
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| Challenge: | Parliamentary debates are a valuable language resource for analyzing comprehensive options in a functional, free society. |
| Approach: | They propose a neural model for political speech sentiment analysis exploiting semantic representations and relations between debate transcripts, motions, and political party members. |
| Outcome: | The proposed model exploits semantic representations and relations between debate transcripts, motions, and political party members to predict political polarity and polarities. |