E-ISSN:2583-1747

Research Article

Algorithmic Dependence

Management Journal for Advanced Research

2026 Volume 6 Number 5 October
Publisherwww.singhpublication.com

Algorithmic Dependence in AI-Driven Investment Decisions among Indian Advisors

Gupta P1*, Khare S2, Pareta S3
DOI:10.54741/MJAR/6.5.2026.331

1* Pooja Gupta, Professor, Department of MBA, BSSS Institute of Advanced Studies, Bhopal, Madhya Pradesh, India.

2 Shreyansh Khare, Sales Executive, Canara Bank Securities Limited, Jabalpur, Madhya Pradesh, India.

3 Samkit Pareta, Sales Executive, SBI Mutual Funds, Bhopal, Madhya Pradesh, india.

India is witnessing a tremendous change in the pattern of investment behaviour due to the intervention of Artificial Intelligence in driving the advisory behaviour and ultimately affecting the decision-making among the investors/advisors. The study attempts to examine the algorithmic dependent advisory behaviour of portfolio managers/advisors in applying AI tools for investment decision-making over their personal judgement to time the market. The study is administered through structured questionnaire on 150 portfolio managers/investment advisors across Inda. The research indicates that investor advisors rely majorly on the AI-driven platforms for investment recommendations to their retail clientele. They strongly agree to follow the recommendations offered by the AI platforms. The respondents agree that their confidence is increasing in market predictions and they strongly believe that AI driven market predictions are better than their judgements on investment decisions. AI-driven algorithmic advisory indeed has developed and enhanced the performance confidence among the advisors but excessive dependence on algorithmic advisory can be alarming as it shall impact the habit of independent critical thinking. The paper attempts to contribute to the growing domain of behavioural finance in India and attempts to invite further research to investigate the rising and changing behaviours in investment and advisory due to AI interventions.

Keywords: algorithmic dependence, decision effectiveness, indian advisors, investor confidence, perceived accuracy, risk-taking behaviour

Corresponding Author How to Cite this Article To Browse
Pooja Gupta, Professor, Department of MBA, BSSS Institute of Advanced Studies, Bhopal, Madhya Pradesh, India.
Email: 		Gupta P, Khare S, Pareta S, Algorithmic Dependence in AI-Driven Investment Decisions among Indian Advisors. Manag J Adv Res. 2026;6(5):7-14.
Available From
https://mjar.singhpublication.com/index.php/ojs/article/view/331/version/331

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2026-09-04 2026-09-21 2026-10-12
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 5.15

© 2026 by Gupta P, Khare S, Pareta S and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

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Download PDFBack To Article1. Introduction2. Literature
Review3. Research
Objectives4. Research
Hypotheses5. Research
Methodology6. Data Analysis
and Results7. Findings and
Conclusion8. ConclusionReferences
Gupta P, et al. Algorithmic Dependence in AI-Driven Investment

1. Introduction

The application of Artificial Intelligence (AI) in predicting financial markets are marking a new era of transformation in investment behaviour of the retail investors in India. Since early 1970 through late 1990, the algorithmic trading systems performed rule-based, pre-programmed strategic predictions without the element of adaptive learning capabilities (Hendershott, Jones & Menkveld, 2011). The intervention of machine learning and deep learning technologies led to the development of sophisticated financial platforms that are capable of analyzing vast datasets, identifying complex and non-linear patterns, and also generate investment predictions in real time with more efficiency and ease, than earlier. In India, AI-powered platforms such as Groww, Zerodha Kite, Smallcase, PaytmMoney, and likes., have created real-time experience for its users. These AI-driven tools are popularly and confidently used, mainly by the tech-savvy generation, the millennials and first-generation investors, whose numbers have seen a historic rising, post pandemic in 2020, truly complimented by the accelerated growth of fintech adoption in India, post demonetization in 2016 (Mehrotra and Agarwal (2020)).

The advent and application of AI-platforms to time the market for investments, though has been beneficial to its users, but it also raises seriously caution on the level of user dependency on these AI-driven investment predictions, as the excessive dependency, in long run, may affect the critical thinking abilities of the users to make rational investment selection. The paper is an attempt to contribute to the growing domain of behavioural finance in India and attempts to invite further research to investigate the rising and changing behaviours in investment and advisory due to AI interventions. It also attracts the attention of policymakers to step in for regulatory measures to monitor and control the usage of AI in advisory.

2. Literature Review

1. Trust in AI plays an important role in technology adoption and reliance, thus higher trust is expected to increases the level of acceptance and reliance on AI-generated recommendations. Davis (1989) proposed the Technology Acceptance Model (TAM) and identified initially, that perceived usefulness and

ease of use are the primary reasons for accepting or rejecting the tech-based application, however, with subsequent extensions to the study it was observed that trust is a critical mediating factor specially in case where predictions are based on asymmetric information, and in uncertainties (Gefen, Karahanna & Straub, 2003). Cheng and Jiang (2021) studies that transparency and explanation ability of AI algorithms, resulted in increase in the trust levels of retail investors in Asian Markets. These findings direct emphasis the relevance of trust factor applicable to Indian investors too.

2. Perceived accuracy will tend increase subjective assessment over objective accuracy for the AI-led investment recommendations. Logg, Minson and Moore (2019) studied the investor behaviour and coined the term ‘algorithmic appreciation’ and identified that investors intend to trust AI-driven recommendations more than the human advisors. The caution was raised that the trust in AI, if tested critically under volatile or novel market conditions, may not be accurate or can be misleading. The behavioral finance theories have suggested that perceived accuracy is a strong factor that amplifies confidence and reduces critically thinking abilities of judging the underlining events. In context to Indian investors, particularly the novice investors that are high in numbers post covid, the financial literacy remains highly disproportionate across the demographic groups, thus, the perceived accuracy on AI platforms will also disproportionately influence their already weak ability and experience to evaluate algorithmic outputs (Lusardi & Mitchell, 2014).

3. In the current study the algorithmic dependency refers to the degree of investors reliance, either habitually or/and preferred, on AI-driven predictions for primary investment decision-making ruling out the need for independent analysis. Algorithmic dependency was included in the study based on the foundational work on ‘automation bias’s by Parasuraman’s (2000) referring to the tendency of the user to favor automated decision-support system over personal judgements and apply it in retail finance. Lee and See (2004) in their study argued that when users perceive automated systems as highly reliance without negligible possibility of error in prediction, this gives rise to high level of satisfaction and automation biasness, thus resulting in deteriorating performance in novel and complex situations.


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Such complacent behaviour will result in huge losses, during market anomalies.

4. Under behaviour finance, investor confidence has been widely studies to emphasizes the degree of certainty with which the investor will act based on their investment beliefs and assumptions. In the study by Barber & Odean (2001), the pattern of excessive trading, under-diversification and inferior portfolio performance was called as investor overconfidence in predicting the market trends. In reference to the AI-led investment predictions, Hirshleifer and Luo (2001) studied that the positive AI-led predictions strengthen the amplifies pre-existing overconfidence among investor and affects their risk-taking behaviors. Riskiness is inevitable in any investment and investor confidence defines the level of risk appetite of an investor, defining the ability and willingness to accept high risk of financial uncertainty for higher returns. Under the Prospect Theory propounded by Kahneman and Tversky's (1979), the risk associated behaviour towards return was studied, however, AI-led recommendations may distort the investors’ ability to examine the understated risk against to the probable positive outcomes.

5. The effectiveness of the investment decision is commonly measured based on the quality, consistency, and performance outcomes of such decisions in response to the risk-return objectives of the investor. Investment decision effectiveness is a subjective factor that relates strongly to the decision satisfaction and perceived goal alignment dimensions (Payne, Bettman & Johnson, 1993). Investment Decision Effectiveness acts as a mediating factor in encouraging confidence and dependency of the user as established under the human-computer interaction research framework which states that the outcomes of the decisions will be always be affected by the perceived utility of AI tools jointly applied with user trust.

Despite the available literature has comprehended upon the algorithmic trading, investors’ perception, and AI interventions in the field of finance, yet not much study has been conducted to examine how AI driven decision will impact the psychological and behavioral aspects of the advisors in the emerging markets, like India where unregulated AI platforms are applied and excessively relied upon for advisory in the retail markets.

The study directly addresses the research gap in context to emerging pattern of behavioral finance, technology adoption and dynamics of Indian retail investment markets)

3. Research Objectives

1. To examine how trust in AI platforms influences investment decision effectiveness among Indian advisors.
2. To assess the impact of perceived AI accuracy on investment decision outcomes.
3. To investigate how investor confidence, algorithmic dependency, and risk-taking behaviour mediate the relationship between AI platform characteristics and decision effectiveness.
4. To develop and empirically validate a conceptual model of algorithmic dependence in the Indian investment context.

4. Research Hypotheses

Based on the theoretical framework and the literature review, the following hypotheses are proposed -

H1: Trust in AI has a significant positive influence on Investment Decision Effectiveness.

H2: Perceived Accuracy of AI has a significant positive influence on Investment Decision Effectiveness.

H3: Trust in AI significantly predicts the level of Algorithmic Dependency among advisors.

H4: Perceived Accuracy significantly predicts Investor Confidence.

H5: Investor Confidence significantly mediates the AI platform characteristics to decision effectiveness relationship.

H6: Algorithmic Dependency significantly mediates the relationship between AI platform characteristics and investment decision effectiveness.

H7: Risk-Taking Behaviour significantly mediates the relationship between Investor Confidence and Investment Decision Effectiveness.

5. Research Methodology

1. Research Design
The quantitative research is conducted through structured survey, as a prominent practice of collecting data under the field of behavioral finance,


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to test the directional relationships among the constructs and examine the mediating role of AI-driven investment approach in generating desired outcomes.

2. Conceptual Framework
The study suggests the mediation model where Trust in AI (TAI) and Perceived Accuracy of AI (PAI) are two independent variables, Investor Confidence (IC), Algorithmic Dependency (AD), and Risk-Taking Behaviour (RTB) are 3 mediating variables, and Investor Decision Effectiveness (IDE) is the independent variable. The conceptual construct is drawn, based on the Technology Acceptance Model (TAM), Social Cognitive Theory, and Behavioural Finance theories

Figure 1: Conceptual Framework and Construct

mjar_331_01.PNG
Source:Jamovi Version 2.6.44

3. Sample
The sample consists of the investment advisors’/portfolio managers that are actively using AI tools and platforms or algorithmic predictions in investment decision-making. Using the combination of convenience and snowball sampling, 150 valid responses were collected through questionnaire using google form survey. The sample satisfies the minimum requirement of 10 observations per variable for regression analysis (Hair et.al., 2019).

4. Measurement Instrument
A 28 items questionnaire on a 5-point Likert scale (5=Strongly agree to 1=Strongly disagree) was developed across all the constructs based on the validated scales in the existing literature includes, Trust in AI Platform (McKnight et.al., 2002), Perceived Accuracy (Logg et.al., 2019), Investor Confidence (Graham et.al., 2009), Risk-taking Behaviour (Weber et.al., 2020), Investor Decision Effectiveness (Payne et.al., 1993),

however, the Algorithmic Dependency is newly develop for this study.

5. Statistical Analysis
For the purpose of the study, data analysis was conducted through SPSS v26 using descriptive statistics. Cronbach's Alpha reliability analysis, Pearson correlation analysis, multiple regression analysis, and Baron and Kenny's mediation framework with Sobel tests (1986) for indirect effects

6. Data Analysis and Results

1. Demographic Details
The Table 1. represents that the respondents were predominantly 61.3% males. The 42.7% of the respondents were largely in the age bracket of 26-35 years indicating a strong profile of retail advisors’ that are active in Indian digital era. Approximately tw0-thrid of the respondents has investment experience lying between 1-5 years. The group reported to have higher frequency (46.05%) of weekly usage of AI platform, suggesting that AI tools are frequently relied upon as a consultative tool among the respondents.

Table 1: Demographic Profile of Respondents (N = 150)

Demographic VariableCategoryFrequency (%)
GenderMale61.3% (92)
Female38.7% (58)
Age Group18-25 years34.0% (51)
26-35 years42.7% (64)
36-45 years16.0% (24)
46+ years7.3% (11)
Investment ExperienceLess than 1 year18.7% (28)
1-3 years37.3% (56)
3-5 years28.7% (43)
5+ years15.3% (23)
AI Platform UsageDaily27.3% (41)
Weekly46.0% (69)
Monthly19.3% (29)
Rarely7.4% (11)

Source: Author’s Own Calculations

2. Reliability Test
As indicated in Table 2, the Cronbach’s Alpha for the all the six constructs showed a strong internal consistency ranging between 0.808 to 0.862 which is above the 0.70 benchmark, stating the soundness of the measurement instrument.


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Table 2: Reliability Statistics — Cronbach's Alpha

ConstructNo. of ItemsCronbach's Alpha
Trust in AI Platform50.847
Perceived Accuracy of AI50.831
Investor Confidence40.819
Algorithmic Dependency50.856
Risk-Taking Behaviour40.808
Investor Decision Effectiveness50.862

3. Descriptive Statistics
As per Table 3. all the constructs have scored above the midpoint of 3.0, thus, indicating the respondents have shown high trust in AI platforms, due to its perceived accuracy in prediction, thus encourages investor confidence and decision effectiveness. Where TAI is records the highest mean 3.82, RTB has lowest mean of 3.58 — though above midpoint, yet suggesting that even though advisors frequently use AI platforms with confidence yet few exercise caution towards associated risk.

Table 3: Descriptive Statistics of Study Variables

ConstructMeanStd. Dev.MinMax
Trust in AI Platform (TAI)3.820.741.605.00
Perceived Accuracy of AI (PAI)3.760.711.805.00
Investor Confidence (IC)3.650.781.505.00
Algorithmic Dependency (AD)3.710.811.405.00
Risk-Taking Behaviour (RTB)3.580.831.255.00
Investment Decision Effectiveness (IDE)3.790.761.605.00

4. Correlation Analysis
As shown in Table 4, the matrix is indicating a statistically significant moderate-to-strong positive relationship among the constructs, with highest correlation between Algorithmic Dependency and Investor Decision Effectiveness (r=0.634). This also suggests a looped behaviour of rising confidence in AI-platform usage resulting in relying on AI-driven predictions and performance, thus further boosting confidence for AI dependency. The study indicates stakeholders to pay close attention this behavioral dynamic among the advisors’/portfolio managers

Table 4: Pearson Correlation Matrix

VariableTrust in AIPerceived AccuracyInvestor ConfidenceAlgo. Depend.Decision Effect.
Trust in AI (TAI)1.0000.612**0.543**0.487**0.561**
Perceived Accuracy—1.0000.578**0.521**0.593**
Investor Confidence——1.0000.634**0.617**
Algorithmic Dependency (AD)———1.0000.582**
Investment Decision Effectiveness (IDE)————1.000

5. Multiple Regression Result
As shown in Table 5, the result of regression model is highly significant (F (5,144) = 45.32, p<0.001) and presents 61.2% variance in IDE (R2=0.612, adjusted R2=0.598) with TAI (b = 0.312) as the single strongest predictor, followed by PAI (b = 0.287), IC (b = 0.241), AD (b = 0.198), and RTB (b = 0.176). Every factor is statistically significant.

Table 5: Multiple Regression Results (Dependent Variable: Investor Decision Effectiveness (IDE))

Predictor VariableBeta (b)Std. Errort-valuep-value
Trust in AI Platform (TAI) (IV1)0.3120.0684.59< 0.001
Perceived Accuracy of AI (PAI) (IV2)0.2870.0714.04< 0.001
Investor Confidence (IC) (M1)0.2410.0633.83< 0.001
Algorithmic Dependency (AD) (M2)0.1980.0593.36< 0.001
Risk-Taking Behaviour (RTB) (M3)0.1760.0612.89< 0.005

6. Mediation Analysis
The mediation analysis (Baron & Kenny framework with Sobel tests) confirms the relationship between AI-enabled platforms and Investor Decision Effectiveness are substantiated through psychological and behavioral patterns:

1. IC partially mediates, both, the TAI-IC-IDE path (indirect effect = 0.142, z = 3.89, p < 0.001), and the PAI-IC-IDE path (indirect effect = 0.128, z = 3.54, p < 0.001).
2. AD also partially mediates, both, TAI-AD-IDE path (indirect effects = 0.119, z = between 2.58 and 3.29, p < 0.01) and PAI-AD-IDE path (indirect effects = 0.107, p < 0.01).
3. RTB has partial mediation, with both TAI-RTB-IDE and PAI-RTB-IDE pathways, with lesser indirect effects (0.094 and 0.081, p < 0.05), signifying a crucial factor but with distal role in the overall decision effectiveness.

7. Hypothesis Testing
Out of the 7 hypotheses, hypotheses H1 – H6 accepted, however, H7 is partially accepted indicating a distant and indirect mediating effect on Investor Decision Effectiveness in comparison to other mediating variables, like Investor Confidence and Algorithmic Dependency, in the study.


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Table 6: Hypothesis Testing Results

HypothesisStatementKey ResultStatus
H1Trust in AI has a significant positive influence on Investment Decision Effectivenessb=0.312, p<0.001Supported
H2Perceived Accuracy of AI has a significant positive influence on Investment Decision Effectivenessb=0.287, p<0.001Supported
H3Trust in AI significantly predicts the level of Algorithmic Dependency among advisorsb=0.298, p<0.001Supported
H4Perceived Accuracy significantly predicts Investor Confidenceb=0.274, p<0.001Supported
H5Investor Confidence significantly mediates the AI platform characteristics to decision effectiveness relationshipIndirect: 0.142Supported
H6Algorithmic Dependency significantly mediates the relationship between AI platform characteristics and investment decision effectivenessIndirect: 0.119Supported
H7Risk-Taking Behaviour significantly mediates the relationship between Investor Confidence and Investment Decision EffectivenessIndirect: 0.094Partially Supported

7. Findings and Conclusion

a. The investor confidence and algorithmic dependency have indicated highest correlation (r = 0.634), as per the study, thus suggesting a looped behaviour among the advisors’/portfolio managers in frequently using AI-enabled platforms in investment decision-making, hence alarming certain regulatory attention.

b. Trust in AI (TAI) came out as the most significant predictor of the investment decision effectiveness (b = 0.312, p < 0.001), reinforcing the pattern of psychological attachment towards AI-driven platforms among Indian advisors’/portfolio managers, which also indicates that trust is the driving force that allows advisors’/portfolio managers to willingly stay engaged with AI-led platforms, act with strong conviction to the AI-driven recommendation and feel intense sense of satisfaction with their investment decisions based on these recommendations.

c. Perceived Accuracy of AI (PAI) based recommendations indicates a significant positive impact on Investor Decision Effectiveness (IDE) (b = 0.287, p < 0.001),

which suggests that accuracy in recommendations by AI-led platforms drive the sense of confidence and belief in algorithmic prediction-based investment decisions, thus shaping the psychological state and decision-based performance of investment advisors’/portfolio managers.

d. The study also draw a caution while indicating that, Trust in AI, on side, enhances confidence and decision-making skills but on the other side, it also is accelerating algorithmic dependency (r = 0.487), which in the long-run may result in deteriorating the critical thinking and rational judgement abilities of the advisors’/portfolio managers in timing the market and evaluative predictions. Some regulatory action is needed to monitor and control the design and use of AI-platforms.

e. Among the most important mediating factor, Investor Confidence (IC) established a significant relationship with Trust in AI (TAI) and Perceived Accuracy in AI (PAI) paths (indirect effects = 0.142 and 0.128 for TAI and PAI respectively), in demonstrating high Investor Decision Effectiveness, thus reinforcing self-assurance satisfaction and psychological certainty. The study indicates that AI-led prediction accuracy leads to higher confidence which in turn produces better outcomes. In addition, Algorithmic Dependency (AD) as the second mediating factor (indirect effects = 0.119 and 0.107, both p < 0.01) between TAI and PAI respectively, confirms the habitual usage of AI predictions for investment decision-making and its related performance Such prediction-based outcomes without any personal judgement and critical evaluation will further enhance the sense of intuition among advisors’/portfolio managers. However, perceived accuracy and actual accuracy may not be same always, especially in novel market conditions.

f. Risk-Taking Behaviour partially mediates the AI-driven platform characteristics to the investment decision effectiveness relationship (indirect effects = 0.094 and 0.081, p < 0.05), thus suggests that even though there is no direct and strong relationship between Risk-Taking Behaviour and using of AI predictions for Investor Decision Effectiveness, however, the reliance on AI predictions is reducing their fear of unknown which is boosting the over-confidence, thus tempering their risk appetite, which may prove unreasonable, in the long run or during dynamic market conditions.


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g. The demographic data of the sample clearly indicates that young advisors’/portfolio managers with sound experience in the financial markets are driven to AI-led platforms to validate the market events and AI generated predictions to offer advisory. Although the frequency of using AI is currently limited to weekly usage than daily, which indicates that although the complete reliability on AI-driven platforms is not yet witnessed, but the limited usage is also influencing the behaviour and investment patterns among the respondents, as reflected by regression analysis in the study.

8. Conclusion

The study is conclusive evidence that AI-driven tools and their increasing application by the Indian advisors’/portfolio managers is a bubble of caution which is rising on the pretext of increasing performance confidence. On one hand the positive aspect of the AI-drive platforms is represented with the enhancing confidence and decision-making capabilities of the advisors’/portfolio managers. In the growing trend of the application of AI tools in financial predictions are preferred by the advisors’/portfolio managers because of the trust in the recommendation accuracy as predicted by these tools, thus resulting in growing confidence of the advisors’/portfolio managers in timing the market for advisory. However, on the other hand, the cautionary part is the growing dependence on the application of AI tools in financial decision-making which, in future, may result in deterioration of one’s own capabilities of market analysis and critical thinking abilities to make rational investment decision which is crucial for effective advisory practices. Such AI dependence may not show impact during stable market conditions, however, the same may not be true under the volatile markets. The study tends to attract SEBI and other regulatory bodies towards the requirements for guidelines on AI usage transparencies, risk disclosures standards for AI-driven investment predictions, need of regular upgradation of advisors to check their capabilities to read and react to market conditions, conduct awareness programmes for the investor education to update them with the nuances of AI tools and AI-driven financial advisory. The study lays the base for examining how the dependency on AI is evolving in investment advisory domain by comparing the objective-based portfolio performance using subjective psychological trade-offs.

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