Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F19%3A43913765" target="_blank" >RIV/62156489:43110/19:43913765 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.4018/978-1-5225-5586-5.ch006" target="_blank" >https://doi.org/10.4018/978-1-5225-5586-5.ch006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-5225-5586-5.ch006" target="_blank" >10.4018/978-1-5225-5586-5.ch006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects

Popis výsledku v původním jazyce

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

Název v anglickém jazyce

Using Online Data in Predicting Stock Price Movements: Methodological and Practical Aspects

Popis výsledku anglicky

A lot of research has been focusing on incorporating online data into models of various phenomena. The chapter focuses on one specific problem coming from the domain of capital markets where the information contained in online environments is quite topical. The presented experiments were designed to reveal the association between online texts (from Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies. As the method for quantifying the association, machine learning-based classification was chosen. The experiments showed that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, the lags between the release of documents and related stock price changes, levels of a minimal stock price change, different weighting schemes for structured document representation, and classifiers were studied. The chapter also shows how to use currently available open source technologies to implement a system for accomplishing the task.

Druh

C - Kapitola v odborné knize

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

2019

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Techno-Social Systems for Modern Economical and Governmental Infrastructures

ISBN

978-1-5225-5586-5

Počet stran výsledku

35

Strana od-do

125-159

Počet stran knihy

351

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

—