Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43913611" target="_blank" >RIV/62156489:43110/18:43913611 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.4114/intartif.vol21iss61pp95-110" target="_blank" >https://doi.org/10.4114/intartif.vol21iss61pp95-110</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4114/intartif.vol21iss61pp95-110" target="_blank" >10.4114/intartif.vol21iss61pp95-110</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements

Popis výsledku v původním jazyce

The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected.

Název v anglickém jazyce

Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements

Popis výsledku anglicky

The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA16-26353S" target="_blank" >GA16-26353S: Sentiment a jeho vliv na akciové trhy</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Inteligencia Artificial

ISSN

1137-3601

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

61

Stát vydavatele periodika

ES - Španělské království

Počet stran výsledku

16

Strana od-do

95-110

Kód UT WoS článku

000609005300007

EID výsledku v databázi Scopus

2-s2.0-85046842152