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

Sawhney, Ravinder Singh; Agarwal, Shivam; Wadhwa, Arnav; Shah, Rajiv Ratn

doi:10.18653/v1/2020.emnlp-main.676

Cited by 65 publications

(63 citation statements)

References 60 publications

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“…Bias in Finance Public financial data is impacting virtually every aspect of investment decision making (Perić et al, 2016;Brynjolfsson et al, 2011). Prior research shows that NLP methods leveraging social media (Sawhney et al, 2020a), news (Du and Tanaka-Ishii, 2020), and earning calls (Wang and Hua, 2014) can accurately forecast financial risk. Companies and investors use statistical and neural models on multimodal financial data to forecast volatility (Cornett and Saunders, 2003;Trippi and Turban, 1992) and minimize risk.…”

Section: Background: Why Study Bias?mentioning

confidence: 99%

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

Sawhney¹,

Aggarwal²,

Shah³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Volatility prediction is complex due to the stock market's stochastic nature. Existing research focuses on the textual elements of financial disclosures like earnings calls transcripts to forecast stock volatility and risk, but ignores the rich acoustic features in the company executives' speech. Recently, new multimodal approaches that leverage the verbal and vocal cues of speakers in financial disclosures significantly outperform previous stateof-the-art approaches demonstrating the benefits of multimodality and speech. However, the financial realm is still plagued with a severe underrepresentation of various communities spanning diverse demographics, gender, and native speech. While multimodal models are better risk forecasters, it is imperative to also investigate the potential bias that these models may learn from the speech signals of company executives. In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gendersensitive audio features and fewer female executives in earnings calls of one of the world's biggest stock indexes, the S&P 500 index. We quantitatively analyze bias as error disparity and investigate the sources of this bias. Our results suggest that multimodal neural financial models accentuate gender-based stereotypes. 1

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Section: Background: Why Study Bias?mentioning

confidence: 99%

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

Sawhney¹,

Aggarwal²,

Shah³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…For this study, we derive inspiration from Hu et al (2017); Sawhney et al (2020Sawhney et al ( , 2021 to design the policy network. However, it is important to note that PROFIT is compatible with any general deep network that is capable of handling time-series of textual data.…”

Section: Trading Policy Networkmentioning

confidence: 99%

“…• MAN-SF (text only): BERT based hierarchical encoder for financial text using hierarchical temporal attention (Sawhney et al, 2020).…”

Section: Classification (Clf)mentioning

confidence: 99%

Quantitative Day Trading from Natural Language using Reinforcement Learning

Sawhney¹,

Wadhwa²,

Agarwal³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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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. Existing NLP approaches largely treat stock prediction as a classification or regression problem and are not optimized to make profitable investment decisions. Further, they do not model the temporal dynamics of large volumes of diversely influential text to which the market responds quickly. Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. Our method outperforms state-ofthe-art in terms of risk-adjusted returns in trading simulations on two benchmarks: Tweets (English) and financial news (Chinese) pertaining to two major indexes and four global stock markets. Through extensive experiments and studies, we build the case for our method as a tool for quantitative trading.

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“…Advances in NLP have been utilized in many approaches to show financial information significantly improving performance in forecasting tasks like volatility and stock price prediction (Wang et al, 2013;Ding et al, 2015;Mittermayer and Knolmayer, 2007). Research has also shown that social media affects the stock market (Bollen et al, 2010;Oliveira et al, 2017;Sawhney et al, 2020a). Machine learning methods using simple bag-of-words features to represent the financial documents used in previous research (Kogan et al, 2009;Rekabsaz et al, 2017) largely ignore the inter-dependencies between the sentences.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines

Sawhney¹,

Goyal²,

Goel³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities. To aid the analysis of M&A calls, we curate a dataset of conference call transcripts and their corresponding audio recordings for the time period ranging from 2016 to 2020. We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multispeaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the proposed architecture performing marginally better than strong BERT-based baselines. We release the M3A dataset and benchmark models to motivate future research on this challenging problem domain.

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

Cited by 65 publications

References 60 publications

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

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

Quantitative Day Trading from Natural Language using Reinforcement Learning

Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines

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