Measuring Investor Sentiment in Financial Discourse: How Different Approaches Shape Market Signals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F26%3A0200014" target="_blank" >RIV/00216305:26510/26:0200014 - isvavai.cz</a>

Výsledek na webu

<a href="https://ecocyb.ase.ro/nr2025_4/5_ZuzanaJankova_NikolozKavelashvili.pdf" target="_blank" >https://ecocyb.ase.ro/nr2025_4/5_ZuzanaJankova_NikolozKavelashvili.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24818/18423264/59.4.25.05" target="_blank" >10.24818/18423264/59.4.25.05</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measuring Investor Sentiment in Financial Discourse: How Different Approaches Shape Market Signals

Popis výsledku v původním jazyce

Stock prices are shaped not only by fundamental data but also by investor sentiment, which often deviates from rational decision-making. Given the vast volume of financial texts published by both professional and amateur investors—especially on online financial platforms—sentiment analysis in such unstructured data is essential to understanding their impact on market movements. This study examines the interplay between text data and stock market movements, highlighting the critical role of sentiment extracted from financial news and online news. Existing research has largely relied on general-purpose lexicons or uniform classification techniques, which limits the accuracy of sentiment analysis in financial contexts. To address this gap, we propose a hybrid framework that integrates domain-specific lexicons with advanced machine learning classifiers to improve sentiment extraction from unstructured financial text. Our approach evaluates the impact of lexicon selection on sentiment scores and examines the relationship between classifier choice and prediction accuracy. By improving sentiment analysis methodologies, our findings contribute to the development of more robust stock market forecasting models, strengthen decision-making processes for investors, and increase market efficiency.

Název v anglickém jazyce

Measuring Investor Sentiment in Financial Discourse: How Different Approaches Shape Market Signals

Popis výsledku anglicky

Stock prices are shaped not only by fundamental data but also by investor sentiment, which often deviates from rational decision-making. Given the vast volume of financial texts published by both professional and amateur investors—especially on online financial platforms—sentiment analysis in such unstructured data is essential to understanding their impact on market movements. This study examines the interplay between text data and stock market movements, highlighting the critical role of sentiment extracted from financial news and online news. Existing research has largely relied on general-purpose lexicons or uniform classification techniques, which limits the accuracy of sentiment analysis in financial contexts. To address this gap, we propose a hybrid framework that integrates domain-specific lexicons with advanced machine learning classifiers to improve sentiment extraction from unstructured financial text. Our approach evaluates the impact of lexicon selection on sentiment scores and examines the relationship between classifier choice and prediction accuracy. By improving sentiment analysis methodologies, our findings contribute to the development of more robust stock market forecasting models, strengthen decision-making processes for investors, and increase market efficiency.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Economic Computation and Economic Cybernetics Studies and Research

ISSN

0424-267X

e-ISSN

1842-3264

Svazek periodika

59

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

19

Strana od-do

79-97

Kód UT WoS článku

001651970500005

EID výsledku v databázi Scopus

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