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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning:
Predicting Stock Market Performance Through Investor's Sentiment Analysis by News
Analytics
Mohotarema Rashid
Department of Information Science, University of North Texas
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Abstract
Stock Market performance prediction is a complicated task. Because there are no straightforward
rules to predict the stock market's performance, market performance is affected by so many
variables. Investors' sentiment is one of the variables. This paper aims to capture investors'
sentiments by analyzing news headlines. The author use mixed method study to predict the
performance of the stock market of NYSE (New York Stock Exchange) In addition, this study
explains investors' sentiment through the lens of prospect theory.
Keywords: stock market performance prediction, NLP, deep learning, investor's sentiment,
machine learning, artificial intelligence, prospect theory
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning:
Predicting Stock Market Performance Through Investor's Sentiment Analysis by News
Analytic
Analyzing human psychology is not easy because how people perceive information when
it comes to decision-making is challenging. Based on that, scholars have been interested in how
people make their decisions. In the era of Machine Learning (ML), Artificial Intelligence (AI), and
big data, people are exploiting opportunities to make better decisions that were not common in the
early 19s. With the advent of the interdisciplinary field like Information Science (IS), researchers
applied information science theory in other domains and vice versa. Since there is no specific area
that IS encompasses, it posits a huge opportunity for IS researchers to explore different research
areas with great impact.
That being said, in a broad sense, IS deals with aspects of processes of the information life
cycle. Information Science is at the intersection of people, information, and technology concerned
with the body of knowledge needed to understand problems and aims at improving the decisionmaking procedure of both individuals and organizations. The interdisciplinary aspect of
information science was addressed by Williams (1998). To solve the problem, like what to do with
the information, he mentioned that information science creates a unified place using theories,
techniques, and technologies of versatile fields. At the same time, he gave examples of such areas:
computer science, library science, management science, and many more. Since information is the
product of almost every field, as a convergence point of many disciplines, information science
deals with collecting, retrieving, processing, and extracting meaningful ideas from information.
The convergence point helps us broaden our view about how information can be obtained and
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
processed so that individuals and organizations can benefit from the science that can enable people
to make optimal decisions. Bates & Maack (2009) connected three things: technology, human, and
information.
Being a business major and having experience as a faculty in the business discipline, I was
always passionate about combing my business knowledge with technical knowledge. Before
pursuing a Ph.D. in IS, I had a minimal background in the technical field. But, since I mentioned
that IS is a field where researchers from different fields collaborate, I found a way to combine my
knowledge with the IS field.
In the era of big data, Machine Learning (ML) and Artificial Intelligence (AI) information
are generated every Nano second, which opens the door to extracting meaningful knowledge from
that information. The stock market is where scholars from different fields amalgamate, making
this domain an interdisciplinary field. Stock market performance prediction through a different
kind of information has long been practiced among researchers. But predicting the performance of
a complex market like the stock market is difficult because of the unavailability of black-and-white
rules. Among the variables that affect stock market performance, some of them can be quantified
and some of them cannot be quantified. We can divide the variables broadly into two factors.
Earnings, board of structures, and specific news regarding companies are considered the
company's internal factors (Jawahar et al., 2020), and macroeconomic environment, government
policy, and, most importantly, investors sentiment are regarded as company external factors (AlTamimi et al., 2011). Investors' sentiment is one of the key components that can shape the
performance of the stock exchange. Among the variables that affect the performance of the stock
exchange, investors' sentiment is crucial, shaping the stock exchange's performance (Sohangir,
2018).
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Because investors are the people who ultimately make decisions about buying and selling
stocks, which ultimately shape the performance of the share market, investors react to the
information they read, hear and share. Since most of the information about companies, markets,
and macroeconomic situations is published online and in printed media, investors' sentiments are
affected. Lillo et al. (2015) suggested that the literature related to news impact on investors'
behavior is not massive. Barber and Odean (2008) found that investors frequently discuss the news
published in online and print media, and they concluded that investors' sentiment is spiked by
sensitive news about the company. Nofsinger (2001) stated that in USA if a news is published in
Wall Street journal both institutional investors and individual investors react to that information.
Efficient Market Hypothesis (EMH) developed by Fama (1970) states that the stock price should
reflect all the information available. Still, this theory was challenged by Kahneman and Tversky
(1979) by saying that in stock price movement, investors' perception of company-specific
information does matter. Since investors are not highly rational, quantifying investors' sentiment
is challenging.
That narrowed my research interest to analyzing investors' behavior through news
analytics. Analyzing the stock market through news analytics is not new (Nemes & Kiss, 2021;
Khalil & Pipa, 2022; Oncharoen & Vateekul, 2018; Li et al., 2014). As a result, this paper focuses
on analyzing investors' sentiment through news headline analytics, so it is imperative to explain
what we mean by investors' sentiment. Investor sentiment is not straightforward, and in existing
literature, the definition of investor sentiment is rarely addressed. Zhang (2008) came up with an
understandable definition of investor sentiment. He argued that investors' sentiment indicates
market participants have notions regarding future earnings in terms of some pre-specified standard.
That standard reflects the value the investors place on the underlying assets. In traditional
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
behavioral finance, the conventional model could not capture how investors' sentiment drives the
stock exchange performance (Barberis, 1998). But the model he developed answers the crucial
questions about investors' weight on the specific news. Investors show under-reaction to company
earnings declarations but over-reaction towards a pool of good and bad information about the
company. Guo et al. (2017) state that specific news spreads rapidly with the development of
communication methods among investors. Due to the spread of the information, individual
investors buying and selling decisions are affected. They concluded that sentiment drives the stock
market when many investors' interests align with each other.
Due to the availability of unstructured textual, massive data machine-learning techniques
come into play. The ML and AI model analyzes stock exchange based on placing past data as input
and predicting the future as output. Khalil and Pipa (2022) claim that the news analytics approach
is in the initial stage and an underexplored area. Combining deep learning models and Natural
language processing can advance this field. With the massive amount of data, this paper's focus is
to develop a structured Natural Language Processing (NLP) technique to capture investors
sentiment through analysis of news headlines.
Problem Statement:
Conducting NLP tasks need a massive amount of data to train the model. And sometimes,
the word meaning might be changed in terms of the domain. "Underestimate" is a negative word,
but in the case of the share market, "underestimated stock" seems a positive word. Agarwal (2022)
claimed that the main challenge in the NLP task is to identify and predict the rare and unseen
words; for that purpose, domain-specific knowledge is a prerequisite, which demands a domainspecific sentiment index with domain-specific words. Traditional ML models, like word-to-vector
and bags of words, suffer from the problem mentioned by Agarwal (2022).
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Traditional Techniques like Natural Language Processing Has Shortcomings:
 Domain-based Word Meanings can be different
 Traditional model suffers from the wrong prediction of unseen and rare word
Research Question
The shortcomings of the traditional ML model can be overcome by employing deep
learning techniques. Oncharoen & Vateekul (2018) claim that conventional NLP methods, like
word embedding, suffer from the problem of sparsity in the dataset. They employed a deep learning
model on the dataset of Reuters, Reddit, Intrino and reported that deep learning models like CNN
and LSTM give better prediction power. Correia et al. (2022) described the process of deep neural
networks producing sentiment and proposed a novel approach like a deep learning-based
classification framework for stock market sentiment prediction. They reported the accuracy level
on training data as around 73% and on test data as around 69%. Since I intend to develop and apply
domain-based sentiment analysis and overcome the shortcomings of the traditional NLP model by
employing deep learning techniques that led me to generate the below-mentioned research
questions:
•
How does the news-headlines affect the investors sentiment?
•
Does domain specific sentiment analysis outperform general sentiment analysis?
•
And what is the best algorithm for predicting stock market performance?
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Hypothesis
 In combination with the deep learning model, NLP can predict stock market performance
with optimal accuracy.
 Investors sentiment can be captured through news analytic approach.
Literature review
It's not entirely novel to think about using text analysis to study the financial markets, and
sentiment analysis's influence on them is well known. According to a research by Klein and
Prestbo (1974), which demonstrates that a negative financial news story can impact the markets.
They also concluded that content of the news report and performance of the market are strongly
correlated.
Engle and Ng (1993) put out the idea of a news impact curve as a way to use news to
describe market phenomena. Guo et al. (2017) compared sentiment of stock market on different
period in China and he concluded that sentiment data can be a great indicator of performance of
stock market. But they concluded that always investors sentiment cannot be a greater indicator of
market performance rather it depends on the market specific factors.
By developing a novel framework Wuthrich et al. (1998) predicted the performance of
stock market through analyzing financial news. They concluded that investors concentrate on the
financial stories of the specific company. As a result, it is well accepted that investors pay great
deal of attention to the news headlines (Melvin & Yin, 2000). Wang et al. (2020) developed a
sentiment analysis to provide insights in implementing effective public health initiatives. Their
study data collected from a Chinese social media website and they divided sentiment into positive,
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
negative and neutral and extracted the summary of the theme by using TF-IDF (term frequencyinverse document frequency) model.
Chan & Chong (2017) extracted insights from unstructured data like textual data and found
the trends of financial market. They developed a novel approach called Sentiment Analysis
Engine(SAE) based on linguistic analysis. Then extended their model to not only in token or word
level but also in phrase level. And their model outperformed the traditional Bag of Words (BoW)
model. They concluded that sentiment analysis is helpful to analyze stock market index albeit such
sentiments and stock market index are not perfectly correlated.
Corriea et al. (2022) mentioned that combination with deep learning and deep learning can
give insights in case of extract insights. In this study the authors showed how deep learning reacts
to different contexts. They provided a generalized deep learnings classifications model to analyze
financial sentiments. In their study Convolutional Neural Network (CNN) outperformed among
the several deep learning model and attained accuracy of around 73% on training data and 69%
accuracy on test data.
Das et al. (2022) stated that the prediction of stock market is a very challenging task
because of the heterogeneous components affect the stock market. They predicted the performance
of stock market using public sentiment and analyzed those sentiments through Long and Short
Term Memory Network (LSTM). They collected data from Newspaper and Facebook and used
seven sentiment analysis tools like VADER (Valence Aware Dictionary for Sentiment Reasoning),
Logistic Regression, Loughran–McDonald, Henry, TextBlob, Linear SVC (Support Vector
Machine), and Stanford, they have concluded that Linear SVC contributed towards a large number
of accuracy.
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Kalyani et al. (2016) incorporated Efficient Market Hypothesis (EMH) stated by Fama
(1970) that market price of the stock should be reflected all the available information in the market.
Through their study they tried to assess the relationship among financial news and stock market
prediction. They have used three classification models like Naïve Bayes, Random Forest and
Support Vector Machine (SVM). And they have concluded that Random Forest and Support
Vector Machine attained greater accuracy.
Biswas (2020) mentioned that according to financial researchers, news articles, blogs, and
stock market predictions are significant themes that influence many businesses' revenues. He used
past online news to track the stock market trends by using several machine learning algorithm and
concluded that the stock market experts have a big influence on the stock market investors.
Schumaker & Chen (2009) analyzed 9211 financial news and stock quotes from S&P 500
for five weeks and predicted discrete numeric prediction through Support Vector Machines. The
authors developed a novel system design called AZFinText system design. To analyze the texual
data the authors used three Natural Language Processing (NLP) techniques Bag of Words, Named
Entities and Noun Phrases. They concluded that in terms of texual representation proper noun
scheme outperformed other techniques.
Li et al. (2011) focused on the accuracy of the machine learning model by accumulating
the information that are latent in market news and stock exchange performance. They used a
technique called multi-kernel learning technique to predict the Hong Kong Stock Market.
Usmani & Shamsi (2021) developed a survey of the techniques that have been used in
financial domain to predict the stock market price. Through their survey framework the authors
articulated the pros and cons of the present machine learning models.
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Financial sentiment through news headlines is not new in the literatures. In the era of big
data and machine learning, an unstructured data like text data posits a huge opportunity for the
researchers to exploit the opportunities. As a result, this paper aim is to shed light on the investors
sentiment, which ultimately shape the performance of stock exchange.
Conceptual Framework
Since the problem I have identified that deals with human psychology while making decisions,
peoples decisions making behavior and Investors decision making behavior can be explained by
Prospect Theory developed by Kahneman & Tversky (1979).
This theory deals with explaining people’s decision making under risky circumstances. The
theory comes into play when expected utility theory developed by Von Neuman and Morgenstern
(1953) could not fully explain the decision making choice of individual. Expected utility theory
just showed if there are set of choice for human to make, optimal decision would be that decision
which would maximize the satisfaction of the decision maker. Individual choose the decisions by
quantifying the outcomes of each decisions by probability of the frequency of that outcome. This
theory does not take into account the cognitive limitation of human being (Sebora & Cornwall,
1995).
Prospect theory shed light on the irrational part of the human being. The difference between
prospect theory and utility theory is that under utility theory individual makes decisions in terms
of the final wealth and probability of an outcome where in prospect theory individual makes
decisions in terms of the perceived value he/she gives on an outcome. This theory based on three
principles. They are:
i.
Individuals perceived gain and loss depends on his or her reference point
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Under this principle how individual makes decision depends on the perception of the individual.
For example, On the same course someone is happy with securing D and other student is not happy
with securing B. The underlying cause is the reference point. The person who secured D his target
was to just pass the course on the other hand the person who secured B his target was to secure A
on that particular course.
ii.
Individual adopts a risk-averse strategy when it comes to perceived gains and a riskseeking strategy when it comes to perceived losses.
Under this principle individual underestimate those outcomes of which he is not certain of.
And when individual makes decision in terms of profit they will prefer certain profit to more
profit with possibility of potential loss. For example, if an investor is given two options:
Option 1: gain profit of $10 with no potential loss
Option 2: gain profit of $30 with potential loss
According to prospect theory individual will prefer option 1 , because in terms perceived gain
investor shows risk aversion behavior.
But when it comes to perceived loss, an individual behaves in a completely different way.
Consider the two options that follow, for instance
Option 1: a $10 loss with a guaranteed amount and no potential gain
Option 2: Potential gain with infinite loss
According to prospect theory, in the aforementioned scenario, the person will exhibit a risktaking attitude and select option 2.
iii)
individual is very sensitive towards loss
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Under this principle individual puts more emotion when he experienced loss. Same amount
of loss cannot compensate with same amount of gain. For example, the feeling of loss of $50
cannot compensate the gain of $50. Let’s elaborate this principle with additional example:
If Individual is given two options:
Option 1: Having $ 100
Option 2: Having $200 at first place then take away $100, and ultimately left with $100
Though the net worth in the above two options are same but individual will prefer option 1.
Different fields have used prospect theory. The study of information systems has made great use
of it. Prospect theory is used by disciplines that study human psychology, decision-making, and
behavior to explain unique behavior.
One of the best models for describing the activities of particular decision-makers is prospect
theory (Wang, 2021). This theory is well known for predicting future stock market returns and
various market phenomena. The expectations of expected utility theory frequently diverge sharply
from the prospect theory-captured irrational risk attitudes of individual investors. My research
problem is capturing investors' sentiments by evaluating news headlines, and this research
demands a theory that can forecast investors' psychology. The prospect theory holds that investors
are risk-averse when it comes to possible gain and risk-seekers when it comes to potential loss.
So, through my analysis, I hope to demonstrate whether investors are buying safe stocks during
bullish market conditions and investing in risky stocks during bearish market conditions.
Prospect theory has long been employed in the financial industry to describe how investors
behave in the stock market (Barberis, 2013). Payne et al. (1984) used utility theory and prospect
theory to analyze the choices made by more than 100 American managers. Their findings align
with the principles of prospect theory.
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Using various case studies, Lee and Park (2019) assessed user experience and examined the
prospect theory's validity. Additionally, their findings are consistent with prospect theory's tenets.
Figure 1: Conceptual Framework
Prospect theory addresses the loss aversion behavior of investors. Experts of stock market
stated that loss aversion have been practicing by investors after pre COVID era. In COVID period
investors experienced loss significantly. As a result, through guidelines of the prospect theory I
would implement a mixed method study to test the validity of the prospect theory. I would also
test whether demographic trends among investors influence the loss aversion among investors.
Since prospect theory states that investors make their decisions in terms of their emotional
weight given to the specific situation and their perception. So through interviews and semi
structured questions, I would try to extract those information, on which specific news investors
put more weight and how that affects their buying and selling pattern of stock, which can shape
the performance of the stock market.
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
Then I will cross check the findings of qualitative study with the quantitative study. By
applying ML and deep learning technique to news headline I would extract meaningful insights
and will compare the result and test the validity of the theory, which will lead me to answer my
research questions.
Conclusion
Predicting stock market performance through newspaper headlines has gained attention
among researchers. Textual data is unstructured but in the era of AI, ML, big data it posits huge
opportunity for the researchers to exploit those textual data through AI, ML techniques. A
theoretical integration with the AI, ML can give us greater insight regarding a volatile market like
stock market. As a result, this study use prospect theory as a guideline which prescribes investors
decision making pattern certain circumstances. By revisiting prospect theory after its initiation of
40 years, we can conclude when we have limited clue how to analyze complex thing like investors
sentiment, by applying prospect theory with the combination of AI, ML will make our study
robust.
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Revisiting Prospect Theory in the Era of Artificial Intelligence and Machine Learning
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