Beyond Sentiment in Stock Price Prediction: Integrating News Sentiment and Investor Attention with Temporal Fusion Transformer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922253" target="_blank" >RIV/00216275:25410/24:39922253 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-63219-8_3" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-63219-8_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-63219-8_3" target="_blank" >10.1007/978-3-031-63219-8_3</a>

Result language

angličtina

Original language name

Beyond Sentiment in Stock Price Prediction: Integrating News Sentiment and Investor Attention with Temporal Fusion Transformer

Original language description

News sentiment is attracting considerable interest in stock market prediction, given its crucial role in shaping stock prices. Previous research has mainly focused on improving prediction accuracy by exploiting news sentiment, without adequately considering the different levels of attention that individual news articles receive. Furthermore, despite the advanced predictive capabilities of deep learning models, there has been a lack of focus on the interpretability of these models, leading to predictions that are not transparent. This study presents an innovative prediction model that integrates a FinBERT-based analysis of news sentiment and investor attention metrics with an attention-based Temporal Fusion Transformer framework. This approach not only enables highly effective forecasting, but also provides insights into the temporal dynamics that influence the stockmarket. The effectiveness of the model is demonstrated by analyzing stock price data for 41 of the largest market capitalization companies over the period 2010 to 2021. The results confirm the superiority of the proposed model over existing deep learning approaches, and the attention analysis underscores the critical role of synthesizing news sentiment and attention metrics in predicting stock prices.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-22586S" target="_blank" >GA22-22586S: Aspect-based sentiment analysis of financial texts for predicting corporate financial performance</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, PT III, AIAI 2024

ISBN

978-3-031-63218-1

ISSN

1868-4238

e-ISSN

1868-422X

Number of pages

14

Pages from-to

30-43

Publisher name

SPRINGER INTERNATIONAL PUBLISHING AG

Place of publication

CHAM

Event location

Corfu

Event date

Jun 27, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001283392400003