Modeling News Interactions and Influence for Financial Market Prediction

Mengyu Wang; Shay B Cohen; Tiejun Ma

doi:10.18653/v1/2024.findings-emnlp.189

Modeling News Interactions and Influence for Financial Market Prediction

Mengyu Wang
, Shay B Cohen
, Tiejun Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ’s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics: EMNLP 2024
Editors	Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Place of Publication	Miami, Florida, USA
Publisher	Association for Computational Linguistics
Pages	3302-3314
Number of pages	13
ISBN (Print)	9798891761681
DOIs	https://doi.org/10.18653/v1/2024.findings-emnlp.189
Publication status	Published - 9 Nov 2024
Event	2024 Conference on Empirical Methods in Natural Language Processing - Hyatt Regency Miami Hotel, Miami, United States Duration: 12 Nov 2024 → 16 Nov 2024 https://2024.emnlp.org/

Publication series

Name	Empirical Methods in Natural Language Processing
Publisher	Association for Computational Linguistics

Conference

Conference	2024 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP2024
Country/Territory	United States
City	Miami
Period	12/11/24 → 16/11/24
Internet address	https://2024.emnlp.org/

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10.18653/v1/2024.findings-emnlp.189Licence: Creative Commons: Attribution (CC-BY)

CohenEtAlEMNLP24ModelingNewsInteractionsandInfluenceAccepted author manuscript, 474 KBLicence: Creative Commons: Attribution (CC-BY)

https://aclanthology.org/2024.findings-emnlp.189/

Cite this

Wang, M., Cohen, S. B., & Ma, T. (2024). Modeling News Interactions and Influence for Financial Market Prediction. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 3302-3314). (Empirical Methods in Natural Language Processing). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.189

Wang, Mengyu ; Cohen, Shay B ; Ma, Tiejun. / Modeling News Interactions and Influence for Financial Market Prediction. Findings of the Association for Computational Linguistics: EMNLP 2024. editor / Yaser Al-Onaizan ; Mohit Bansal ; Yun-Nung Chen. Miami, Florida, USA : Association for Computational Linguistics, 2024. pp. 3302-3314 (Empirical Methods in Natural Language Processing).

@inproceedings{dc896b22e4154a339da69c96a2fdadbb,

title = "Modeling News Interactions and Influence for Financial Market Prediction",

abstract = "The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S\&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ{\textquoteright}s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data",

author = "Mengyu Wang and Cohen, \{Shay B\} and Tiejun Ma",

year = "2024",

month = nov,

day = "9",

doi = "10.18653/v1/2024.findings-emnlp.189",

language = "English",

isbn = "9798891761681",

series = "Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics",

pages = "3302--3314",

editor = "Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen",

booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",

note = "2024 Conference on Empirical Methods in Natural Language Processing, EMNLP2024 ; Conference date: 12-11-2024 Through 16-11-2024",

url = "https://2024.emnlp.org/",

}

Wang, M, Cohen, SB & Ma, T 2024, Modeling News Interactions and Influence for Financial Market Prediction. in Y Al-Onaizan, M Bansal & Y-N Chen (eds), Findings of the Association for Computational Linguistics: EMNLP 2024. Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Miami, Florida, USA, pp. 3302-3314, 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, United States, 12/11/24. https://doi.org/10.18653/v1/2024.findings-emnlp.189

Modeling News Interactions and Influence for Financial Market Prediction. / Wang, Mengyu; Cohen, Shay B ; Ma, Tiejun.
Findings of the Association for Computational Linguistics: EMNLP 2024. ed. / Yaser Al-Onaizan; Mohit Bansal; Yun-Nung Chen. Miami, Florida, USA: Association for Computational Linguistics, 2024. p. 3302-3314 (Empirical Methods in Natural Language Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Modeling News Interactions and Influence for Financial Market Prediction

AU - Wang, Mengyu

AU - Cohen, Shay B

AU - Ma, Tiejun

PY - 2024/11/9

Y1 - 2024/11/9

N2 - The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ’s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data

AB - The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ’s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data

U2 - 10.18653/v1/2024.findings-emnlp.189

DO - 10.18653/v1/2024.findings-emnlp.189

M3 - Conference contribution

SN - 9798891761681

T3 - Empirical Methods in Natural Language Processing

SP - 3302

EP - 3314

BT - Findings of the Association for Computational Linguistics: EMNLP 2024

A2 - Al-Onaizan, Yaser

A2 - Bansal, Mohit

A2 - Chen, Yun-Nung

PB - Association for Computational Linguistics

CY - Miami, Florida, USA

T2 - 2024 Conference on Empirical Methods in Natural Language Processing

Y2 - 12 November 2024 through 16 November 2024

ER -

Wang M, Cohen SB , Ma T. Modeling News Interactions and Influence for Financial Market Prediction. In Al-Onaizan Y, Bansal M, Chen YN, editors, Findings of the Association for Computational Linguistics: EMNLP 2024. Miami, Florida, USA: Association for Computational Linguistics. 2024. p. 3302-3314. (Empirical Methods in Natural Language Processing). doi: 10.18653/v1/2024.findings-emnlp.189

Modeling News Interactions and Influence for Financial Market Prediction

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