{
  "schemaVersion": 1,
  "canonicalPerson": "https://www.arnav.life/#arnav-wadhwa",
  "author": "Arnav Wadhwa",
  "publicationCount": 12,
  "publications": [
    {
      "id": "emnlp-2020",
      "title": "Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations",
      "venue": "EMNLP",
      "year": 2020,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Arnav Wadhwa",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Shivam Agarwal"
      ],
      "summary": "A graph-aware attentive approach combining market, social-media, and inter-company signals for stock-movement prediction.",
      "pdfUrl": "/papers/emnlp-2020.pdf",
      "canonicalUrl": "https://aclanthology.org/2020.emnlp-main.676/",
      "doi": null,
      "codeUrl": "https://github.com/midas-research/man-sf-emnlp",
      "acceptanceRate": 22.4,
      "acceptanceRateSourceUrl": "https://aclanthology.org/2020.emnlp-main.0.pdf",
      "results": [
        "Roughly 2.6% accuracy improvement.",
        "Sharpe ratio 1.05 in a three-month backtest."
      ]
    },
    {
      "id": "icdm-2020",
      "title": "Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting",
      "venue": "ICDM",
      "year": 2020,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Arnav Wadhwa",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [],
      "summary": "A spatiotemporal hypergraph network that captures higher-order dependencies across stocks via multi-scale temporal convolutions.",
      "pdfUrl": "/papers/icdm-2020.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/sthgcn-icdm",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Evaluated across five years, 1,174 trading days, and 12 market phases.",
        "Mean F1 0.346 versus 0.329.",
        "Sharpe ratio 2.065 versus 1.991.",
        "Training time per epoch reduced by 28×."
      ]
    },
    {
      "id": "coling-2020",
      "title": "GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion",
      "venue": "COLING",
      "year": 2020,
      "authors": [
        "Ramit Sawhney",
        "Arnav Wadhwa",
        "Shivam Agarwal",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [],
      "summary": "A contextual graph-based language model for analyzing parliamentary debates and political cohesion.",
      "pdfUrl": "/papers/coling-2020.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/gpols-coling",
      "acceptanceRate": 35.3,
      "acceptanceRateSourceUrl": "https://aclanthology.org/2020.coling-main.0v1.pdf",
      "results": [
        "Evaluated on 33,461 parliamentary transcripts.",
        "Accuracy 0.76, nine percentage points above text-only BERT and four points above the strongest prior graph method."
      ]
    },
    {
      "id": "aaai-2021",
      "title": "Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach",
      "venue": "AAAI",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Arnav Wadhwa",
        "Tyler Derr",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Shivam Agarwal"
      ],
      "summary": "A learning-to-rank approach using spatiotemporal hypergraph attention for stock selection.",
      "pdfUrl": "/papers/aaai-2021.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/sthan-sr-aaai",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Evaluated on 2,852 stocks over six years.",
        "Sharpe ratios 1.42, 1.12, and 1.19.",
        "Return ratios 0.44, 0.33, and 0.62."
      ]
    },
    {
      "id": "eacl-2021",
      "title": "FAST: Financial News and Tweet Based Time Aware Network for Stock Trading",
      "venue": "EACL",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Arnav Wadhwa",
        "Shivam Agarwal",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Arnav Wadhwa"
      ],
      "summary": "A time-aware network for stock trading from financial news and tweets.",
      "pdfUrl": "/papers/eacl-2021.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/fast-eacl",
      "acceptanceRate": 24.7,
      "acceptanceRateSourceUrl": "https://aclanthology.org/2021.eacl-main.0.pdf",
      "results": [
        "Evaluated on 109,000 tweets and 90,000 financial-news headlines.",
        "More than 8% higher returns and roughly 10% higher Sharpe ratio."
      ]
    },
    {
      "id": "naacl-2021",
      "title": "Quantitative Day Trading from Natural Language using Reinforcement Learning",
      "venue": "NAACL",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Arnav Wadhwa",
        "Shivam Agarwal",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Arnav Wadhwa"
      ],
      "summary": "A reinforcement-learning approach to quantitative day trading from natural language.",
      "pdfUrl": "/papers/naacl-2021.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/profit-naacl",
      "acceptanceRate": 28,
      "acceptanceRateSourceUrl": "https://aclanthology.org/2021.naacl-main.0.pdf",
      "results": [
        "Evaluated on 109,915 tweets and 90,361 financial-news headlines.",
        "More than 13% higher Sharpe ratio and more than 16% lower maximum drawdown."
      ]
    },
    {
      "id": "www-2021",
      "title": "Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading",
      "venue": "The Web Conference",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Arnav Wadhwa",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Shivam Agarwal"
      ],
      "summary": "Hyperbolic graph learning for algorithmic trading in scale-free stock markets.",
      "pdfUrl": "/papers/www-2021.pdf",
      "canonicalUrl": "https://doi.org/10.1145/3442381.3450095",
      "doi": "10.1145/3442381.3450095",
      "codeUrl": "https://github.com/midas-research/hyper-stockgat-www",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Evaluated on 2,943 stocks over six years.",
        "More than 12% higher profitability and more than 4% higher risk-adjusted returns."
      ]
    },
    {
      "id": "sigir-2021",
      "title": "Hyperbolic Online Time Stream Modeling",
      "venue": "SIGIR",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Megh Thakkar",
        "Arnav Wadhwa",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Megh Thakkar"
      ],
      "summary": "Hyperbolic online time-stream modeling for irregular information flows.",
      "pdfUrl": "/papers/sigir-2021.pdf",
      "canonicalUrl": "https://doi.org/10.1145/3404835.3463119",
      "doi": "10.1145/3404835.3463119",
      "codeUrl": "https://github.com/midas-research/hyperbolic-tlstm-sigir",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Evaluated on 109,915 tweets and 90,361 financial-news headlines.",
        "Improved profitability, movement prediction, and volatility forecasting as reported in the paper."
      ]
    },
    {
      "id": "uai-2021",
      "title": "Modeling Financial Uncertainty with Multivariate Temporal Entropy-based Curriculums",
      "venue": "UAI",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Arnav Wadhwa",
        "Ayush Mangal",
        "Vivek Mittal",
        "Shivam Agarwal",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [],
      "summary": "A curriculum-learning approach to modeling financial uncertainty with multivariate temporal entropy.",
      "pdfUrl": "/papers/uai-2021.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/finclass-uai",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": []
    },
    {
      "id": "ijcai-2021",
      "title": "TEC: A Time Evolving Contextual Graph Model for Speaker State Analysis in Political Debates",
      "venue": "IJCAI",
      "year": 2021,
      "authors": [
        "Ramit Sawhney",
        "Shivam Agarwal",
        "Arnav Wadhwa",
        "Rajiv Ratn Shah"
      ],
      "coFirstAuthors": [
        "Ramit Sawhney",
        "Shivam Agarwal"
      ],
      "summary": "A time-evolving contextual graph model for speaker-state analysis in political debates.",
      "pdfUrl": "/papers/ijcai-2021.pdf",
      "canonicalUrl": null,
      "doi": null,
      "codeUrl": "https://github.com/midas-research/tec-ijcai",
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Evaluated on more than 20 years of debates and 33,461 transcripts.",
        "Accuracy 75.07%, MCC 0.47, and accuracy improvement 1.38%."
      ]
    },
    {
      "id": "jch-2021",
      "title": "An object detection approach for detecting damages in heritage sites using 3-D point clouds and 2-D visual data",
      "venue": "Journal of Cultural Heritage",
      "year": 2021,
      "authors": [
        "Rachna Pathak",
        "Anil Saini",
        "Arnav Wadhwa",
        "Himanshu Sharma",
        "Dhiraj Sangwan"
      ],
      "coFirstAuthors": [],
      "summary": "An object-detection approach for heritage-site damage detection using 3-D point clouds and 2-D visual data.",
      "pdfUrl": "/papers/jch-2021.pdf",
      "canonicalUrl": "https://doi.org/10.1016/j.culher.2021.01.002",
      "doi": "10.1016/j.culher.2021.01.002",
      "codeUrl": null,
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": [
        "Trained on 10,000 labeled patches derived from 7,500 images.",
        "Achieved 58.19% mAP@0.5 IoU."
      ]
    },
    {
      "id": "iete-2023",
      "title": "Comparative Assessment of Regression Techniques for Wind Power Forecasting",
      "venue": "IETE Journal of Research",
      "year": 2023,
      "authors": [
        "Rachna Pathak",
        "Arnav Wadhwa",
        "Poras Khetarpal",
        "Neeraj Kumar"
      ],
      "coFirstAuthors": [],
      "summary": "A comparative assessment of regression techniques for wind-power forecasting.",
      "pdfUrl": "/papers/iete-2023.pdf",
      "canonicalUrl": "https://doi.org/10.1080/03772063.2020.1869591",
      "doi": "10.1080/03772063.2020.1869591",
      "codeUrl": null,
      "acceptanceRate": null,
      "acceptanceRateSourceUrl": null,
      "results": []
    }
  ]
}