Sentiment analysis of financial news: Mechanics and statistics

Argimiro Arratia; Gustavo Avalos; Alejandra Cabaña; Ariel Duarte-López; Martí Renedo-Mirambell

doi:10.1007/978-3-030-66891-4_9

Sentiment analysis of financial news: Mechanics and statistics

Argimiro Arratia^*, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, Martí Renedo-Mirambell

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in Book › Chapter › Research › peer-review

3 Citations (Scopus)

Abstract

This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks' prices, and financial markets in general.

Original language	English
Title of host publication	Data Science for Economics and Finance
Subtitle of host publication	Methodologies and Applications
Publisher	Springer International Publishing AG
Pages	195-216
Number of pages	22
ISBN (Electronic)	9783030668914
ISBN (Print)	9783030668907
DOIs	https://doi.org/10.1007/978-3-030-66891-4_9
Publication status	Published - 9 Jun 2021

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10.1007/978-3-030-66891-4_9

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title = "Sentiment analysis of financial news: Mechanics and statistics",

abstract = "This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks' prices, and financial markets in general.",

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AU - Avalos, Gustavo

AU - Cabaña, Alejandra

AU - Duarte-López, Ariel

AU - Renedo-Mirambell, Martí

PY - 2021/6/9

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AB - This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks' prices, and financial markets in general.

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ER -

Sentiment analysis of financial news: Mechanics and statistics

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