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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue III, March 2026

Role of Investor Perception Towards AI-Driven Stock Market in

Banglore City

Abhishek S Shirahatti

, Bhargava K

, Kartikey Koti

1,2

Final year MBA Student at Sapthagiri NPS University, Bangalore

Professor, Sapthagiri NPS University, Bangalore

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150300032

Received: 18 March 2026; Accepted: 25 March 2026; Published: 04 April 2026

ABSTRACT

This study aims to evaluate Bangalore investors' perceptions of AI-powered stock market platforms. The study's

primary objectives are to ascertain respondents' familiarity with AI-based stock trading platforms, identify the

factors influencing their adoption, and assess perceived risks associated with these technologies. It examines

how investors' opinions of AI platforms connect to their past experiences with AI-based financial instruments in

addition to contrasting trust, efficiency, and decision-making accuracy between AI systems and traditional

human financial advisors. The study also examines security and privacy issues as well as consumer preferences

when utilizing AI-powered trading platforms. The study is based on primary data collected from 100 respondents

in Bangalore, 73 of whom were men and 27 of whom were women.

The findings indicate an increasing awareness of technological advancements in the stock market, since a

significant portion of respondents are either somewhat or very familiar with AI-based trading platforms.

Accuracy and transparency were shown to be the most significant factors influencing trust in AI-driven

platforms. AI-based systems may be helpful in reducing human prejudice, as most respondents acknowledged

that emotional bias affects stock market decisions. Despite the fact that many investors consider AI trading to

be moderately to extremely risky, the majority of respondents preferred a hybrid approach that combines both

AI systems and human experience, and half of the respondents said they would be willing to rely on AI over

traditional financial advisors.

Keyword: Artificial Intelligence in Stock Trading, Investor Perception, AI-Driven Trading Platforms, Risk

Perception in AI Finance, Human vs AI Financial Advisory.

INTRODUCTION

Artificial intelligence (AI) has significantly altered how people trade and make investment decisions in stock

markets across the globe. AI enables automated trade execution, risk assessment, and real-time forecasting,

which accounts for more than 60% of stock market transactions in many developed countries. This shift is mostly

supported by big data analytics and machine learning algorithms, which improve forecast accuracy and lessen

human emotional bias in financial decisions. However, investor perception continues to play a major role in the

extent to which AI technologies are adopted in financial markets.

A positive perception that is influenced by factors like accuracy, dependability, and transparency encourages

investors to employ AI-based trading platforms. However, concerns about moral quandaries, data privacy, and

system reliability sometimes lead to mistrust and hinder the adoption of these technologies. Global research

shows that trust in AI-based financial systems varies by geographic location and demographic group.

AI-based stock market operations are widely used in major financial markets like the US, China, Germany, the

UK, and France, where complex algorithms and machine learning systems enable automated trading and market

analysis. These tools improve trade efficiency, reduce human bias in investment decision-making, and predict

market trends for financial institutions and investors.

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In India, the application of AI in financial markets is expanding rapidly. Major financial exchanges like the

Bombay financial Exchange and the National Stock Exchange of India are using AI technologies for tasks

including risk assessment, stock price prediction, and fraud detection. Fintech companies like Groww and

Zerodha also employ machine learning to give investors customized trading strategies and insights. Investors'

opinions on AI, however, differ widely. Younger, tech-savvy investors often appreciate the efficiency and speed

of AI solutions, while more traditional investors still choose human brokers for assurance and personalized

guidance. According to NASSCOM (2024), nearly half of Indian investors believe AI is technologically

advanced but only moderately dependable.

At the heart of this technological revolution lies Bangalore, which is recognized as India's center of technology.

The city's high concentration of IT professionals and fintech companies provides an environment that promotes

the application of AI in financial operations. Investors in Bangalore frequently employ algorithmic tools to

evaluate market trends and support their investing decisions. However, there is still disagreement. Tech-savvy

individuals generally trust AI because of its dependability and analytical capabilities, but others are concerned

about algorithmic bias and the absence of human judgment and emotional intelligence.

Therefore, an understanding of investor psychology is necessary when examining the application of AI in

financial markets. Cognitive perception refers to an investor's knowledge and technical understanding of AI

systems, whereas affective perception relates to an investor's level of trust and emotional response to these

technologies. Both aspects have an impact on investors' behavioral intention to adopt AI-enabled trading tools.

For AI-driven financial markets to evolve sustainably and for investors to feel safe, informed, and protected

when using such systems, technology developers and regulatory agencies must provide transparency, reliability,

and data security.

LITERATURE REVIEW

1. Zhang 2025 examined the impact of fintech innovation on investor behavior using behavioral finance

concepts. The study found that fintech platforms improve access to financial information and investment

opportunities. However, it also noted that these technologies may increase behavioral biases among

investors.

2. Shukla and Umashankar 2025 analyzed the role of advanced technologies in automated trading systems

using a quantitative survey approach. The findings showed that investors trust AI-based trading systems

more when they are transparent and reliable. The authors recommend improving algorithm transparency

to increase investor confidence.

3. Ravichandran and Afjal 2025 investigated the impact of investor attention on AI-based stocks using

econometric analysis. The study found that higher investor attention leads to increased trading activity

and stock price volatility. It suggests that investors should rely more on fundamental analysis rather than

market hype.

4. Hansen and Lee 2025 explored the financial stability implications of generative AI in financial markets

through theoretical analysis. The findings indicated that AI can enhance financial decision-making but

may also increase speculative behavior and systemic risk. The study recommends stronger regulatory

frameworks for responsible AI adoption.

5. Martínez and López 2025 conducted a systematic review on the role of AI in investment funds. The

findings showed that AI is widely used for portfolio optimization, risk management, and market

forecasting. The authors suggest further research on the long-term effectiveness of AI-managed funds.

6. Srivastava and Sikroria 2024 studied the effectiveness of AI and algorithmic trading in predicting stock

market movements using quantitative analysis. The results showed that AI models improve prediction

accuracy and market efficiency compared to traditional models. The authors recommend integrating

machine learning with financial expertise.

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7. Shobayo et al. 2024 analysed stock price prediction using sentiment analysis techniques such as Fin

BERT and GPT-4. The findings revealed that AI models improve prediction accuracy by capturing

investor sentiment from financial news. The study suggests incorporating NLP tools for better financial

forecasting.

8. Ferreira and Silva 2024 conducted a meta-review of studies related to AI-based stock market prediction.

The findings showed that deep learning and hybrid AI models outperform traditional statistical methods.

The study recommends focusing on real-time AI implementation and risk management.

9. Romanko et al. 2023 examined the use of ChatGPT-based models in investment portfolio selection

through experimental simulations. The findings showed that AI can support portfolio diversification and

investment decision-making. However, the study emphasized the need for human oversight.

10. Fuster et al. 2022 analysed the role of machine learning in credit markets using empirical lending data.

The results indicated that AI improves the accuracy of credit risk assessment. However, it may also create

inequalities in access to credit.

11. Bhatia and Bhatia 2021 studied the applications and challenges of AI in financial markets through

conceptual analysis. The findings showed that AI improves trading efficiency, forecasting, and fraud

detection. The study highlights the need for proper regulation and ethical governance.

12. Goodell et al. 2021 examined the role of AI and fintech in transforming financial markets through a

literature review. The findings showed that AI improves financial forecasting, portfolio management,

and risk analysis. The authors suggest focusing on regulatory and ethical challenges in future research.

13. Kshetri 2020 examined the opportunities and challenges of AI adoption in financial services. The study

found that AI enhances automation and operational efficiency in financial institutions. However,

concerns about privacy, cybersecurity, and bias remain significant.

14. Lussange et al. 2019 investigated the impact of trader psychology using multi-agent AI simulations. The

findings showed that behavioural biases such as herd behaviour and overconfidence contribute to market

volatility. The study suggests integrating behavioural finance concepts into AI trading models.

15. Jagtiani and Lemieux 2019 analysed the role of alternative data and machine learning in fintech lending.

The findings revealed that alternative data improves credit risk evaluation and expands financial access.

The authors emphasize responsible data usage and regulatory oversight.

16. Lee and Shin 2018 explored the fintech ecosystem and its influence on financial services. The study

found that fintech innovations improve efficiency, customer experience, and financial inclusion. The

authors suggest collaboration between traditional banks and fintech firms.

17. Chishti and Barberis 2018 examined the development and impact of fintech on global financial services.

The study showed that fintech innovations are transforming payments, lending, and investment

management. The authors recommend supportive regulatory frameworks to encourage innovation.

18. Lo 2017 introduced the Adaptive Markets Hypothesis combining behavioural finance and market

efficiency theories. The study suggests that financial markets evolve as investors adapt to changing

environments. It recommends flexible investment strategies based on market conditions.

19. Brynjolfsson and McAfee 2017 examined how digital technologies and AI influence economic systems.

The findings showed that AI increases productivity and efficiency. However, it may also disrupt

traditional industries and employment.

20. Gomber et al. 2017 reviewed the development of digital finance and fintech research. The findings

indicated that fintech improves financial efficiency, transparency, and market competition. The study

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suggests further research on financial inclusion and regulatory technology.

21. Dhar 2016 explored the potential of AI in financial services. The study found that AI improves trading,

risk management, and fraud detection. It recommends that financial institutions invest in AI infrastructure

and expertise.

22. Arner et al. 2016 studied the evolution of fintech after the global financial crisis. The findings showed

that technological innovation and regulatory reforms accelerated fintech growth. The authors recommend

balanced regulations to support innovation and financial stability.

23. Biais et al. 2015 analysed the impact of high-(%) trading on financial markets. The study found that

algorithmic trading improves market liquidity and efficiency. However, stronger regulatory monitoring

is necessary to avoid unfair advantages.

24. Baker and Ricciardi 2014 examined psychological factors affecting investor behavior. The findings

showed that emotions and biases such as overconfidence and loss aversion influence investment

decisions. The authors recommend structured financial planning to reduce biases.

25. Chen et al. 2012 explored the role of business intelligence and big data analytics in decision-making. The

study found that data analytics enables organizations to extract valuable insights from large datasets. It

recommends investment in advanced analytics systems.

26. Barberis and Thaler 2003 reviewed behavioural finance theories and investor psychology. The findings

revealed that investors often deviate from rational decision-making due to cognitive biases. The study

suggests incorporating behavioural insights into financial models.

27. Rogers 2003 introduced the Diffusion of Innovations theory explaining how new technologies spread.

The study found that factors such as relative advantage, compatibility, and social influence influence

technology adoption. It recommends effective communication strategies for faster adoption.

28. Venkatesh et al. 2003 developed the Unified Theory of Acceptance and Use of Technology (UTAUT).

The findings showed that performance expectancy, effort expectancy, and social influence influence

technology adoption. The study suggests designing user-friendly technologies.

29. Davis 1989 proposed the Technology Acceptance Model (TAM) to explain user acceptance of new

technologies. The study found that perceived usefulness and ease of use determine technology adoption.

The model is widely used to study user behavior toward digital systems.

30. Kahneman and Tversky 1979 introduced Prospect Theory to explain decision-making under risk. The

findings showed that individuals tend to avoid risk when experiencing gains but seek risk when facing

losses. The theory highlights the importance of psychological biases in financial decisions.

Research Gap

1. The majority of current research focuses on the technical effectiveness and predictive capacity of AI and

algorithmic trading systems, with little attention paid to investor perception, trust, and behavioural

responses to AI-driven stock trading platforms, especially in emerging markets.

2. There is a dearth of empirical research comparing investor trust, decision-making accuracy, and

preference between AI-driven trading algorithms and conventional human financial advisors, despite the

fact that numerous studies address fintech adoption and AI applications in finance.

3. The majority of prior research has been done in international or developed market contexts;

comparatively few studies have examined investor perception at the city level in India, especially in tech

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hubs like Bangalore, where the use of AI in financial markets is expanding quickly but investor concerns

about risk, privacy, and data security are still largely unexplored.

Objective of the study

1. To examine respondents’ familiarity, influencing factors and perception of risk in using Ai based

stock platform.

2. To identify the relationship between AI based stock platforms and traditional human financial

advisors by respondents.

3. To study the preference, perception and adoption of AI based stock trading platform by respondents.

Hypotheses

1. There is a significant relationship between respondents’ familiarity with AI-based stock trading

platforms and their adoption of such platforms.

2. Factors such as perceived usefulness, ease of use, and technological awareness significantly

influence respondents’ use of AI-based stock trading platforms.

3. There is a significant relationship between respondents’ trust in AI-based stock platforms and their

preference for AI-driven investment decisions.

4. To examine the respondent’s opinion towards risk, privacy and data security.

5. Respondents who trust AI-based stock trading platforms are less likely to rely on traditional human

financial advisors.

6. Positive perception of AI-based stock trading platforms significantly increases respondents’

intention to adopt AI-driven stock trading.

Limitation

1. The sample is limited to only 100 respondent more respondents many help in understanding their

opinion.

2. The study is limited geographically to Bangalore, a location focused on technology. Due to

differences in financial literacy and technical knowledge, investor perceptions in different cities or

rural areas may be different.

3. Instead of real trading performance or financial results of AI-driven stock market platforms, the

research primarily focuses on perception and behavioural elements.

RESEARCH METHODOLOGY

Primary Data: The research is founded on original data. We have currently collected 100 responses and

completed values in an easy-to-understand way by extracting percentages. The variables that were taken into

account are based on opinions, tastes, and demographics about AI-based stock platforms. A methodical,

organized questionnaire was developed and given to the respondents at random in order to collect this data. This

is the main pilot study to determine the difficulties in order to address them and carry out additional research.

Secondary Data: Usually, a variety of sources are used to compile this data, including corporate reports,

government records, journals, and documents. In order to identify the study subjects and gaps, we have also

employed journal publications, which will provide our investigation a strong foundation.

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ANALYSIS AND DISCUSSION

PART A

1. 80% belong to the 21–30 age group, while 11% are below 20 and very few are above 30. This indicates

the study is dominated by young individuals, mainly reflecting the views of early- career investors.

2. 73% were male, while female make up 27% of the sample. This shows higher male participation and

suggests comparatively lower representation of female perspectives.

3. A majority of respondents are working professionals 56%, followed by students 38%. This indicates a

mix of practical experience and emerging interest in AI trading among young learners.

4. Most respondents 63% fall within the ₹50,001–₹1,00,000 income group, followed by 26% in the

₹25,000–₹50,000 range. This suggests that the majority belong to the middle-income category with

moderate investment capacity.

5. Beginners form the largest group at 56%, followed by 27% intermediate and 14% experts. This shows

that most respondents are new to investing and may rely more on AI tools for decision-making.

6. About 49% of respondents have used AI-based trading platforms, while 51% have not. This reflects that

adoption is still developing but shows strong potential for future growth.

PART B

Table 1 Familiarity of Ai Driven Stock Market Trading

Options

Percentage

Very familiar

32%

Somewhat familiar

27%

Heard of it but not sure

22%

Not familiar at all

19%

Total

100%

Source Primary source

Young male professionals with modest incomes are more likely to be exposed to AI-based investing

technologies, according to a survey of 100 respondents, of whom 32% were extremely familiar, 27% were male,

5% were female, and the respondents made between Rs 50,001 and Rs 1,00,000.

Table 2 Factor Influencing the Trust in Ai Driven Stock Market Platform

Options

Percentage

Accuracy

28%

Transparency

29%

Past performance

17%

Recommendations

Ease of use

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Data security

Total

100%

Source Primary source

Male respondents are most influenced by accuracy 24%, whereas female respondents are most influenced by

transparency 6%.

These comments, which are primarily from young male respondents between the ages of 21 and 30 who are

employed professionals with modest monthly incomes, indicate that user-friendly systems and peer pressure are

important factors in fostering trust in AI platforms.

Table 3 Emotional Bias in Stock Market Decisions

Options

Percentage

Yes

58%

17%

Not sure

25%

Total

100%

Source Primary source

While 25% are undecided, the majority of respondents 58% think AI lessens emotional prejudice. Respondents

in the 21–30 age range, men, working professionals, and those with incomes between ₹50,001 and ₹1,00,000

are more likely to hold this opinion, suggesting that this group favours AI for making wise investment choices.

Table 4 Risk Perception Towards Ai Stock Market Investment

Options

Percentage

Very high risk

20%

High risk

39%

Low risk

36%

Very low risk

Total

100%

Source Primary source

AI trading is viewed as low risk by the largest percentage 39%, followed by extremely low risk by 30%.

The majority of these responses are from young male working professionals making between ₹50,001 and

₹1,00,000, indicating that they have a reasonable level of confidence about the security of AI-driven trading

platforms.

Table 5 Ai Over Tradition Human Financial Advisors

Options

Percentage

Yes

50%

22%

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Maybe

28%

Total

100%

Source Primary source

While 28% of respondents think AI can beat human financial advisors, 50% of respondents are unsure.

Despite investors' recognition of AI's potential, these replies are primarily linked to young male professionals

making between ₹50,001 and ₹1,00,000.

Table 6 Experience Towards Using Ai Tools

Options

Percentage

Major role

32%

Moderate role

36%

Minor role

26%

No role

Total

100%

Source Primary source

36% of respondents think prior AI experience has little bearing on perception, while 32% think it has no bearing

at all.

Young respondents between the ages of 21 and 30, including students and working professionals with moderate

incomes, are mostly responsible for these responses.

Table 7 Comparison of AI Driven Trading VS Manual Trading

Options

Percentage

Yes

51%

21%

Maybe

28%

Total

100%

Source Primary source

While 28% of respondents think AI-driven trading increases returns, over 51% are unsure.

These opinions are largely expressed by young male respondents who are working professionals and fall under

the ₹50,001–₹1,00,000 income category, indicating cautious optimism regarding AI-based trading profitability.

Table 8 Hybrid Approach of Trading Over AI Driven System

Options

Percentage

Yes

66%

14%

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Maybe

20%

Total

100%

Source Primary source

While 20% are unsure, the majority of respondents 66% prefer a mixed strategy that combines AI and human

oversight. This respondent's approach is accurate for those with moderate income levels.

Table 9 Adoption of AI Driven Trading in the Future

Options

Percentage

30%

22%

31%

Total

100%

Source Primary source

31% of respondents highly support the use of AI trading in the future, while 30% are neutral. These responses,

which show a moderate to high desire to use AI-driven trading technology, come primarily from young

respondents between the ages of 21 and 30, male participants, working professionals, and people making

between ₹50,001 and ₹1,00,000.

Table 10 Social Influence on Using AI Driven Stock Trading

Options

Percentage

13%

11%

34%

25%

17%

Total

100%

Source Primary source

Moderate scores were given to social influence, with 34% choosing level 3 and 25% choosing level 4. According

to the analysis, the respondents fall between students and working professionals who make between Rs25,000

and Rs50,000 annually.

Table 11 Market and Media Influence on AI Stock Market

Options

Percentage

11%

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36%

22%

24%

Total

100%

Source Primary source

36% of respondents rated marketing and media influence as level 3, while 24% rated it as level 5. These replies

are primarily observed among young male professionals making between ₹25,000 and ₹50,000, suggesting that

attitudes are somewhat influenced by digital marketing and financial media.

Table 12 Customer Perception Towards AI in Stock Trading

Options

Percentage

13%

31%

30%

17%

Total

100%

Source Primary source

Moderate influence ratings were given to customer impression aspects, with 31% choosing level 3 and 30% ch

oosing level 4. is suggests that the intriguing aspect is trust and curiosity among younger and moderate people

whose ages lie between 21 and 30.

Table 13 Ai Algorithms Vs Human Experts

Options

Percentage

11%

33%

21%

26%

Total

100%

Source Primary source

Customer impression aspects received evaluations of moderate influence, with 31% selecting level 3 and 30%

selecting level 4.

The largest response (33%) indicates moderate faith in AI systems, whilst 26% suggest considerable trust. These

comments are most common among male participants, working professionals, and those in the 21–30 age group.

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Table 14 Data Privacy and Security in Using Ai-Based Trading Platforms

Options

Percentage

14%

12%

24%

22%

28%

Total

100%

Source Primary source

Customer impression aspects received moderate influence ratings, with 31% selecting level 3 and 30% selecting

level 4. Data security and privacy were deemed very critical by 28% of respondents, while they were deemed

pretty important by 24%. proving that data security is a serious issue when using AI-based trading platforms.

Young men between the ages of 21 and 30 who work as professionals and have moderate incomes make up the

majority of the respondents. They feel comfortable using AI-based trading platforms.

Table 15 Confidence On Predict Market Trends Tools

Options

Percentage

23%

36%

32%

Total

100%

Source Primary source

31% of respondents selected level 3 and 30% selected level 4 when it came to the moderate influence ratings for

customer impression characteristics. 32% of respondents are extremely confidence in AI's ability to predict

market trends, while 36% are only somewhat confident. Young male respondents between the ages of 21 and

30, with a history in working professions and intermediate investment expertise, are the main demographic

affected by this.

RECOMMENDATION

1) 22% of respondents reported that they had heard of AI trading but were not sure about it, indicating limited

awareness. Therefore, financial institutions and trading platforms should conduct training programs,

webinars, and awareness campaigns to improve investor knowledge about AI-based stock trading systems.

2) The survey shows that female respondents represent only 27% of the total sample, which is significantly

lower than male participation. This suggests the need for financial literacy programs and investment

awareness initiatives targeted toward women investors.

3) Only 28% of respondents considered transparency as a key factor influencing trust in AI platforms, which

is relatively lower compared to other factors. AI trading companies should therefore provide clear

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explanations about algorithms, decision-making processes, and data usage to strengthen investor

confidence.

4) Around 19% of respondents perceive AI trading as very risky, showing concerns among investors about the

safety of these technologies. Platforms should introduce risk education, demo trading features, and better

investor guidance to reduce fear and uncertainty.

5) Only 24% of respondents expressed strong concern about data privacy and security, indicating limited

awareness of potential risks. AI trading platforms should enhance cybersecurity systems and educate

investors about the importance of protecting financial data while using digital trading tools.

CONCLUSION

The study looked at investor sentiments toward AI-driven stock market trading in Bangalore based on responses

from 100 participants. The findings show that while a large number of respondents are aware of AI-based trading,

a smaller fraction (19%) claimed to be totally ignorant about AI-driven stock market trading, suggesting that

some investors' awareness is still low. Among the trust criteria, data security had the lowest response rate (8%),

suggesting that although investors value security, fewer respondents identified it as the main element influencing

trust than accuracy or transparency.

Just 17% of respondents claimed that emotional bias had no effect on stock market decisions, indicating that

most investors are aware of the existence of emotional effect in trading. The fact that very low risk 5% had the

fewest replies in the perception of risk indicates that most investors still think that AI-driven trading involves

some level of risk. Similarly, only 6% of respondents claimed that prior AI experience had no bearing on their

opinion of AI technologies.

Overall, the results suggest that investors in Bangalore accept AI-driven trading systems to a moderate extent,

with many preferring a hybrid approach that blends AI technology with human expertise. Nonetheless, perceived

risk, ignorance, and problems with comprehension and trust continue to shape investor sentiments. Therefore,

increasing awareness, improving transparency, and ensuring reliable AI systems can help increase investor

confidence and encourage wider usage of AI-driven stock market platforms in the future.

Scope For Further Research

1. To gain a more comprehensive picture of investor attitudes toward AI-driven stock trading, future research

can be carried out with a bigger sample size across various Indian cities or states.

2. To identify differences in AI adoption behaviour, researchers might investigate comparative studies between

several demographic groups, including age, income level, occupation, and investing experience.

3. The actual performance and efficacy of AI-driven trading systems in comparison to human advisors in terms

of investment returns, risk management, and judgment correctness may be the subject of future research.

4. Future research can potentially examine new issues pertaining to the application of AI in financial markets,

including data privacy, cybersecurity threats, regulations, and ethical ramifications.

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