Multimodal financial sentiment for stock return prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923419" target="_blank" >RIV/00216275:25410/25:39923419 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1877050925028479" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050925028479</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.09.177" target="_blank" >10.1016/j.procs.2025.09.177</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal financial sentiment for stock return prediction

Popis výsledku v původním jazyce

This paper proposes a novel multimodal deep learning framework for stock return prediction that integrates heterogeneous data sources: technical indicators, market investor sentiment indices, and textual sentiment extracted from earnings conference call transcripts. The proposed model employs a hybrid architecture combining transformer encoder for the technical modality and neural networks for market and textual modalities. A modality-level attention mechanism is used in a late fusion setup to dynamically weight the contributions of each modality. We evaluate our model on a large-scale dataset comprising 24,821 samples from 497 S&amp;P 500 companies over the period 2010–2022. The results show that our model outperforms traditional models (LSTM, BiL-STM, CNN-LSTM) and alternative fusion strategies, achieving a directional accuracy of 59.94% on the test set. Attention weight analysis confirms that all three modalities contribute meaningfully to prediction performance. These results demonstrate the overall effectiveness of the proposed framework in accurately predicting abnormal stock returns in a multimodal setting.

Název v anglickém jazyce

Multimodal financial sentiment for stock return prediction

Popis výsledku anglicky

This paper proposes a novel multimodal deep learning framework for stock return prediction that integrates heterogeneous data sources: technical indicators, market investor sentiment indices, and textual sentiment extracted from earnings conference call transcripts. The proposed model employs a hybrid architecture combining transformer encoder for the technical modality and neural networks for market and textual modalities. A modality-level attention mechanism is used in a late fusion setup to dynamically weight the contributions of each modality. We evaluate our model on a large-scale dataset comprising 24,821 samples from 497 S&amp;P 500 companies over the period 2010–2022. The results show that our model outperforms traditional models (LSTM, BiL-STM, CNN-LSTM) and alternative fusion strategies, achieving a directional accuracy of 59.94% on the test set. Attention weight analysis confirms that all three modalities contribute meaningfully to prediction performance. These results demonstrate the overall effectiveness of the proposed framework in accurately predicting abnormal stock returns in a multimodal setting.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA25-15405S" target="_blank" >GA25-15405S: Multimodální analýza finančního sentimentu pro predikci finančních trhů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Computer Science, vol. 270

ISBN

—

ISSN

1877-0509

e-ISSN

1877-0509

Počet stran výsledku

10

Strana od-do

582-591

Název nakladatele

Elsevier B.V.

Místo vydání

Amsterdam

Místo konání akce

Osaka

Datum konání akce

10. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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