short-paper

The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models

Authors:

Janderson B. Nascimento,

Marco CristoAuthors Info & Claims

WebMedia '15: Proceedings of the 21st Brazilian Symposium on Multimedia and the Web

Pages 121 - 124

https://doi.org/10.1145/2820426.2820467

Published: 27 October 2015 Publication History

Abstract

According to the efficient market hypothesis, financial prices are unpredictable. However, meaningful advances have been achieved on anticipating market movements using machine learning techniques. In this work, we propose a novel method to represent the input for a stock price forecaster. The forecaster is able to predict stock prices from time series and additional information from web pages. Such information is extracted as structured events and represented in a compressed concept space. By using such representation with scalable forecasters, we reduced prediction error by about 10%, when compared to the traditional auto regressive models.

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Cited By

Cheng WBea KLeow SChan JHong ZChen Y(2022)A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock ForecastingMathematics10.3390/math1014243710:14(2437)Online publication date: 13-Jul-2022
https://doi.org/10.3390/math10142437
Dessain J(2022)Machine learning models predicting returnsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116970199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116970
Jiang HYe CLai SZhou H(2021)Event Attention Network for Stock Trend Prediction2021 International Conference on Service Science (ICSS)10.1109/ICSS53362.2021.00019(70-75)Online publication date: May-2021
https://doi.org/10.1109/ICSS53362.2021.00019
Show More Cited By

Index Terms

The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

WebMedia '15: Proceedings of the 21st Brazilian Symposium on Multimedia and the Web

October 2015

266 pages

ISBN:9781450339599

DOI:10.1145/2820426

General Chairs:
Marco Antônio Pinheiro de Cristo
UFAM, Brazil
,
David Fernandes de Oliveira
UFAM, Brazil
,
Program Chairs:
Adriano C. Machado Pereira
UFMG, Brazil
,
Fabricio Benevenuto
UFMG, Brazil
,
Marcelo Moreno
UFJF, Brazil
,
Valter Roesler
UFRGS, Brazil

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

CYTED: Ciência Y Tecnologia Para El Desarrollo
SBC: Brazilian Computer Society
FAPEAM: Fundacao de Amparo a Pesquisa do Estado do Amazonas
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2015

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Webmedia '15

Sponsor:

CYTED
SBC
FAPEAM
CNPq
CGIBR
CAPES

Webmedia '15: 21st Brazilian Symposium on Multimedia and the Web

October 27 - 30, 2015

Manaus, Brazil

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WebMedia '15 Paper Acceptance Rate 21 of 61 submissions, 34%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Cheng WBea KLeow SChan JHong ZChen Y(2022)A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock ForecastingMathematics10.3390/math1014243710:14(2437)Online publication date: 13-Jul-2022
https://doi.org/10.3390/math10142437
Dessain J(2022)Machine learning models predicting returnsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116970199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116970
Jiang HYe CLai SZhou H(2021)Event Attention Network for Stock Trend Prediction2021 International Conference on Service Science (ICSS)10.1109/ICSS53362.2021.00019(70-75)Online publication date: May-2021
https://doi.org/10.1109/ICSS53362.2021.00019
Wang YLi QHuang ZLi JBoldi PWelles BKinder-Kurlanda KWilson CPeters IMeira W(2019)EANProceedings of the 10th ACM Conference on Web Science10.1145/3292522.3326014(311-320)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3292522.3326014

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