Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Sentiment building from textual data content in quarterly reports

Jelassi, Tarek LU (2021) NEKN02 20211
Department of Economics
Abstract
Textual analysis is increasingly becoming a reliable tool for pattern assessing and forecasting especially statistically. Implementing such techniques in the financial field is still in an infantry stage and it represents a novel research area. While previous literature tends to rely on news articles as the source of data when examining the link with financial returns, internal information such as annual or quarterly reports drew small to no interest in academic research. This study aims to investigate the relationship between textual data content in quarterly reports and the corresponding stock price movement, to end up with a high-accuracy model when it comes to predicting the return. In this paper, we apply two different methods for... (More)
Textual analysis is increasingly becoming a reliable tool for pattern assessing and forecasting especially statistically. Implementing such techniques in the financial field is still in an infantry stage and it represents a novel research area. While previous literature tends to rely on news articles as the source of data when examining the link with financial returns, internal information such as annual or quarterly reports drew small to no interest in academic research. This study aims to investigate the relationship between textual data content in quarterly reports and the corresponding stock price movement, to end up with a high-accuracy model when it comes to predicting the return. In this paper, we apply two different methods for sentiment building, first relying on python programming language and its lexicon-based approaches, second collaborating with Parlametric to establish document-specific sentiments. While the first method, combined with modeling done through MATLAB, lacked significance even with decent accuracy levels in sign prediction, the dictionary-free framework offered higher degrees of certitude. Our results confirm the hypothesis of an existing link between the asset returns and quarterly reports which is very encouraging especially for future studies. (Less)
Please use this url to cite or link to this publication:
author
Jelassi, Tarek LU
supervisor
organization
course
NEKN02 20211
year
type
H1 - Master's Degree (One Year)
subject
keywords
Textual analysis, Python Programming Language, Modeling, Return Forecast, Sentiment building.
language
English
id
9048969
date added to LUP
2021-10-26 08:16:40
date last changed
2021-10-26 08:16:40
@misc{9048969,
  abstract     = {{Textual analysis is increasingly becoming a reliable tool for pattern assessing and forecasting especially statistically. Implementing such techniques in the financial field is still in an infantry stage and it represents a novel research area. While previous literature tends to rely on news articles as the source of data when examining the link with financial returns, internal information such as annual or quarterly reports drew small to no interest in academic research. This study aims to investigate the relationship between textual data content in quarterly reports and the corresponding stock price movement, to end up with a high-accuracy model when it comes to predicting the return. In this paper, we apply two different methods for sentiment building, first relying on python programming language and its lexicon-based approaches, second collaborating with Parlametric to establish document-specific sentiments. While the first method, combined with modeling done through MATLAB, lacked significance even with decent accuracy levels in sign prediction, the dictionary-free framework offered higher degrees of certitude. Our results confirm the hypothesis of an existing link between the asset returns and quarterly reports which is very encouraging especially for future studies.}},
  author       = {{Jelassi, Tarek}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Sentiment building from textual data content in quarterly reports}},
  year         = {{2021}},
}