Investor knowledge refers to the awareness and understanding that individuals possess about different financial products, markets, and investment mechanisms. It includes both basic financial literacy (such as understanding interest rates, inflation, diversification, and risk-return trade-offs) and more technical knowledge related to specific investment avenues like mutual funds, equities, bonds, or insurance. For mutual funds in particular, investor knowledge encompasses familiarity with Net Asset Value (NAV), expense ratios, fund categories, systematic investment plans (SIPs), taxation rules, and the role of fund managers. This knowledge acts as a foundation for making rational and informed decisions, reducing dependency on hearsay, marketing promotions, or solely relying on intermediaries.

A well-informed investor is more capable of evaluating risks, comparing alternatives, and aligning investment products with personal financial goals. Investor knowledge not only enhances confidence in decision-making but also minimizes errors such as misallocation of funds, over-reliance on short-term returns, or ignoring cost structures. In addition, higher knowledge leads to greater satisfaction, as decisions are based on awareness and realistic expectations rather than assumptions. In the context of mutual funds, strong investor knowledge helps individuals select schemes that match their risk appetite, financial horizon, and return objectives, ultimately contributing to financial security and long-term wealth creation.

Technical Knowledge:

Technical investor knowledge refers to the deeper, more specialized understanding of financial products and markets that goes beyond basic financial literacy. For mutual fund investors, this includes knowledge of how Net Asset Value (NAV) is calculated, the impact of expense ratios, entry and exit loads, and the role of fund objectives, portfolio composition, and asset allocation in shaping returns. It also involves comprehension of systematic investment plans (SIPs), systematic withdrawal plans (SWPs), taxation rules, and risk classifications across equity, debt, hybrid, and index funds. Technical knowledge enables investors to critically evaluate performance benchmarks, understand market indicators, and assess how macroeconomic factors affect their investments.

This level of knowledge is essential because it helps investors move away from making decisions based on perceptions, advertisements, or herd behavior, and instead adopt a more analytical and rational approach. Technical knowledge allows them to compare funds effectively, identify hidden costs, evaluate consistency of returns against benchmarks, and align investments with long-term financial goals. As a result, investors with strong technical knowledge are more likely to experience higher satisfaction, since their decisions are rooted in accurate assessment and realistic expectations. In today’s increasingly complex financial market, technical investor knowledge is a critical determinant of both investment performance and the confidence investors feel in their financial decision-making. Moreover, technical investor knowledge is not static; it evolves with exposure to financial markets, experience, and access to updated information. Investors who actively track market trends, regulatory changes by bodies like SEBI, or global economic developments are better positioned to apply their technical knowledge in real-time decision-making. This adaptability ensures that investors can respond proactively to risks, rebalance portfolios when required, and capitalize on emerging opportunities. In this way, technical knowledge not only enhances decision-making accuracy but also builds long-term financial resilience and trust in the investment process, making it a vital skill for sustainable wealth creation.

Satisfaction:

Investor satisfaction refers to the degree to which an individual’s expectations from an investment product, such as mutual funds, are met or exceeded. It is shaped by multiple factors including returns generated, risk levels, liquidity, service quality, and the alignment of the investment outcome with the investor’s financial goals. When investors feel that their financial decisions are yielding expected benefits whether in terms of wealth growth, tax savings, or security they report higher satisfaction. Satisfaction is therefore not only dependent on absolute returns but also on the perceived fairness, transparency, and efficiency of the investment process.

In the mutual fund context, satisfaction also arises from the decision-making experience itself. Investors who have sufficient technical knowledge and understand the risks, costs, and potential of their chosen funds are more confident and less likely to regret their choices. This sense of control and clarity enhances overall satisfaction, even if returns fluctuate in the short term. Conversely, lack of knowledge or mismatched expectations often leads to disappointment and dissatisfaction. Hence, satisfaction is both a psychological outcome linked to confidence and trust and a financial one, determined by tangible results from the investment.

Scholl, B., & Fontes, A. (2022). In the research paper titled “Mutual fund knowledge assessment for policy and decision problems.” The study concludes that while investors often display surface-level financial literacy, their deeper technical knowledge about mutual fund products such as the significance of share classes, expense ratios, and diversification is limited. This knowledge gap leads to suboptimal decision-making, where investors tend to overestimate their understanding but fail to choose funds that align with their long-term goals. The authors emphasize that accurate and targeted education tools, particularly those highlighting fee structures and performance benchmarks, are critical to improving decision-making. If such efforts are implemented effectively, investors are more likely to feel confident and satisfied with their investment choices, thereby reducing regret and enhancing overall financial well-being.

Capon, N., Fitzsimons, G. J., & Prince, R. (1996). In the research paper titled “An Individual Level Analysis of the Mutual Fund Investment Decision.” The paper highlights that many investors rely more on brand reputation, word of mouth, and convenience than on technical knowledge of fund performance or risk-return characteristics. This behavior shows that financial knowledge is often secondary to perceptions of trust and familiarity. Such reliance on heuristics can create satisfaction in the short term but may expose investors to dissatisfaction when fund performance does not meet expectations. The authors argue that empowering investors with technical knowledge particularly about cost implications and risk metrics could allow for better-aligned decision-making, where satisfaction is rooted in informed choices rather than surface impressions.

Alexander, G. J., Jones, J. D., & Nigro, P. J. (1997). In the research paper titled “Mutual Fund Shareholders: Characteristics, Investor Knowledge, and Sources of Information.” The authors reveal that while mutual fund investors are generally well-educated, they still display notable gaps in understanding key financial aspects, particularly regarding expenses, load charges, and risk factors. Many investors continue to depend heavily on advisors and simplified marketing materials, which can distort expectations. This lack of clarity contributes to dissatisfaction when fund outcomes do not align with perceived promises. The study suggests that better education, clear disclosure of costs, and transparent communication would not only enhance investor decision-making but also increase satisfaction by aligning expectations with realistic outcomes.

Saleem, S., Bashir, M., & Shabbir, R. (2021). In the research paper titled “Determinants of Investment Behavior in Mutual Funds.” The study concludes that investment behavior in mutual funds is shaped not only by financial literacy but also by investor perceptions of risk, expected return, and awareness of available schemes. Investors with stronger technical knowledge and higher awareness are more capable of evaluating products objectively, leading to better portfolio diversification and stronger satisfaction with decisions. On the contrary, low awareness results in overreliance on intermediaries and herd behavior, which may cause disappointment if returns fall short of expectations. Thus, technical knowledge acts as a buffer, helping investors evaluate trade-offs more effectively, which in turn improves their satisfaction with decisions made.

Muthupandian, M., & John, J. (2022). In the research paper titled “A Study on Investors’ Preference and Satisfaction towards Mutual Funds” The research highlights that while many investors prefer private sector funds and are motivated by tax benefits and safety, their satisfaction strongly depends on whether fund attributes are transparently communicated and align with expectations. Investors who received clear communication on risk, returns, and performance reported higher satisfaction levels, whereas those with poor understanding of fund mechanics expressed concerns. This implies that satisfaction is less about actual returns and more about clarity, transparency, and technical awareness. By improving investor education and aligning fund marketing with actual performance realities, mutual funds can significantly improve investor confidence and satisfaction.

Kumbhar, V., & Patil, S. (2023). In the research paper titled “A Study on Awareness and Preference towards Mutual Funds (with expectations and satisfaction)” The study finds that investor satisfaction with mutual funds is closely tied to levels of awareness about scheme features, risk categories, and service delivery. Many respondents were satisfied when they had prior knowledge of fund operations and could link them with their financial objectives. However, satisfaction declined where awareness was low, leading to mismatched expectations and disappointment. This finding underlines that building strong technical knowledge, supported by simplified educational campaigns, plays a crucial role in sustaining satisfaction, particularly in competitive markets where multiple fund options exist.

Kumar, R., & Sharma, A. (2025). In the research paper titled “Determinants of Mutual Fund Investment Returns: Evidence from Indian Retail Investors” The study emphasizes that socio-economic profile, financial literacy, risk appetite, and decisiveness significantly shape both returns and satisfaction in mutual fund investments. Investors with stronger technical knowledge of fee structures, systematic investment plans (SIPs), and asset allocation strategies not only earned better returns but also expressed higher satisfaction with their decisions. Conversely, limited knowledge led to lower returns and regret over decisions. This underscores that building investor competence through targeted awareness programs is crucial to ensuring satisfaction, as well as optimizing long-term wealth creation.

Patel, H., & Shah, P. (2025). In the research paper titled “Analyzing Investor Satisfaction with Mutual Fund Investments” The study concludes that satisfaction levels among investors are influenced by technical knowledge of mutual fund features, participation in SIPs, and quality of services offered by fund houses. Investors who were more knowledgeable about scheme objectives, fee structures, and risk-return patterns reported greater confidence and long-term satisfaction, even in cases of temporary market downturns. However, those who lacked clarity often showed dissatisfaction and confusion. The authors stress that strengthening investor education and simplifying fund disclosures are key strategies to foster satisfaction and long-term trust in mutual funds.

Research Gap:

Despite the growing body of literature on investor behavior, financial literacy, and mutual fund investment preferences, most existing studies have primarily focused on broad aspects of financial awareness or general investment behavior rather than specifically on the technical knowledge of investors in mutual funds. Many earlier works highlight investor reliance on brand image, advisors, or convenience factors, but they often neglect how technical understanding such as knowledge of expense ratios, NAV calculations, risk classifications, SIP benefits, or taxation directly influences satisfaction with investment decisions. This leaves a gap in understanding whether enhanced technical literacy translates into improved decision confidence, reduced regret, and higher long-term satisfaction. Additionally, while some studies have explored satisfaction levels of mutual fund investors, these are usually linked to returns and service quality rather than the depth of technical knowledge applied during decision-making. There is limited empirical evidence connecting investors’ technical comprehension with their post-purchase satisfaction, especially in the Indian context where mutual fund penetration is growing rapidly due to campaigns like “Mutual Funds Sahi Hai.” As financial products become increasingly complex, understanding this link is vital to designing effective investor education programs, improving disclosure practices, and ensuring sustainable satisfaction. This research gap highlights the need for a focused study that evaluates how technical knowledge shapes investment outcomes and satisfaction among mutual fund investors.

The research methodology adopted for this study follows a systematic approach to achieve the stated objectives. The research is descriptive in nature, aiming to examine the relationship and impact of technical analysis knowledge and technical knowledge on investors’ satisfaction in mutual fund investments. Primary data was collected from 175 respondents through a structured questionnaire, ensuring clarity and reliability. A pilot study was conducted to validate the tool, and reliability tests confirmed consistency of responses. The sampling technique used was simple random sampling to provide equal representation and reduce bias. For data analysis, statistical tools such as Pearson correlation, regression analysis, and Structural Equation Modelling (SEM) were applied to test hypotheses and measure relationships between variables. The methodology ensures robustness and credibility of results, enabling meaningful insights into how investor knowledge influences satisfaction in mutual fund decisions.

Data Analysis:

The following table indicates the demographic factor of the study:

The demographic profile of respondents reveals a diverse mix across gender, age, qualification, occupation, and income categories. Out of the total, males (104) slightly outnumber females (71), indicating a higher male participation in the study. Age-wise distribution shows that the majority of respondents are between 31–40 years (63) and 41–50 years (47), which together form the core working-age population most actively engaged in financial planning and investment decisions. Younger investors aged 20–30 years (22) and older groups like 51–60 years (34) and above 60 years (9) form smaller segments, highlighting that while mutual fund participation spans all age groups, it is most concentrated in mid-career professionals who are more financially stable and inclined toward structured investments. Educational and occupational details reflect that the respondent base is well-qualified, with the majority holding postgraduate degrees (74) or professional qualifications (47), followed by graduates (50), while only a small fraction have education up to HSC (4). This indicates that mutual fund investors in the sample are largely educated and potentially better equipped to understand investment products. Annual family income distribution shows that the majority fall in the 10–20 lakh range (108), followed by those earning less than 10 lakhs (53), while only a minority earn more than 20 lakhs (14). This suggests that mutual fund investments are most common among middle-income households, reflecting their growing financial aspirations and willingness to allocate savings toward wealth-building instruments.

The following table indicates investment frequency of respondents:

The data on investment frequency in mutual funds shows a varied pattern of investor behavior. A significant portion of respondents reported investing often (55), reflecting a consistent engagement with mutual funds, while 24 respondents indicated they always invest, suggesting a highly disciplined approach, possibly through systematic investment plans (SIPs). On the other hand, 48 respondents stated they invest rarely, and 28 respondents said they invest sometimes, highlighting a group of investors with irregular or opportunistic participation. Additionally, 20 respondents mentioned they had only invested in the past, indicating discontinued or withdrawn interest. Overall, the results suggest that while a majority demonstrate regular or frequent investment behavior, there remains a substantial segment of investors who are either irregular or inactive, pointing to differences in commitment levels and financial planning strategies.

The following table indicates percentage of your total investment invested:

The data on the percentage of total investments allocated to mutual funds highlights that the majority of respondents prefer to keep mutual funds as a moderate portion of their portfolio. The largest group, 76 respondents, invest between 11% to 25% of their total investments in mutual funds, reflecting a cautious yet steady allocation strategy. Another 55 respondents allocate only 0 to 10%, showing either low confidence, limited awareness, or preference for other investment avenues. Meanwhile, 37 respondents invest 26% to 50%, indicating a stronger reliance on mutual funds as a core part of their portfolio. Only 7 respondents allocate more than 50%, suggesting that very few investors are highly aggressive or fully reliant on mutual funds. Overall, the findings reveal that mutual funds are generally seen as a supplementary investment rather than the dominant choice, with most investors balancing them alongside other financial instruments.

Objective-1 to study relation between technical analysis knowledge of Investment and satisfaction of investment in MF.

Null Hypothesis H01: There is no relationship between technical analysis knowledge of Investment and satisfaction of investment in MF.

Alternate Hypothesis H11: There is a relationship between technical analysis knowledge of Investment and satisfaction of investment in MF.

To test the above null hypothesis, Pearson Correlation test is applied and results are as follows:

Interpretation: The above results indicate that calculated p-value is 0.030. It is less than 0.05. Therefore Pearson Correlation test is rejected. Hence Null hypothesis is rejected and Alternate hypothesis is accepted.

Conclusion: There is a relationship between technical analysis knowledge of Investment and satisfaction of investment in MF.

Findings: The Pearson correlation analysis shows a correlation coefficient of 0.315 between satisfaction and technical analysis knowledge, which indicates a moderate positive relationship. This means that as investors’ technical analysis knowledge increases, their satisfaction with investment decisions also tends to rise. The p-value of 0.000 confirms that this correlation is statistically significant at the 1% level, suggesting that the observed relationship is unlikely to be due to chance. With a sample size of 175 respondents, the result is reliable and highlights that technical analysis knowledge plays an important role in influencing satisfaction levels among investors.

• Objective 2: to study impact of technological knowledge on satisfaction of investment in MF.
• Null Hypothesis H02: There is no impact of technological knowledge on satisfaction of investment in MF.
• Alternate Hypothesis H12: There is a impact of technological knowledge on satisfaction of investment in MF.

To test the above null hypothesis, Pearson Correlation test is applied and results are as follows:

• Interpretation: The above results indicate that calculated p-value is 0.000. It is less than 0.05. Therefore Pearson Correlation test is rejected. Hence Null hypothesis is rejected and Alternate hypothesis is accepted.
• Conclusion: There is a impact of technological knowledge on satisfaction of investment in MF.
• Findings: The Pearson correlation analysis reveals a coefficient of 0.598 between satisfaction and technical knowledge, indicating a strong positive relationship. This means that as investors’ technical knowledge increases, their satisfaction with investment decisions also tends to rise significantly. The p-value of 0.000 confirms that this correlation is highly statistically significant at the 1% level, showing the relationship is not due to chance. With a sample size of 175 respondents, the findings are robust and reliable, highlighting that technical knowledge plays a crucial role in shaping and enhancing satisfaction levels in investment decisions.

• Regression Model:
• Dependent Variable: Satisfaction
• Independent Variable: Technical analysis knowledge , Technical knowledge

The model summary shows that the R Square value is 0.358, which means that about 35.8% of the variation in investor satisfaction can be explained by the independent variables—technical analysis knowledge and technical knowledge. This indicates a moderate level of explanatory power, suggesting that these two factors significantly influence satisfaction but are not the only determinants. The remaining 64.2% of the variation is explained by other factors not included in the model, such as market conditions, advisor support, personal financial goals, or psychological factors. The Adjusted R Square of 0.351 further confirms the reliability of the model, as it accounts for the number of predictors, showing that technical knowledge remains an important but not exclusive contributor to investor satisfaction.

Above results indicates that p-value is 0.000. It is less than 0.05. It indicates that linear regression model is good to fit.

Above table indicate the values of coefficients and corresponding significance. According to p-value of the Satisfaction it is observed that except “Technical knowledge” and “Technical analysis knowledge” remaining variables has significant impact on Satisfaction.  The mathematical equation to estimate the Satisfaction is presented as follows:

S= 27.177 + 0.15*TK + 0.103*TAK

Structural Equation Modelling:

Path Coefficients:

The path coefficients indicate the strength of influence between constructs. Technical analysis knowledge has a positive coefficient of 0.253 on satisfaction, showing that while it contributes to investor satisfaction, its effect is moderate. In contrast, technical knowledge has a stronger coefficient of 0.476, meaning it plays a more significant role in shaping satisfaction compared to technical analysis skills. This suggests that investors derive greater confidence and satisfaction from having a strong foundational understanding of mutual fund structures, risks, and costs rather than from technical charting or market trend analysis.

Outer Loadings:

Outer loadings measure how well each indicator reflects its latent construct. For satisfaction, items S-1 (0.856), S-3 (0.831), S-4 (0.811), and S-5 (0.890) show strong loadings, confirming they are reliable measures, while S-2 (0.446) is weaker and may need reconsideration or refinement. For technical analysis knowledge, TAK-3 (0.919) and TAK-4 (0.644) are valid contributors, whereas TAK-1 (-0.112), TAK-2 (0.125), and TAK-5 (0.157) show poor loadings, indicating weak representation of the construct. For technical knowledge, all indicators (0.630–0.811) demonstrate acceptable to strong loadings, with TK-4 (0.811) being the most reliable, suggesting this construct is well-captured by its items.

Outer Weights:

Outer weights assess the relative contribution of each indicator to its construct. For satisfaction, all five items contribute positively, with S-4 (0.293) and S-1 (0.289) being the strongest contributors, reinforcing their importance. For technical analysis knowledge, TAK-3 (0.789) emerges as the dominant indicator, while TAK-4 (0.406) has moderate contribution; the remaining indicators have negligible or negative weights, indicating they are less useful in measuring this construct. For technical knowledge, TK-4 (0.366) and TK-1 (0.354) show the strongest weights, while TK-2, TK-3, and TK-5 contribute moderately. This demonstrates that while all items help define the construct, a few carry stronger explanatory power than others.

The overall analysis concludes that both technical analysis knowledge and technical knowledge significantly influence investor satisfaction in mutual funds, though their impact differs in strength. Pearson correlation results show that technical analysis knowledge has a moderate positive relationship (r = 0.315), while technical knowledge demonstrates a stronger positive relationship (r = 0.598) with satisfaction. Regression analysis further highlights that technical knowledge has a significant impact on satisfaction, whereas technical analysis knowledge shows a weaker influence. Structural Equation Modelling confirms this distinction, with technical knowledge (0.476) exerting a stronger effect on satisfaction than technical analysis knowledge (0.253). The reliability of measurement indicators also supports that technical knowledge is more consistently represented compared to technical analysis knowledge. Overall, the findings emphasize that while both knowledge domains enhance investor confidence and satisfaction, a strong foundational understanding of investment mechanisms and technology contributes more substantially than technical charting skills in ensuring satisfaction with mutual fund investments.

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