News, Sentiment, and Financial Markets: A Computational System to Evaluate the Influence of Text Sentiment on Financial Assets.
Citation:
Stephen Kelly, News, Sentiment, and Financial Markets: A Computational System to Evaluate the Influence of Text Sentiment on Financial Assets., Trinity College Dublin, 2016Download Item:
Abstract:
With the advent of the internet and digitisation of news and books, the volume of unstructured
text has increased dramatically in recent years. This deluge of information is set to grow and come
from new and unconventional sources. New and innovative techniques and tools for manipulating
this data and making sense of it will become essential. The work in this thesis consists of a system
that analyses the content of news, extracting a sentiment time series variable, and uses this
variable in a time series modelling component to determine any inter-relationships between
changes in the price of financial assets. Each component of the system attempts to remove human
subjectivity from the modelling process to allow the system to compute a sentiment variable and
investigate its statistical significance and explanatory power by employing several time series
models. The system includes a number of processes and components to achieve this goal such as
data harvesting, processing, text analysis, time series modelling, hypothesis testing, and
visualisation. The work described in this thesis includes contributions to the area of text and
content analysis, information retrieval, time series analysis, statistical and econometric modelling.
A number of methods studied in the literature have incorporated text data with financial analysis
and prediction models. A review of some of the main studies and systems that have combined
methods from each discipline is presented in Chapter 2. Chapter 3 describes the system developed
in this thesis and its capabilities. The system is evaluated for different data inputs in Chapter 4,
where the influence of different news types and sources is investigated for equity and commodity
markets. The final chapter concludes the thesis by summarising the contributions and outlining
future work.
This thesis represents a combination of work that contributes to the areas of content analysis and
financial and statistical modelling. The main contribution of this thesis is in the implementation
and evaluation of a system that incorporates methods from text analysis and time series
modelling. The novelty of the system lies in the ability to compute a time series from text that acts
as a proxy for sentiment that can be aggregated with financial time series data in a statistical
model to estimate the impact of news on the financial asset. An evaluation of this system is
presented where the explanatory power of the sentiment variable for financial returns is
investigated. It is shown that the news source and text type play an important role when
computing a proxy for sentiment that has statistically significant explanatory power for financial
returns. The time varying influence of sentiment on financial returns is noted with a relationship
made between economic business cycles and volatility. The system is evaluated using data from
two financial markets, the equity and commodities markets, and in both instances it is found that
a proxy for sentiment extracted from news has a statistically significant influence on financial
returns.
Author's Homepage:
http://people.tcd.ie/kellys51http://people.tcd.ie/kahmad
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PUBLISHED
Author: KELLY, STEPHEN
Advisor:
AHMAD, KHURSHIDQualification name:
DoctoralPublisher:
Trinity College DublinType of material:
ThesisCollections
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Full text availableSubject (TCD):
Data Analysis , Econometric and statistical analysis , Natural Language Processing , STATISTICAL ANALYSISMetadata
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