INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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Decoding the Investor's Mind: Exploring Neurofinance in Decision-
Making
1
Greeshma Francis.,
2
Dr Madhavi Lokhande
1
ISME Bengaluru (Research Scholar -University of Mysore) Bengaluru
2
ISME Bengaluru, Research Supervisor
DOI: https://doi.org/10.51244/IJRSI.2025.120800393
Received: 06 October 2025; Accepted: 12 October 2025; Published: 18 October 2025
ABSTRACT
This paper explores neurofinance as a new domain integrating neuroscience, psychology, and behavioural
finance to understand how investors make decisions. Risk-taking, reward expectation, and loss aversion—all
core behavioural biases in investments—are driven by the activity of certain neural structures, the nucleus
accumbens, amygdala, and insula. The author proposes the use of artificial intelligence and machine learning
in neurofinance to improve the predictive capabilities of robo-advisers. While mentioning the primary
obstacles of laboratory settings, ethics, and privacy, the author also alludes to the growing prospects of
neurofinance in the development of future emotionally and rationally balanced investments. The anticipated
outcomes are encouraging, but research in the neurofinance domain suffers from a number of limitations -
lackluster laboratory conditions, low participant numbers, and a paradigm shift towards the need for
knowledge in neuroscience. Through the lenses of neuroscience and behavioural finance, this research hopes to
make a step toward understanding investor behaviour and the mental aspects associated with financial choices.
Keywords: Behavioural finance, Traditional finance, Neurofinance, Investment decision
INTRODUCTION
The search for financial well-being is a lifelong companion in human existence. As humans are emotional
beings, our wants go beyond the satisfaction of our immediate desires and encompass security, stability, and a
future that will be shaped by the decisions we make today. The paper attempts to shed light on the
relationships between saving and investing and the ways in which the developing discipline of neurofinance
influences the choices we make that determine our financial futures. The human psyche is fundamentally
driven by the desire for security, comfort, and the ability to satisfy desires, which gives rise to the quest for
riches. Money serves as a symbol of authority and independence, and it can be used to achieve personal goals.
As a result, the urge to increase money through investments makes logical because it provides the opportunity
for both financial gain and social progress.
People make their way through the maze of investing options while balancing risk and return to achieve both
financial success and a deep feeling of fulfillment. People begin investing because they wish to increase their
income. However, not everything about this process is rational and well-considered. Many biases in our
thinking cause us to make judgments that may not be the best use of our financial resources. In fact, the human
brain also makes contradictory conclusions. That is extremely clear in terms of what motivates a person to play
the lottery and what mindset a person has while purchasing insurance.
It’s interesting to analyze why people buy lottery tickets and insurance. Insurance is a financial hedge from
unanticipated losses with a negative expected return on investment. Buying lottery tickets is a form of
gambling that involves accepting a lower-than-expected return in the goal of obtaining a larger profit.
Strangely, while buying lottery tickets to make money, we also get insurance to protect ourselves from
potential losses.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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The emerging science of neurofinance is shedding light on the complex interplay between money and
cognition and demonstrating how our brains actually affect the investment decisions we make.
It is a multidisciplinary field at the juncture of neurology, psychology, and finance that is gaining importance
in understanding and influencing investment decisions. The world of Neurofinance is a recent field that probes
into the inner workings of the human psyche to reveal the cognitive mechanisms underlying financial
judgment. It offers a prism through which we may see the brain mechanisms influencing our decisions as
individuals struggle with risk, reward, and the intricate interaction of emotions. The brain landscape plays a
complex role in determining one's financial fate
Traditional Finance, Behavioural Finance, And Neuro Finance
The transition from classical finance to behavioral finance, and then to microfinance, marks a paradigm shift in
understanding and explaining financial decision-making processes. Conventional theories, like Efficient
Market Hypothesis (EMH), assume that people make rational decisions based on all available information.
(Peterson, 2010) Conventional finance explains how markets work and how to distribute resources most
effectively when there are risky, uncertain, time-sensitive, and strategic financial options.
Behavioural finance challenged the rationality assumption, recognizing that people frequently avoid strictly
rational decision-making due to cognitive biases, emotions, and psychological factors. The genesis of
behavioural finance stems from the study of cognitive psychology. It is a branch of psychology that focuses
on an individual's internal processes, such as language, visual perception, recall, thinking, learning, feeling,
problem-solving, judgment, and decision-making. From a cognitive psychological perspective, emotion is
viewed as a reaction to a stimulus or event following some form of evaluation or consideration (Merkle, 2007).
Hence, while emotional elements greatly impact a person’s financial decision, there are also default human
mental condition constraints that are bound to exist. In this case, there is bound to be a shift in thinking which
is considered bias and needs recalibration (Pompian, 2006). Emotions and affect, which evoke certain levels of
mental activity and action, are now known not to undermine mental functions by design (Zajonc, 1979;
Haselton et al., 2005; Peterson, 2007a, b).
Neurofinance examines the neurological underpinnings of economic judgments by monitoring brain activity
during financial activities using state-of-the-art methods like functional magnetic resonance imaging (fMRI).
The discipline of neurofinance emerged as a result of certain academics going above and beyond to investigate
how and why these violations occur in the brain. This evolution enables researchers to identify specific brain
regions involved in risk perception, reward anticipation, and decision making. The shift from behavioural
finance to neurofinance enhanced our understanding of financial decision-making. When Behavioural finance
illuminates our decision-making biases and heuristics as people negotiate the turbulent waters of investing, risk
aversion, and reward-seeking. In addition, neurofinance provides a detailed explanation of the cognitive
processes influencing financial behaviour by revealing the brain circuitry coordinating these very biases. The
studies on neurofinance provide a broad perspective that helps us understand the complex interplay between
the financial world and the human psyche, paving the way for a more thorough understanding of economic
decision-making. It advances our knowledge of the cognitive biases and heuristics that influence investor
behaviour in concert with behavioural finance. Various theories, such as prospect theory, loss aversion, and the
endowment effect, identify neurological correlates that clarify the processes by which people depart from
conventional economic rationality.
RESEARCH DESIGN AND METHODOLOGY
The present study adopts a conceptual research design guided by extensive theoretical and empirical works in
the areas of traditional finance, behavioural finance, and neurofinance. There is no primary data collection and
quantitative analysis; hence, the focus is on integrative synthesis and not statistical testing. This study employs
a narrative literature review in which the author analyses the literature in a flexible manner, but with a degree
of structure to demonstrate the progression of the literature and to show new developing interdisciplinary
connections between psychology, economics, and neuroscience.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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The relevant studies were identified from searches in the Scopus, Web of Science, Science Direct, and Google
Scholar databases by combining several keywords such as “neurofinance,” “behavioural biases,” “investor
decision-making,”.The inclusion criteria were peer-reviewed journal articles, book chapters, and conference
papers published
Neurofinance In Individual Investor Decision-Making
Numerous studies highlight the critical role that brain processes play in influencing economic decisions, which
have ramifications for how people perceive risk, anticipate rewards, and behave while investing, which is
frequently irrational
(Fama, 1998)The efficient market hypothesis (EMH), which holds that one cannot continuously outperform
the market, is the foundation of conventional financial theory. It asserts that prices are based on a rational
evaluation of all available information, providing little to no opportunity for arbitrage. (Markowits, 1952) It’s
primarily assumed that humans are rational agents and utilize perfect information to make rational decisions.
(Barberis, 2003)However, further studies proved that individual experience biases like loss aversion and
narrow framing. (Tversky A., 1992)Subsequently prospect theory captures a major observation that loss
incurred appears larger than the gain. This gives a different perspective for losses and gains. (Hirshleifer,
2015)While several research show that integrating behavioural and psychological insights might help to lessen
the gaps between theory and empirical data, these more realistic models' prediction ability still needs to be
enhanced. The transition from traditional finance to behavioral finance acquired importance in the early
twenty-first century, representing a relatively recent evolution in the study of financial decision-making. It
reflects a growing realization that human behaviour differs from the rational assumptions of traditional
economic models. Behavioral finance recognizes that psychological biases, emotions, and cognitive errors play
a substantial role in determining financial decisions
(Ratcliff R., 1998)A cognitive process that happens often and is fundamental to human conduct is decision-
making. Based on preset criteria, a selection is produced from a collection of items. It is well acknowledged in
the field of cognitive research that a person's ability to make decisions is diminished when they suffer a frontal
lobe lesion.
In another study, De Martino et al. (2006) studied the neural correlates of the ‘framing effect’, which is a
cognitive bias where people respond to the same financial problem differently depending on how it is
presented. Their research tied the framing effect to increased activity in the amygdala, further strengthening the
impact of emotions on financial decision-making. Emotions, on the other hand, do impact investment
behaviour, which Peterson (2007) documented. According to his research, the lack of proper control over
affective systems in the brain could result in mistaken financial decisions. Peterson proposed that the
integration of psychology is needed to understand the behavioural mechanisms underlying financial choices.
Supporting this view further, Knutson and Bossaerts (2007) studied the interplay between reward and risk
processing in the brain. They reported that the ventral striatum is essential in the encoding of gratification,
especially concerning the expected reward contained in income streams, while the insula is more heavily
involved in life risk perception representation. This difference indicates that separate brain circuits are
responsible for reward anticipation and risk evaluation, which helps explain some dangerous, inconsistent risk-
taking patterns people display under different circumstances. Equally, Tom et al. (2007) demonstrated that loss
expectancy diminishes activation in the areas associated with the subjective value, not with greater activation
in the areas related to negative emotions. These results offer neurobiological accounts for loss aversion, which
is known to be a behaviourally biased phenomenon in finance.
The interdisciplinary characteristics of neurofinance were described by Goetz and James (2008), who
supported the application of neuroscience to economics, psychology, and biology, arguing that these fields
must be integrated to appreciate the complexities of financial decision-making. Their conceptual work argued
that financial planning should incorporate far more than simply economic models; underlying neural
mechanisms associated with behaviour need to be taken into account. This was supported by Knutson et al.
(2008), who showed that reward-evoking stimuli, even if incidental, could trigger spontaneous NAcc
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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activation, which in turn biased participants towards greater risk-taking in finance. This means that external
stimuli, even when unrelated to actual financial returns, may subconsciously alter one’s willingness to take
risks.
Preuschoff et al. (2008) provided a more comprehensive literature review examining neural mechanisms
underlying different levels of decision-making, along with reward expectation and risk perception influencing
financial choices. Their work vindicated Markowitz’s Modern Portfolio Theory (MPT) as it proposed that
neural activity is associated with valuation processes formulated by return and risk analysis. Proposing further
argument to this hypothesis, Kuhnen and Chiao (2009) studied the role of neurotransmitters such as dopamine
and serotonin on financial decision-making. Their analysis showed that the processes assessing financial risks
and returns are so affected by genetic variation influencing these neurotransmitter pathways, suggesting
biological predisposition leads some investors to be more risk-seeking while others are more risk-averse.
Kuhnen and Knutson (2011) evaluated how emotions affect financial decision-making and observed that
investors in a good mood tend to take higher risks, whereas those in a bad mood tend to be more conservative
with their finances. These observations correspond with behavioural finance concepts, which posit that the
psychology of an investor is crucial to both market movements and volatility. Stenstrom and Saad (2011) went
on to study the effect of hormones, particularly the role of testosterone, on the behaviour of individuals in
finance. Their findings claimed that high testosterone levels, for example, are likely to endorse the pathological
gambling risk syndrome and aggressive behaviour in financial decision-making.
Wu et al. (2011) studied how financial skewness, or the asymmetry in potential outcomes associated with an
investment, affects neurophysiology and emotional reaction. Their results suggested that emotion-laden
motivations, not mere logical factors, lead investors to act upon the opportunity to exploit highly skewed
financial chances. This research provided more evidence that suggests financial decisions are made in relation
to emotional and psychological factors, instead of reasoning and logic.
To summarize the milestones of studies conducted in the field, it can be identified that in the early 2000s, the
research focused on identifying neural correlates of financial decision-making. By mapping reward and risk-
related brain areas using neuroimaging methods, research aimed to find neurological correlates of financial
decision-making. From the middle of the 2000s to the beginning of the 2010s, research combined behavioral
finance theories with brain data to investigate how emotions and cognitive biases affect investing decisions.
The mid-2010s saw a change in focus towards real-world uses, such as comprehending how stress hormones
affect risk choices and investigating strategies to enhance decision-making.Post 2020 A more individualized
approach to financial behavior analysis is reflected in recent research that highlights technical innovations,
such as EEG neurofeedback and explores the function of emotions in certain demographic circumstances
Thus, neurofinance arose as an organic development to understand the complex neural systems that influence
economic decisions. with neuroimaging techniques acquired unparalleled insights into the brain circuits linked
with risk perception, reward processing, and decision-making.
The significant correlational study offered relevant proof that investment decisions are influenced by emotions
that are artificially stirred up through different brain circuits.Studies demonstrates how neural mechanisms
impact financial decision-making as investment behavior is shown to be dominated by irrational factors such
as cognitive biases and emotional responses. The intricate network of the nucleus accumbens, amygdala,
insula, and various neurotransmitter systems involved in financial decision-making argue for amplifying
traditional economic models of investor behavior to include neurobiological perspectives.
Neural Correlates Of Behavioral Biases
The results of numerous neuroscience studies demonstrate that specific brain regions become active prior to
the actual decision behaviour, which occurs before the decision is made.
The behaviour is not acting in a way that makes sense. It can therefore be said that an individual's genetic
makeup and personality have an impact on brain activity. Additionally, a person's genetic makeup may cause
them to react affectively to some stimulants but not to others.Neurofinance examines behavioural biases by
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analysing brain activity associated with decision making with uncertain outcomes. Each bias utilises unique
neural pathways responsible for valuation, emotion, memory, and social cognition (Kuhnen & Knutson, 2005).
Overconfidence Bias
Overconfidence, in which investors tend to greatly inflate their knowledge or skills, is linked to neural
pathways in the dorsolateral prefrontal cortex (DLPFC) associated with executive control and the reward
anticipation (Kuhnen & Knutson, 2005) mechanisms of the ventral striatum, particularly the nucleus
accumbens. This bias implies that the DLPFC is malfunctioning, and so the investor in question is deemed to
be overconfident, and thus their risk is underestimated (De Martino et al., 2006). fMRI studies have shown that
the prediction of risk-seeking behaviour appears greatly overestimated in the nucleus accumbens and,
simultaneously, the insula is underactivated. Thus, overconfidence is suggestive of a neural disproportion of
reward expectation and cognitive appraisal of risk.
Loss Aversion
Loss aversion manifests as the disutility suffered as the result of the potential for loss as opposed to the gains
achievable, and is the result of the amygdala and insula, with the corresponding fear and emotional responses
to loss, together with the amygdala’s mPFC for its valuation (Tom et al., 2007). fMRI data shows losses result
in greater neural action as opposed to comparable gains in the strata, and mPFC, and serves as reinforcement to
Kahneman and Tversky’s prospect theory in biological terms. Kuhnen and Knutson (2011) in addition showed
amygdala activation is greater for those who sell prematurely, suggesting the respondent is in an emotionally
hypersensitive state to loss.
Anchoring Bias
Anchoring is the phenomenon of pegging and therefore valuing heavily the first piece of information. It is the
result of the posterior parietal cortex (PPC) as well as the orbitofrontal cortex (OFC). Engelmann et al. (2009)
observed the PPC records and remembers numerical ‘anchors’ before the corrective phase of the thinking
process, while the OFC frames and interprets the value to create biased reference points. The insula in this
instance serves to explain the discomfort to transgressing the anchors, suggesting areas of loss in the emotional
span as well as rigidity in thinking.
Herding Bias
Herding behaviour, or post, follows the personal conclusion of an individual's decision-making process and
supports the group conclusion by appending their opinions; the decision of focus of interest is probably centred
around the ACC, the so-called anterior cingulate cortex, and the temporoparietal junction which delineates the
boundaries of social thinking and the application of the theory of mind (Klucharev et al., 2009). Burke et al.,
2010 postulated that the ventral striatum is also involved, or more precisely, herds cow postulates the winner
of dopamine and ventral striatum influenced that the decision winner is pleasing. Burke et al. consider a group
opinion is also accepted, evidence clusters that support conformity are Burke et al., 2010. Ipsative scoring is
non-voluntary herding at the lowest net social level is by no means an instance of social imitation.
Mental Accounting and Framing
Mental accounting has been described as the division of money into non-related subcategories, which is also a
labour of skill, and the amygdala respectively. The influence of framing also Knutson et al., 2007 framed that
the desire to ask finances that are primary eluded and is more common than those which come on an emotional
level rather than on rational (Kahneman et al. 2003). Still in rampant comparable De Martino et al., 2006
extends the observation that the amygdala is that her Why is so an apple pie unable to framed means version to
a linear promise value.
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Regret Aversion and the Endowment Effect
Knutson et al. (2008) showed that the avoidance of future remorse (regret aversion) engages the insular region
of the brain and the posterior cingulate cortex (PCC) and, in particular, the cingulate cortex and other areas of
the brain that self-reference and self-evaluate and emotionally contest (Knutson et al. 2008). Weber, where the
orbitofrontal cortex (OFC) is crucial, and others (2009) showed the case of the Endowment Effect (ownership
increases perceived value).
This seems to suggest that regret aversion and endowment bias might differ in reasoning yet share the same
neural mechanisms.
Biases of Availability and Representativeness
The Availability and Representativeness Bias overlap (especially in scenarios of “over-relying” on the
preference of the most dominant information) and are controlled by the hippocampus (retrieval) and are often
paired with the amygdala (with emotions) and the ventromedial prefrontal cortex (decisions).
A fluent example is the work of Bechara et al. (2000) who showed that, with respect to the Iowa Gambling
Task, dysfunction of the ventromedial prefrontal cortex (vmPFC) and amygdala was associated with impaired
risk perception. Subsequently, Camille et al. (2011) illustrated that vmPFC injury patients, contrary to other
patients in whom reduced reliance on emotional heuristics is observed, provide evidence in support of the
neural model of judgement that is often correct, but in other cases misguided.
Brain And Decision Making
Human brains engage in a remarkable movement of neurotransmitters and neuronal connections when we
anticipate rewards. An important factor in determining an individual's financial preferences is this brain
reaction. The complex dynamics of human behaviour in financial affairs are not well captured by the
conventional finance theory, which is predicated on the idea that people make rational decisions within a
controlled framework. Humans are driven by a complex mix of emotions, instincts, and cognitive processes,
unlike the predictable and regulated entities envisaged in economic models.
The recognition of the human natural capacity for empathy and social cognition is a critical component in
comprehending the relationship between neurology and investment choices. Making decisions is not a lonely
activity that takes place inside one's own head; rather, it requires the capacity to understand and reproduce the
feelings and experiences of others, particularly when they are in a public place. Investment decisions are
heavily influenced by this social component since people might be influenced by the opinions of their peers,
market trends, or the group's overall emotion.it's interesting to note, though, that this association does not
explain why financial decisions are predictable, even with improvements in neuroscience and brain research.
The intricate relationship between neurology and investment preferences is shown by the fact that
unpredictable results frequently result from actions that appear to be sensible.
(Fliessbach K, 2007) demonstrate a substantial relationship between individual variances in brain responses to
wins and losses and variations in information search across risky bets. (Niklas, 2016)Individual differences in
brain reactivity to wins and losses are linked to variances in information search vs risky gambles. Activity in
the ventromedial prefrontal cortex during gain processing correlates favorably with attention to the monetary
value of risky gambles. The activity in the ventral striatum and posterior cingulate cortex during loss
processing is positively linked with subjects' attention to gambling probabilities
Brain Imaging To Understand The Mind
Understanding the complexities of making investment choices has been a key emphasis in the science of
neurofinance. As financial markets grow, understanding the intricacies of making choices becomes
increasingly important for forecasting market trends and optimising investment strategies. Researchers can
investigate the brain networks that underpin financial decisions using cutting-edge technologies such as
functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). EEG readings often
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capture spontaneous and event-related brain activity. Spontaneous EEG reflects unprovoked neural responses,
including behavioural manifestations. (Williamson SJ, 1997)Spontaneous EEG is commonly utilised in clinical
settings to evaluate seizure disorders, but has not been widely used in cognitive neuroscience research.
fMRI is one of the most current types of neuroimaging techniques. Because of its minimal invasiveness, lack
of radiation exposure, and widespread availability, fMRI has emerged as the main tool in cognitive
neuroscience since the early 1990s.
Techniques For Brain Mapping
Technique
Purpose
Electroencephalography (EEG)
Detects electrical activity in the brain by recording wave patterns.
Computerized Tomography (CT) Scan
Uses X-rays to generate cross-sectional images of the brain.
Magnetic Resonance Imaging (MRI)
Provides high-resolution images of brain structures without ionizing
radiation.
Positron Emission Tomography (PET)
Uses radioactive tracers to assess metabolic activity and chemical
processes.
Magnetoencephalography (MEG)
Measures magnetic fields produced by brain activity for real-time
monitoring.
Diffusion Tensor Imaging (DTI)
An MRI-based technique that maps white matter tracts by tracking water
diffusion.
Near-Infrared Spectroscopy (NIRS)
Measures oxygen levels in brain tissues using near-infrared light.
Source: psychcentral.com,Medically reviewed by Seunggu Han, M.D. ,Updated on October 22, 2021
,Consolidated by Author
Application Of Neuro Finance And Future Scope
In terms of investment, neurofinance approaches provide essential insights regarding the psychological
variables that influence market behaviour. The field of neurofinance has deeply shed light on how investors
perceive risk, make decisions, and respond to financial uncertainty. Unlike classical finance theories which
assume rational decision-making, neurofinance analyses the impact of brain activity, through various imaging
techniques such as fMRI and EEG, on financial choices (Lo, 2012). It has been established that the prospect of
financial rewards activates an important brain area known as the nucleus accumbens (NAcc), which has high
associations with pleasure-seeking and risk-taking activities (Knutson & Bossaerts, 2007). On the other hand,
the anterior insula – known for associating with perception of fear and loss – leads individuals to risk aversion
and panic-driven financial decisions (Kuhnen & Knutson, 2005).
One major application of neurofinance is in portfolio management, where investors can be trained to become
aware of their biases and work to overcome them. For example, behavioural finance is rapidly finding its way
into automated financial advisory systems (robo-advisers) which alter managing funds heuristically or based
on an individual’s risk perception and cognitive biases (Frydman & Camerer, 2016). Moreover, neurofinance
explains why herd behaviour exists in financial markets, where traders ignore inherent risks and blindly follow
market trends, causing speculative bubbles and crashes (Shiller, 2000).
Investors have been documented to be influenced by cognitive biases such as overconfidence, loss aversion,
and framing, which have been integrated within the domain of behavioural finance (Kahneman & Tversky,
1979). With regard to these cognitive biases, neurofinance validates Kahneman and Tversky’s work by
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providing a biological basis through the identification of neural correlates of biases. For example, Kuhnen and
Chiao (2009) found that individual differences in risk-taking behaviour are related to genetic variation that
affects dopamine and serotonin neurotransmission. This shows that an investor’s appetite for risk is not only
learned, but depends upon biological underpinnings.
Moreover, neurofinance furthers behavioural finance by addressing the realm of emotional investing and
explaining situations when investors do not act logically due to fear or exhilaration (Lo & Repin, 2002). In
particular, the phenomenon of loss aversion is described by Kahneman and Tversky (1979), who state that the
emotional experience associated with financial losses is more severe than that associated with financial gains.
Through the incorporation of biometric measurements and brain imaging, institutions can design customised
plans aimed at reducing the impact of emotions on strategic decision-making. Neurofinance has a particularly
bright future with the developments of AI and machine learning that can use neural information to anticipate
the actions of investors (Frydman et al., 2014). One such development is called neuroadaptive financial
advising, which utilises real-time neural data to provide advice for a particular strategy/method (Sapra & Zak,
2019).
Another unexplored future area of research is the application of wearable neurotechnology, particularly EEG
headsets, that monitor real-time stress levels and cognitive load of investors (Hafner et al., 2021). More
attention is also being given to studying the differences between men and women in neurofinance as some
studies point out that there are different neural pathways engaged by risks in finances in the two sexes
(Sapienza et al., 2009).Alongside the potential benefits, some ethical issues related to the area of privacy, data
protection, and possible alteration of behaviour of investors present a difficulty (Ariely & Berns, 2010).
Regulations need to be formulated to protect against the ethical misuse of neurofinance tools in the market.
The amalgamation of neuroscience and behavioural finance has augmentatively changed investment decision-
making through neurofinance by providing a more detailed understanding of risk perception, emotional biases,
and financial behaviour. With further development in technology, neurofinance is destined to evolve along
with AI-driven financial management systems and real-time tracking of investor activities. Regardless, there
are ethical and methodological issues that need to be resolved to guarantee the adequate employment of
neurofinance principles within the scope of finance.
(P. Burrell, 1997)The use of neural networks in finance, particularly investment, has produced promising
outcomes in terms of decision-making and forecasting . (Sahi, 2012).The discipline of neurofinance, which
tries to understand the brain processes that drive investment behaviour, is still in its early stages . (James,
2011) Integrating neuroscience results into financial planning practice has the potential to increase financial
planner effectiveness.
For instance, fMRI enables analysts to monitor brain activity in actual time whereas investors make
investments. Researchers can identify risk perception regions by comparing brain activation patterns to risk
assessments. financial professionals could utilize fMRI data to measure individual risk tolerance and adjust
investment portfolios to match investors' brain reactions to risk.
By better understanding how the brain receives information and makes decisions, investors may streamline
their decision-making processes and steer clear of frequent pitfalls by learning about the neurology of decision-
making. Understanding the psychological underpinnings of market events and how investors often respond to
them will help them to predict and respond to market movements more effectively. With this knowledge, one
can manage the stress and make more educated decisions under unpredictable market conditions.
People will be able to recognize how emotions influence their decision-making when they have a better
understanding of how the brain functions during decision-making. With this knowledge, they will be able to
make better selections.
Constraints Of Neurofinance
Neurofinance has enhanced the understanding of investors by merging neuroscience with behavioural finance,
but there are many methodological and practical issues that restrict its use in actual decision-making in finance.
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These challenges arise from the issues of laboratory research, sampling and selection bias, sociobiological
ethics, and the multidisciplinary knowledge needed to perform and analyse neurofinance research.
The majority of neurofinance studies tend to emphasise the participant observation method which is conducted
solely in the confines of a laboratory. This, along with the medical supervision that comes with it, does not
capture the essence of actual financial decision-making, far removed from the decision to be made in the real
world. Self-reports may bias their responses due to altered phenomenic concourse changes engagements in the
frame, leading to range-bound behaviour at least temporarily, resulting in a change of emotions. Different
phenomena may manifest different variations in behaviour solely because they know that they're being
observed. This assumption is widely known as the Hawthorne effect (McCarney et al., 2007). For example,
heightened stress levels within laboratory conditions may subsequently lead to altered cortisol secretion which
affects risk-taking behaviour (Kandasamy et al., 2014). This represents a challenge arising from the
controllable validity issues neurofinance leaves to be explored, as investment behaviour from real markets
highly relies on myriad uncontrolled factors that influence emotions and market sentiments which tends to be
makeup constrained in the laboratory.
Peterson (2010) and Lohrenz et al (2007) note that many neurofinance studies, especially those reliant on fMRI
and EEG technologies, utilise fewer than one hundred participants, most of whom are students or medical
patients. One of the most fundamental challenges in neurofinance research is the small sample size, as this
greatly limits their statistical power and generalizability. These narrow sample populations often lack the
heterogeneity needed for generalisability, for example, institutional traders and hedge fund managers alongside
retail investors and self-directed investors are seldom included (Camerer, 2018). Age, gender, cultural
background, and even one’s past investment behaviour are critical to forming one’s identity, which in turn
shapes that person’s unique approach to financial decision-making. The lack of diversity among samples used
means that such research will be fundamentally flawed.
The integration of neuroscience, psychology, and behavioural finance makes neurofinance research extremely
complex and interdisciplinary in nature. Functional MRI scans, EEGs, and neurochemical measures offer rich
data, but lack proper training and annotation can lead to erroneous interpretations and diagnostic pathways
(Poldrack, 2012). In addition, numerous studies portray differing accounts of the amygdala, prefrontal cortex,
and ventral striatum involvement in risk-taking, indicating that the processes underlying neural mechanisms
are highly context-sensitive (Knutson & Bossaerts, 2007; De Martino et al., 2006). Furthermore, just because
some change in a certain brain area is associated with some activity in a particular region during the execution
of an investment decision does not imply that this region exerts control over the decision (Ariely & Berns,
2010). The application of neuroscientific methods poses ethical questions around privacy, manipulation, and
consent due to their integration into finance. Neurofinance exploitation may aid investment firms and financial
institutions in developing neuro-marketing strategies that prey on investors' cognitive heuristics (Zak, 2007).
For example, if neural correlates of impulsive choices are detected, firms may indiscriminately market certain
investor populations with aggressive risk-taking financial products. This behaviour raises ethical and legal
concerns about finance and protectionism (Lo, 2012). In addition, acquiring neural information through fMRI
or EEG requires consent, and there is always a concern about how such sensitive information is kept,
processed, and abused (Farah, 2012).
Costs associated with neuroimaging techniques like fMRI, PET, and EEG limit the scope of neurofinance
research to small-scale studies. Each fMRI scan can cost anywhere from £500–£1,000, making it economically
unfeasible for countless institutions and researchers (Poldrack & Farah, 2015). Furthermore, neuroimaging
devices are non-portable, making it impossible to gather data in real-time during financial trading activities,
which are decision-making races that occur in milliseconds (Lo & Repin, 2002). These constraints on
technology inhibit exploration of the neural underpinnings of decision-making in high-pressure trades. Lastly,
neurofinance has not been able to develop a unified theory that consolidates its different empirical findings, as
is the case with the Efficient Market Hypothesis (Fama, 1970) or Prospect Theory (Kahneman & Tversky,
1979). Neurofinance research seems to be lacking a model that explains how the distinct contributions of
various brain regions to the perception of risk, reward expectancy, and emotion regulation is integrated
(Camerer, 2018). Also, there are no practical recommendations on how investors, policymakers, and financial
advisors can utilise the insights of neurofinance for real-world investment decision making (Loomes, 2010).
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Key Findings Of The Study
The findings of this research indicate that investor choices are made in part due to physiological internal
workings of the body. Past research in neurofinance showed that the nucleus accumbens (NAcc) region of the
brain associated with pleasure and anticipation of reward works to modulate risk-taking and impulsive
investing (Kuhnen & Knutson, 2005). NAcc activation accounts for investor preference for high-risk
investments even when losing investments is more rational. There are other brain areas that when more
activated explain the emotional response syndrome (amygdala, and insula) to losses, fear, and regret in the
form of investing loss aversion and in the panics of market decline. These studies have shown that decisions
made during investments cannot be purely rational, as there is a strange entanglement of emotions and
cognition in the brain that must be also considered.
The conceptual synthesis very much shows that neurofinance, in addition to AI and machine-learning
technologies, can greatly improve financial advisory services. Future robots that serve as financial advisors, or
robo-advisors, can use neural or physiological proxies for investment sentiment including stress, emotional
arousal, or decision delay, to predict investor emotions and alter portfolio suggestions in real time. This greatly
helps in investment behavioural coaching, risk calibration, and personalised financial advice.
Nonetheless, the study outlines crucial shortcomings. The majority of current research within neurofinance
continues to be constrained to the domains of laboratory settings, and within the context of small and
homogenous samples. In addition, the excessive expenditure on neuroimaging and the ethical issues around
data privacy, informed consent, and potential manipulation also pose challenges.
The scope of neurofinance stems from its ability to reshape our comprehension of behavioural finance, but is
impeded by a lack of method and sample size, coupled with ethical and technological constraints. There is an
urgent need to evolve research design to allow more naturalistic observation, broaden the scope of sampling,
enhance collaboration across disciplines, and ensure ethical use of neuroscientific tools in finance. Other
questions that need attention include the use of portable neuroimaging devices, the recording of neural activity
during trading, and the creation of computational models that incorporate neurofinance principles. Closing this
gap would be essential to legitimising neurofinance as a subfield of behavioural economics and financial
decision-making.
CONCLUSION
A relatively new branch of study called "neurofinances" combines technology and neuroscience knowledge to
examine how our brains function when we make financial decisions. Traditional financial theories, such as the
Efficient Market Hypothesis, are being refuted by Neurofinances, which demonstrates that traders do not
behave like perfect rational decision makers, but rather employ a variety of techniques. Integration of
neurofinance into other financial theories, like limited rationality and adaptive market hypothesis, is regarded
as a critical topic for future development (Tseng, 2017). Neurofinance, as a field of study, is novel and
attempts to connect brain processes with activities of investing. The fundamental ideas of neurofinance state
that feelings are particularly important when it comes to making financial decisions. The majority of
neurofinance research focusses on trading conduct. Studying the brain may also be beneficial for other
investing habits, such credit, retirement planning, asset management, and personal financial planning choices.
Furthermore, only event-based experiments have been used in the corpus of research in this area of
neuroscience. According to neuroscientists, they may use brain scanning methods such as fMRI to predict an
observer's emotional reaction to an investment and whether or not they would buy it. This indicates that for an
application of this study, people would need to be placed within an fMRI during a certain financial decision-
making event. These and other behavioural reasons may render live research settings impossible outside of
ethical and medical constraints. More work needs to be done on techniques appropriate for live neurofinance
studies without irrefutable ethical implications. Neurofinance is another instrument that could further explain
the concept of the financial consumer. Furthermore, more effort should be focused on explaining how
knowledge of the relevant regions of the brain dealing with financial decision-making contributes to the lack of
rational behaviour.This understanding will aid the process of financial advising, and the allocation of positions
will be optimally structured on the relevant investor paradigms related to their needs. More work is needed in
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this area in order to understand how the brain functions when it is analysing decision-making, risk
categorisation, and reward anticipation in dynamic interaction with one another
Neurofinance sheds light on the subconscious mechanisms that influence financial decisions by finding
neurological correlates of investor behaviour. This knowledge is crucial for investors, financial professionals,
and policymakers, as it improves forecast accuracy, optimizes investment strategies, and contributes to a more
complete and effective approach to navigating the intricacies of financial markets.
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