Trading on Sentiment in Financial Text:
An Ontology
Rashmi Jha
PhD Student
College of Business Administration
University of Illinois at Chicago
601 S Morgan Street (MC 294)
Chicago, IL 60605 USA
E-mail: [email protected]
Arkalgud Ramaprasad, PhD
Professor Emeritus, University of Illinois at
Chicago
Visiting Professor, University of Miami
421 Jenkins Building
Coral Gables, FL 33124, USA
E-mail: [email protected], [email protected]
Keywords: Sentiment, Financial Text, Trading, Ontology
1. Introduction
The study of sentiments in financial text has received considerable attention from information
systems and finance researchers and practitioners over the past decade (for e.g., Antweiler and
Frank, 2004; Loughran and McDonald, 2010; Tetlock’s 2007). They have examined the
statistical relationships between financial market indicators and sentiments (coarsely
categorized as positive, neutral, and negative) extracted from the text.
However, the decision to purchase stock takes place in a highly competitive and ambiguous
environment. It involves various agents (traders), different sources of textual information and
conflicting views, opinions, and sentiments about the particular stock, market, or the economy
as a whole. The generally used statistical textual analysis does not capture the semantic
complexity of sentiments in the different types of texts fully. We propose a refined taxonomy
of sentiment based on the extant literature on the topic. We then combine it with simple
taxonomies of financial text and market behavior to propose ontology for articulating the logic
of trading on sentiment in financial text. We will discuss how the ontology can be used to study
the phenomenon systemically and systematically, and the potential insights from such study.
The following describes the ontology of the Trading on Sentiment in Financial Text.
2. Ontology of Trading on Sentiment in Financial Text
There is no standard definition of ontology of a complex, ill-structured problem. We will define
ontology, as a logically constructed n-dimensional natural language model of the problem
statement (Ramaprasad & Papagiri, 2009; Ramaprasad et.al, 201). The dimensions are derived
from the problem statement. Each dimension is independent of the other and is taxonomy of
discrete categories. Each taxonomy may be flat or hierarchical. Further, the order of categories
in a particular dimension at a particular level of the taxonomy may be nominal (no particular
order) or ordinal (based on some parameter).
In the following we will first present the derivation of ontology for analyzing the problem
statement “Trading on Sentiment in Financial Text”. We note that we present an ontology
framework not the ontology. The dimensions of the ontology are derived logically from an
interdisciplinary synthesis of the literature. This allows us to define the dimensions as well as
the problem domain precisely and accurately.
The ontology shown in Figure 1 has three dimensions, namely: (a) Sentiment, (b) Financial
Text, and (c) Market Behavior. Each dimension is defined by a one- or two-level taxonomy.
The taxonomies of the dimensions are logically constructed based on psychology and finance
Financial Text
Structured
Annual reports
Quarterly reports
10K reports
…
Semi-structured
[financial text on]
Sentiment
Positive
Love
Joy/Happiness
Fearlessness
Neutral
Surprise
Negative
Anger
Sadness
Fearfulness
[sentiment in]
[Influence of]
(academic and practitioner) literatures.
Market Behavior
Stock price
Stock volume
Stock volatility
News outlets
Analyst recommendations
…
Unstructured
Discussion forum
Blogs
…
Figure 1. Ontology of Influence of Sentiment in Financial Text
In the ontology we use a refined taxonomy for sentiment. Sentiment is usually classified into
three broad categories “Positive”, “Neutral” and “Negative” in Finance literature(e.g. Das and
Chen,2007;Tetlock,2007). These categories are too generic to explain in-depth the semantic
mechanisms of influence of sentiment in financial text on trading behavior. For example,
previous researchers have found evidence of differential influence of positive vs. negative
sentiment but they have not examined the differential influence of the distinct sentiment of the
same valence e.g., negative as suggested by the model proposed in Lerner and Keltner (2000)
study. Our taxonomy of sentiment explains the dimension at a finer level of granularity. The
second level includes the subcategories from (Ekman, Friesen and Ellsworth’s, 1972) emotion
categories that are widely used in computer sciences research (e.g. Liu, 2010).
Similarly, we have categorizes Financial Text as Structured, Semi-structured, and Unstructured.
We expect the type and frequency of sentiments expressed in these types of text to be different
and hence have differing impact on Market Behavior.
3 Application of the Ontology
The ontology constitutes a closed description of the problem statement. It provides a detailed
yet comprehensive description of the problem domain. Three illustrative components of
research problem from 27 (3x3x3) level-1 components which can be derived from the ontology
are:
1. Influence of Positive Sentiment in Structured Financial Text on Stock price;
2. Influence of Neutral Sentiment in Unstructured Financial Text on Stock volume; and
3. Influence of Negative Sentiment in Semi-structured Financial Text on Stock volatility.
Each component may be instantiated in many ways empirically, in a few ways, or not
instantiated at all. Thus, we can empirically specify the conjectures about the problem. First
order conjectures would be to find out the ‘main’ effects of positive, neutral and negative
sentiments (and their subcategories). Second order conjectures would be to find out the
‘interaction’ effects of positive, neutral and negative sentiments (and their subcategories).
4. Conclusion
The ontology provides a systematic basis inquiry into trading on sentiment in financial text.
The interaction mechanism among the dimension and between the dimensions can be studied
and explained by the semiotic, psychological, or social sciences. They can be studied using
statistical and simulation models.
References
Antweiler, W. and Frank, M. Z. (2004). “Is all that talk just noise? The information content of
internet stock message boards.” Journal of Finance, 3, 1259–1294
Das, Sanjiv R. and Chen, Mike, Y. 2007: Yahoo! for Amazon: Sentiment Extraction from
Small Talk on the Web 1386. Management Science 53(9), pp. 1375–1388, ©2007 INFORMS
Ekman, P., Friesen, W. V. and Ellsworth, P. (1972), Emotion in the Human Face, Oxford
University Press.
Lerner, J.S.and Keltner, D. (2000). Beyond valence: Toward a model of emotion specific
influences on judgment and choice. Cognition and Emotion, 14,473–493.
Liu, B. (2010). “Sentiment Analysis and Subjectivity,” Handbook of Natural Language
Processing, Second Edition.
Loughran, T. and McDonald. (2010). When is a liability not a liability? Textual analysis,
dictionaries and 10.ks. Journal of Finance 66, 35–65.
Ramaprasad, A. and Papagari, S. S. (2009). “Ontological Design.” Proceedings of DESRIST
2009, Malvern, PA Ramaprasad, A., Prakash, A. N. and Rammurthy, N. (2011).
Ramaprasad, A., Prakash, A. N. and Rammurthy, N. (2011). “Construction Project
Management System (CPMS): An Ontological Framework.” Proceedings of Research &
Academic Conference on Project Management. Pune, India: PMI.
Tetlock, Paul C. (2007). “Giving content to investor sentiment: The role of media in the stock
market.” Journal of Finance, 62, 1139-1168.