Sunandan Chakraborty


2019

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Identifying Predictive Causal Factors from News Streams
Ananth Balashankar | Sunandan Chakraborty | Samuel Fraiberger | Lakshminarayanan Subramanian
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a new framework to uncover the relationship between news events and real world phenomena. We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams. This graph is constructed by measuring how the occurrence of a word in the news influences the occurrence of another (set of) word(s) in the future. We show that PCG can be used to extract latent features from news streams, outperforming other graph-based methods in prediction error of 10 stock price time series for 12 months. We then extended PCG to be applicable for longer time windows by allowing time-varying factors, leading to stock price prediction error rates between 1.5% and 5% for about 4 years. We then manually validated PCG, finding that 67% of the causation semantic frame arguments present in the news corpus were directly connected in the PCG, the remaining being connected through a semantically relevant intermediate node.

2018

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Unsupervised Word Influencer Networks from News Streams
Ananth Balashankar | Sunandan Chakraborty | Lakshminarayanan Subramanian
Proceedings of the First Workshop on Economics and Natural Language Processing

In this paper, we propose a new unsupervised learning framework to use news events for predicting trends in stock prices. We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams. Using the temporal occurrence of words, WIN measures how the appearance of one word in a news stream influences the emergence of another set of words in the future. The latent word-word influencer relationships in WIN are the building blocks for causal reasoning and predictive modeling. We demonstrate the efficacy of WIN by using it for unsupervised extraction of latent features for stock price prediction and obtain 2 orders lower prediction error compared to a similar causal graph based method. WIN discovered influencer links from seemingly unrelated words from topics like politics to finance. WIN also validated 67% of the causal evidence found manually in the text through a direct edge and the rest 33% through a path of length 2.

2016

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The Effects of the Content of FOMC Communications on US Treasury Rates
Christopher Rohlfs | Sunandan Chakraborty | Lakshminarayanan Subramanian
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing