Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction

Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction

News has been an important source for many financial time series predictions based on fundamental analysis. However, digesting a massive amount of news and data published on the Internet to predict a market can be burdensome. This paper introduces a topic model based on latent Dirichlet allocation (...

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Bibliographic Details
Main Authors: Nont Kanungsukkasem, Teerapong Leelanupab
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Bayesian method
data mining
data preparation
data processing
feature extraction
financial time series
Online Access:https://ieeexplore.ieee.org/document/8726415/

Internet

https://ieeexplore.ieee.org/document/8726415/

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