Introduction
The UK has one of the most sophisticated financial markets in the world, where the London Stock Exchange is one of the most important world markets where equity and fund trading take place. The barriers to market entry have been considerably reduced over the last few years due to structural deregulation, online trading platforms and their blistering development, and finally, online trading platforms have become more accessible, with the significant assistance of digital innovation. The amount of retail participation has soared, and this has been aided by the presence of cheap brokers and tax-efficient products, including ISAs (FCA, 2024). Nevertheless, the greater access has not been accompanied by financial capability. The national surveys indicate that adults in the UK perform poorly on the underlying financial concepts such as inflation, diversification and compound interest, which casts doubt on whether retail investors are prepared to operate in more complex markets (OECD, 2023).
According to behavioural finance studies, low levels of financial literacy increase exposure to cognitive biases, including overconfidence, herding and loss aversion, which can result in speculative, under-diversified or over-risky types of investment behaviour (Suresh, 2021). More recent FCA reports also indicate that the UK retail investor, especially younger and more inexperienced groups, are increasingly being influenced by social-media stories and gamified trading platforms, which add to impulsive trading patterns and inadequate risk-judgements (FCA, 2024). These trends are breaking the conventional belief that financial literacy is sufficient in making rational decisions.
Although the previous literature indicates the connection between literacy and investment returns, including portfolio diversification, long-term planning and market participation (Lusardi and Mitchell, 2014), the UK evidence base is rather limited, with geographic diversity and high dependency on national surveys or student samples. These strategies blur geographical disparities in fiscal ability and context-dependent behavioural processes. Manchester is a comparatively valid, but insufficiently studied, case: a fast-paced economic hub that has a diverse, digitally active investor base but on which the empirical literature is limited regarding the influence of knowledge degree on investment decisions (Darwish, 2025).
This article fills these gaps by investigating the correlation between financial literacy, Risk-taking, diversification and product selection on a sample of 100 Manchester based retail investors. It also examines whether financial literacy among the population of this region, based on demographic issues, such as age, education, income, etc., is predictable. The study can contribute to the current controversies in the retail investor protection, financial inclusion and the effectiveness of literacy-based policy interventions in UK by combining behavioural finance views and regional empirical evidence.
The three research questions used during the study are based on such gaps:
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How does financial literacy influence UK retail investors' decisions on risk-taking, portfolio diversification, and financial product selection?
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What is the relationship between demographic factors (age, education, income) and financial literacy among UK retail investors?
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In what ways do varying levels of financial literacy shape the investment strategies and behaviours of UK retail investors?
There are four contributions in this article. First, it offers the first regional empirical evidence that determines literacy behaviour relationships in Manchester. Second, it presents a challenge based on the data on the widely held belief that literacy enhances investment returns. Third, it demonstrates that rational knowledge is mostly dominated by behavioural clues, confidence biases and outside sources of information in the process of making investor decisions. Lastly, it provides policy-relevant findings to UK regulators, which can prove ineffective without behaviourally informed interventions.
The rest of the article continues as follows: Section 2 provides the literature review; Section 3 is a section of data and methodology; Section 4 presents the empirical findings; Section 5 is a discussion of the research and policy implications; and finally, is the conclusion.
Background and problem motivation
Background: UK retail investing landscape
In the UK, the history of retail investing has had a long history, with the formation of the London Stock Exchange in 1801, and changing over time because of deregulation, technological advances, and the growing access to capital markets by consumers (Stringham, 2002). In the last ten years, the industry has experienced an even greater change due to the fast emergence of digital brokerage houses, Mobile-app trade, and robo-advisory software that allow individuals to purchase equities, ETFs, diversified portfolios, with minimal capital and near non-contact with conventional financial advisors (He et al., 2025). The growth of tax effective products like ISAs and the rise of low cost online brokers has led to a sudden surge in self-directed activity especially within younger groups. Certain evidence reveals that surveys conducted by FCA have recorded increased participation of first-time investors, who tend to make their decisions based on stories shared online and social pressure instead of financial analysis (Financial Conduct Authority, 2021). Simultaneously, OECD surveys invariably indicate that UK adults perform dismally on basic financial knowledge tests, which indicate a deficit in their risk diversification, inflation and compound interest, which are key components of informed investment behaviour (OECD, 2023). Financial capability has been increased by national efforts, such as those provided by the Money and Pensions Service, but ONS and FCA statistics reveal that differences in financial literacy are still widespread and uneven geographically (Gov.UK, 2021). It is further increased by the increased visibility of behavioural biases in the market accessibility and investor capability, through the trend of younger investors into overconfidence, investing in the more speculative assets of cryptocurrencies and meme stocks, and increased risk-taking enabled by gamified digital trading interfaces. According to FCA reports (2025), these behavioural patterns are the material threats to consumer resilience because retail investors are likely to overestimate the risk and overestimate market volatility. The overlap of low literacy, high digital activity/behavioural vulnerabilities highlight the urgent requirement to know how financial literacy influences investment choices on the contemporary UK market, especially in diverse and fast-developing regional centres like Manchester.
Problem motivation
Although the digital trading platforms and fintech applications have grown fast, making investments accessible to larger quarters of the UK population, a significant number of retail investors still display low financial literacy. According to FCA reports (2024), a significant number of new and inexperienced investors do not fully understand risk, diversification, or long-term financial concepts and therefore go into the market. The given knowledge gap not only limits the ability to make an effective decision, but also increases the vulnerability to behavioural biases, including loss aversion, overconfidence, herding and availability heuristics (Wang and Zou, 2024). These biases are often shown in sub-optimal behaviour, such as speculative trading, poor diversification and higher risk-taking, although more investment tools have been made available.
Elderly people, those with low incomes, and people with low formal education are particularly vulnerable, as they have increased risks of making bad decisions and, in some instances, they are financially exploited (Vanguard, 2023). The existence of these differences leads to more serious issues of financial inclusion and investor well-being, particularly with the further expansion of retail market participation. However, current empirical data seldom analyses the influence that difference in the financial literacy levels has on the actual investment behaviour in UK regional settings (Nasrin, 2025). Most of the previous findings are based on national surveys or sample of students, which conceals critical micro-regional variations in financial ability and experience of investors.
Manchester is an such a neglected setting a fast-growing investment centre with high digitalisation and socio-economic diversity and minimal academic interest. It is therefore imperative to understand how literacy affects the risk preference of the investors as well as their diversification patterns and product preferences in this regional ecosystem. Filling this gap, this paper critically examines how financial literacy is related to major investment behaviour among Manchester retail investors and relates to the current literature discussing bettering financial wellbeing, investor resilience and market participation in the UK.
Research gap
Despite the acknowledgement of the importance of financial literacy in investment decision-making, there still is a gap of evidence in the specific context of the UK (Mireku, Appiah and Agana, 2023). The existing research is largely based on national surveys, or on student samples, providing little information on the dynamics in specific regions, especially in cities economically varied like Manchester, where financial inclusion and financial literacy can differ significantly between population groups (Ghafran and Yasmin, 2024). This limits the generalisability of the national results to local investor behaviour. More than that, in most cases, previous research considers financial literacy as a single concept or targets specific behaviours like risk-taking or diversification (Cavezzali, Gardenal and Rigoni, 2012). There is limited empirical research that assesses the interaction of literacy with demographic variables, including age, education, income, gender, to determine various and interdependent investment decisions (Malini, 2024). Considering both the quick post-pandemic increase in the use of fintechs, gamified trading, and speculative assets, new evidence based in the United Kingdom is needed to understand whether low literacy contributes to or alleviates the growth of these tendencies. This article fills these gaps by a behavioural analysis of the city of Manchester, which is multidimensional.
Literature review
The contemporary financial landscape has changed to the extent that financial literacy has had to be regarded as a multidimensional factor in investment decision-making. Regardless of the increasing digital access, historical literacy disparities still determine retail investor returns (Lusardi and Mitchell, 2014). Although the lack of knowledge builds to under-diversification and excessive risk-taking (Rooij, Lusardi and Alessie, 2007), behavioural finance reveals that even informed investors are susceptible of biases relating to overconfidence and herding (Kahneman and Tversky, 1979). It is particularly applicable to the UK, where the market has increasingly become product-saturated and digital (FCA, 2025), and it can be argued that the diversity of investors in Manchester can also offer a valuable background to analysing these dynamics.
Financial literacy and investment outcomes
Financial literacy is a well-established base variable in determining good financial decision making with researchers like Lusardi and Mitchell (2014) highlighting its contribution to equity market participation and participation in long term financial planning and wealth accumulation. The experience of other countries has frequently revealed that better-educated people are building more diversified portfolios, are more confident about assessing financial tools, and are choosing a strategy which is consistent with their long-term financial wellbeing. Nevertheless, new studies put the universality of this relationship into question. The researchers such as van Rooij, Lusardi and Alessie (2011) demonstrate that the greater the level of literacy, the greater it predicts participation in stock market in some settings, although it is less important or less consistent in other contexts, suggesting that some institutional, cultural, and psychological factors mediate the knowledge-behaviour relationship. These inconsistencies are a problem to the assumption that literacy is the cause of rational investment decisions. This is further criticized by the studies on behavioural finance that have shown that even well-informed investors are vulnerable to cognitive and emotional biases such as overconfidence, herding, loss aversion and present bias (Kahneman and Tversky, 1979). This diversity is also observed in the findings of international studies: other studies have reported the positive correlation between literacy and positive investment outcomes, but other studies have no (or insignificant) effects. These ambiguous findings indicate that literacy could result in a positive impact on conceptual knowledge and not necessarily result in behavioural change-particularly in digital markets of fast-moving speculative assets. Evidence based in the UK is scanty and is mostly based on national surveys and not region-specific analysis. Literacy is not extensively empirically interlinked with tangible behavioural systems like portfolio diversification or risk-taking, in certain local settings. The research will help fill this gap by demonstrating that financial literacy was not a significant predictor of risk-taking, diversification, and product choice among Manchester retail investors, and the impacts of behavioural, situational, and platform-driven factors are more influential than the impact of literacy.
Behavioural biases in retail investing
The behavioural biases have become more and more the focus of the analysis of the retail investor decision-making, especially when digital trading systems become more and more intensive in stimulating rapid, emotionally-driven investment behaviour. There is considerable evidence of behavioural finance studies that retail investors often make use of mental shortcuts instead of applying a systematic evaluation even when they have sufficient financial expertise. One of the most accurately recorded biases is overconfidence. Barber and Odean (1998) indicate that investors overestimate their forecasting skills in the market, which subject them to overtrading and poor performance. This prejudice is quite conspicuous in the UK setting, as FCA consumer research reveals that younger and unexperienced investors tend to penetrate the turbulent markets, including cryptocurrencies, without understanding the risk they expose themselves to. This is interpreted by behavioural theorists as a discrepancy between confidence and competence and so the speculative impulses cannot be curtailed merely by financial literacy.
Guiding behaviour goes against the conventional economic assumptions too. Retail investors tend to take the steps of their fellow investors or a trend on social media instead of assessing financial data on their own. Sayyed et al. (2024) demonstrate that herding may bring about disequilibrium in the market with investors following what is perceived to be group wisdom. Similar evidence is reported by the FCA which reports an increase in the number of trending stocks and narrative trades by UK investors, which is adding to clustered and risky behaviour- which is in line with the findings that literacy was not a determinant of investment decision-making and that external factors might be more influential.
The concept of mental accounting also makes rational decision-making difficult. According to Thaler, money is split up by investors into distinct mental accounts and this facet of discretionary investment is sometimes viewed as a long-term objective in the mind, although the investment is seen as speculative funds (Thaler 1999). This is an inconsistency that FCA research has verified as many retail investors engage in short-term speculation and at the same time state long-term financial goals. Collectively, these behavioural propensities demonstrate that the financial literacy cannot be used to explain UK retail investment behaviour fully. Rather, biases like overconfidence, herding and mental accounting have a significant impact, and behavioural insights, as opposed to literacy-based policy interventions, are required to enhance the situation.
Gaps in existing evidence
Even though the literature on financial literacy and investment behaviour globally has extended, there are still knowledge gaps in relation to the UK scenario. The current researches are mainly based on nation-wide surveys, university samples or cross-country data, and thus they miss region-specific trends that are influenced by local socio-economic forces (Brown and Cowling, 2022). This is because broad methods do not illustrate the dynamics of financial capability, digital investment behaviours and behavioural tendencies variation in the UK cities. In addition, little literature combines financial literacy with tangible behavioural variables, including risk tolerance, diversification decisions, and product investment, even though they are the key variables in determining how retail investors approach the modern financial markets. The literature has been mostly examining literacy as an independent variable without looking at its effects with behavioural biases, risk perceptions as well as the situational factors (Saleem, Usman and Bashir, 2023). Empirical evidence on this topic in the UK is limited, and there is even less evidence on region-specific financial centres, like Manchester. This is an interesting gap considering that Manchester has a growing base of retail investors, social and economic diversity, and quick-paced towards digital investment platforms. These constraints show that region-based, behaviourally informed research is necessary to study the role of literacy, demographics and behavioural variables in determining the outcome of investment in the changing UK digital investment environment.
Data and Methodology
Data source and sampling
This article is based on primary data, which was collected based on a structured survey conducted on retail investors located in Manchester (Kanika, 2023). The type of survey design adopted was quantitative to facilitate the systematic measurement of the financial literacy, risk attitude, diversification behaviour and product selection among a varied population of respondents. The strategy employed is the sampling where people who are actively involved in investing using the digital platform, ISA, brokerage account or an online trading platform were targeted. One hundred valid responses were collected, and this is adequate to conduct regression and correlation analysis as frequently done in studies of behavioural finance. The participants were identified by participating in investment forums, financial education groups and local professional networks to achieve the inclusion of both experienced and novice investors and hence the heterogeneous nature of the population of retail investors in Manchester. Ethical aspects were also observed greatly: all the respondents were volunteers, anonymity was ensured, and data was collected following the general ethics of conducting research, which guaranteed the nature of confidentiality and informed consent. The sample was demographically diverse about age, education level, income brackets and investment experience, which allowed evaluating the question of whether age, demographic and income bracket influence investment behaviour or whether demographic variables affect financial literacy. This methodology was used to guarantee an accurate sample of the retail investor population that was representative of a real cross-section of the industry participants who were present in one of the most economically dynamic and fast-growing regional markets in the UK.
Variables contribution
The research uses a well-constructed series of quantitative variables that are used to quantify financial literacy, investment behaviour and behavioural characteristics among the retail investors. Variables are all derived out of the survey instrument and are coded in a systematic manner to be replicated easily.
Financial Literacy Score
A measurement of financial literacy is through a five-item objective knowledge test which involves questions on inflation, interpretation of interest rates, basic numeracy, differentiation of risks and fundamental financial concepts. They would be coded as 1 correct answer and 0 incorrect answer giving a composite Literacy Score of 0-5. This index represents objective financial knowledge of the respondents and this is the main independent variable in the following analysis.
Risk-Taking Variables
Risk-related behaviour is captured through three separate measures.
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Risk Preference is based on respondents’ self-reported willingness to invest in high-risk/high-return products and is coded as an ordinal or binary variable depending on the model specification.
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Portfolio Risk Level records whether individuals classify their overall investment approach as low, medium or high risk.
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Risk-Understanding Scale is constructed from multiple Likert items assessing comprehension of volatility, uncertainty and the risk return trade-off. Higher scores indicate stronger conceptual understanding of investment risk.
Diversification Measures
Diversification is operationalised through two indicators to capture both subjective and objective portfolio structure.
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Self-Reported Diversification is measured on a four-point ordinal scale ranging from “not diversified” to “highly diversified.”
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Diversification Index is a count variable (0–8) reflecting the number of distinct asset categories held, including equities, bonds, mutual funds, ETFs and other investment products. This index follows standard household finance practice in measuring breadth of asset allocation.
Overconfidence Indicators
Overconfidence is approximated using two self-assessment variables.
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Investment Confidence captures respondents perceived confidence in managing their investments, measured on a Likert scale.
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Self-Perceived Financial Knowledge reflects respondents’ subjective assessment of their financial understanding. These indicators serve as behavioural proxies for confidence–knowledge discrepancies commonly examined in behavioural finance literature.
Control Variables
To account for demographic influences, the analysis includes age (continuous), education level (ordinal categories) and income bracket (ordinal categories). These variables represent standard socio-economic controls in financial capability and behavioural finance models.
Model specification and hypothesis testing
The entire empirical testing was performed in SPSS in a series of correlation and regression modelling in line with the positivist, hypothesis-testing nature of the study. Descriptive statistics were used to start the analysis of the sample, summarising the distribution of financial literacy scores, attitudes towards risk, the level of diversification and use of products offer by the insurer, and the demographic composition of the sample.
To test Hypothesis 1 (that more risk-appropriate behaviour would be linked with greater financial literacy) and Hypothesis 2 (that greater risk-appropriateness behaviour would be linked with higher financial literacy), the study commenced by using Pearson correlation analysis to test bivariate relationships between the Literacy Score and the variables representing the confidences, risk level, risk preference, diversification measures, and ETF use. These associations were subsequently tested more formally with ordinary least squares (OLS) regression in continuous or ordinal outcomes (e.g. self-reported diversification, diversification index, risk-understanding), chi-square tests of association in categorical levels of portfolio risk, and a binary logistic regression equation where Literacy Score would be the primary predictor. The model being tested in the hypothesis 2 (that age, education and income would significantly predict financial literacy) would have been a multiple linear regression model where Literacy Score would be dependent variable where age, education and income would be entered simultaneously as independent variables. Further Pearson correlations between literacy and all the demographic variables were also calculated to determine the direction and strength of the relationships.
The hypothesis 3 (that various degrees of financial literacy would influence self-perceived knowledge, risk-understanding scale and diversification behaviour) was tested by a combination of correlation analysis and OLS regression models, that is, specifying self-perceived knowledge, risk-understanding scale and diversification indicators as the dependent variables and Literacy Score as the key explanatory variable. The scale reliability was determined with the help of Cronbach alpha on 19 questions on the survey questions based on the literacy and investment behaviours but the conventional 0.70 scales were applicable in order to determine the acceptable internal consistency (Nunnally, 1978).
The adaptation of popular items of financial literacy outlined by Lusardi and Mitchell (2011) and other similar survey instruments was the support of content validity, where the constructs measured were consistent with the literature definitions. Regression linearity and the lack of severe cases of multicollinearity were the two fundamental assumptions of regression reviewed by examining descriptive distribution, correlation matrices before carrying out the hypothesis tests.
Model diagnostic
Various diagnostic tests were performed to evaluate the strength of the regression models. Variance Inflation Factors (VIF) were used to assess multicollinearity, and all predictors had a VIF below 2.0, which does not indicate the presence of multicollinearity and proves the fact that the age, education, and income were independent explanatory variables. The Q-Q plots and Shapiro-Wilk tests were used to check whether the residual values follow normal distributions; both tests showed that distribution of the residual values is close to normal, and there is no significant violation of the assumptions of linear modelling. The test of linearity and homoscedasticity were evaluated by use of scatterplots of standardised residuals versus predicted values. The plots were randomly dispersed and not funnelled to support the existence of linear relationships and constant variance between fitted values. The model performance was further supported in terms of goodness-of-fit statistics: OLS regressions provided very low levels of R 2 (0.000-0.013), which is in line with the lack of predictive effects; logistic regression models had a very low level of explanatory power with it being defined by negligible values of pseudo-R 2 (Nagelkerke R 2 less than 0.01). Logistic specifications of a model 2x test were non-significant which implies that financial literacy did not enhance classification probability as opposed to the base-level probability. All these diagnostics are evidence that assumptions of the model were satisfied and that the lack of any significant effects is a result of the empirical form of the data and not a result of a statistical artefact.
Results
This section presents findings of an empirical study of 100 Manchester retail investors, including descriptive statistics, correlation and regression analyses. It looks at the relationship between financial literacy and risk-taking, product choice and diversification, and determines whether literacy is predicted by demographic factors to relate the results with the aims and theoretical context of the study.
Internal consistency of the measurement scale proceeded on 19 questions in the questionnaire, with a Cronbach’s alpha of 0.817 and standardized alpha of 0.851. The two have a high reliability and coherent measurement of the underlying constructs as they both surpass the generally accepted 0.70 mark (Nunnally, 1978). Such high amount of internal consistency guarantees that the items always measure financial literacy and related behaviours, which would give a consistent basis of the following correlation and regression analysis, and would give more confidence to the empirical results of the study.
Table 1 Reliability Analysis
| Reliability Statistics | ||
|---|---|---|
| Cronbach's Alpha | Cronbach's Alpha Based on Standardized Items | N of Items |
| .817 | .851 | 19 |
The demographic analysis of the 100 Manchester-based retail investors offers necessary background to the further analysis of financial literacy and investment behaviour. The age distribution (M = 2.53, SD = 1.24; median = 2) shows that the respondents were mainly of the age group of 25 34, which is a sign of younger investor base characteristic of digitally active markets. The level of education was relatively high (M = 3.23, SD = 1.56; median = 3), half of the population had bachelor's degree and quarter had postgraduate qualifications, which is in line with the fact that Manchester is one of the largest educational and financial centres in the UK. The income levels were focused on middle-income groups (M = 3.11, SD = 1.54; median = 3) which implies that the respondents were not concentrated on higher-income groups and had enough disposable income to do regular investing. Experience in investing was also biased on early-stage investors (M = 2.44, SD = 1.05; median = 2), and most of them have a history of one to six years of involvement in the market. When combined, these attributes create a portrait of relatively young, well-educated and middle-income investors with moderate experience which is a solid foundation on which to explore whether age, education and income are predictors of financial literacy (H2) and on how the demographic differences influence risk-taking, diversification and product-choice behaviour among the sample.
Descriptive Statistics for Demographic Variables (N = 100)
Table 2 Descriptive Statistics
| Variable | M | SD | Median |
|---|---|---|---|
| Age Category | 2.53 | 1.24 | 2 |
| Gender | 1.56 | 0.65 | 1.50 |
| Education Level | 3.23 | 1.56 | 3 |
| Income Category | 3.11 | 1.54 | 3 |
| Investment Experience | 2.44 | 1.05 | 2 |
Note. M = Mean; SD = Standard Deviation. All variables coded as ordinal categories corresponding to demographic groups used for regression analysis.
Hypothesis 1 tested the existence of stock-appropriate behaviour, portfolio diversification, and the choice of cost-efficient investment products as predicted by financial literacy. The results, contrary to the expectations based on the behavioural finance and rational choice theory, did not support this hypothesis with any empirical evidence. Correlation analysis did not reveal significant relationship between literacy scores and self-reported confidence in investment management (r = -.005, p =.959), which is to say that better objective knowledge may not be reflected in a better subjective competence. This is in line with the fact that behavioural biases and previous experience often influence confidence and not knowledge.
Table 3 Pearson correlation for literacy score
| How confident do you feel managing your personal investments? | Literacy Score | ||
|---|---|---|---|
| How confident do you feel managing your personal investments? | Pearson Correlation | 1 | -.005 |
| Sig. (2-tailed) | .959 | ||
| N | 100 | 100 | |
| Literacy Score | Pearson Correlation | -.005 | 1 |
| Sig. (2-tailed) | .959 | ||
| N | 100 | 100 |
The ordinal logistic regression on the level of portfolio risk generated a non-significant model of risk-related behaviours, χ 2 (1) = 1.310, p =.252. The regression coefficient of literacy was non-significant and with a negative value (B = -.227, SE =.202, p =.261), indicating that literacy does not significantly affect the classification of themselves as low-, medium-, or high-risk oriented investors. On the same note, binary logistic regression of pitching high-risk investment preference found no predictive impact of literacy (B = 0.200, SE = 0.365, p = 0.583). All these findings suggest that behavioural and contextual influences affect risk-taking tendencies instead of being influenced by knowledge-based judgements.
Table 4 Binary logistic regression
Model Summary
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | .016 | .000 | -.010 | .90992 |
Predictors: (Constant), Literacy Score
ANOVA
| Model | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| Regression | .020 | 1 | .020 | .025 | .876 |
| Residual | 81.140 | 98 | .828 | ||
| Total | 81.160 | 99 |
Dependent Variable: How diversified is your current investment portfolio?
Predictors: (Constant), Literacy Score
Coefficients
| Model | B | Std. Error | Beta | t | Sig. |
|---|---|---|---|---|---|
| (Constant) | 3.191 | .206 | 15.474 | .000 | |
| Literacy Score | .016 | .100 | .016 | .157 | .876 |
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Dependent Variable: How diversified is your current investment portfolio?
The null results were also obtained in diversification outcomes. The relationship between literacy and self-reported diversification was non-significant and weak (r =.016, p =.876), and a linear regression ensured that literacy was not a predictor of diversification scores (B =.016, p =.876; R 2 =.000). This disputes the assumptions of more informed investors who invest better. Lastly, literacy did not foretell the use of cost-effective products like ETFs. Binary logistic regression produced an insignificant effect (B =.200, p=.583), and insignificant model-fit (Nagelkerke R 2=.006). Since the proportion of prospective users (10 percent) of ETFs was low, the product availability and social pressure might be more critical than literacy.
Overall, Hypothesis 1 cannot be confirmed. The findings indicate that risk-taking behaviour, diversification, and cost-effective product adoption cannot be explained by financial literacy alone, which implies that the effect of behavioural biases, demographic factors, and access to the market should be considered in understanding investor behaviour.
Hypothesis 2 looked at the question whether demographic factors such as age, education, and income significantly influenced the level of financial literacy among the Manchester-based retail investors. The hypothesis was based on human capital theory (Becker, 1964) which postulates that human beings are able to build financial capability in terms of education, experience and economic resources. The literature of behavioural finance also identifies demographic characteristics as the determinant predictors of financial knowledge (Lusardi and Mitchell, 2014). But these theoretical expectations are not supported by empirical evidence of this study.
Correlation analysis showed that the various demographic variables do not play a significant role in the financial literacy. The correlation between age and literacy was also weak and non-significant (r =.053, p =.601), which implies that the older investors failed to be more financially literate as compared to their younger counterparts. The literacy and income relationship was not significant (r =.019, p =.851), which indicates that the more a person earned, the better was his financial knowledge. Unexpectedly, the relation between literacy and education was not significant and slightly negative (r = -.082, p =.419), which meant that higher education did not correlate with higher scores of literacy in this sample. There had been one correlation of interest in the demographic matrix: education and income were found to be significantly negatively correlated (r = -.207, p =.038), which may indicate that the highly educated respondents were more likely to fall into low-income categories, possibly due to younger professionals still in the initial stages of their career.
Table 5 Correlation Analysis for relationship between literacy and age
Correlations
| How diversified is your current investment portfolio? | Literacy Score | Age | Edu | Income | ||
|---|---|---|---|---|---|---|
| How diversified is your current investment portfolio? | Pearson Correlation | 1 | .016 | .003 | .064 | -.061 |
| Sig. (2-tailed) | .876 | .976 | .528 | .547 | ||
| N | 100 | 100 | 100 | 100 | 100 | |
| Literacy Score | Pearson Correlation | .016 | 1 | .053 | -.082 | .019 |
| Sig.(2-tailed) | .876 | .601 | .419 | .851 | ||
| N | 100 | 100 | 100 | 100 | 100 | |
| Age | Pearson Correlation | .003 | .053 | 1 | .025 | -.136 |
| Sig. (2-tailed) | .976 | .601 | .805 | .177 | ||
| N | 100 | 100 | 100 | 100 | 100 | |
| Edu | Pearson Correlation | .064 | -.082 | .025 | 1 | -.207 |
| Sig. (2-tailed) | .528 | .419 | .805 | .038 | ||
| N | 100 | 100 | 100 | 100 | 100 | |
| Income | Pearson Correlation | -.061 | .019 | -.136 | -.207 | 1 |
| Sig. (2-tailed) | .547 | .851 | .177 | .038 | ||
| N | 100 | 100 | 100 | 100 | 100 |
* Correlation is significant at the 0.05 level (2-tailed).
The multiple regression analysis also supported the fact that there were no significant demographic influences on literacy. The general model did not have significant values F (3, 96) = 0.316, p = .814, or predicting out any significant value in the variance of literacy (R 2 =.010). All of the demographic predictors were not significant: age (B = .041, p =.584), education (B = -047, p =.437), and income (B =.006, p =.926). All these findings suggest that age, education and income had no significant influence on predicting financial literacy among the sampled investors.
These findings are contrary to a large portion of the current literature, which generally has positive correlations between demographics and financial literacy (e.g., Disney and Gathergood, 2013; Bucher-Koenen and Lusardi, 2011). The most probable reason is that the sample included active retail investors, and the demographic differences might have been minimized by the baseline publicity to the financial markets. Behavioural finance views also hint that experience, social influence and online financial information can be more influential in the development of literacy than traditional demographics. Overall, Hypothesis 2 is not proved.
Hypothesis 3 tested the significance of financial literacy in self-perceived knowledge, risk related understanding and diversification practices of investors. The hypothesis was based on the behavioural finance theory and human capital approaches, which held that the greater the level of literacy, the greater the ability of investors to make quality, organized and diversified investment decisions. The empirical evidence does not however substantiate this expectation.
The initial analyses examined the hypothesis of whether literacy was a predictor of self-perceived stock market knowledge or not. The outcome of the linear regression showed that there was no significant correlation (B =.040, p =.668, R 2 =.002) between increased literacy scores and increased self-assessments of knowledge. This is in line with behavioural finance literature that reports discrepancies between objective and subjective capabilities, usually propagated by overconfidence or social comparison effects (Hsu et al., 2020). In this way, the factor of self-perceived expertise within the sampled investors seems to be influenced more by the psychological inclination or previous experience rather than the actual knowledge of financial context.
Table 6 Regression Analysis on predictive relationship
ANOVA
| Model | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| Regression | .809 | 3 | .270 | .316 | .814 |
| Residual | 81.941 | 96 | .854 | ||
| Total | 82.750 | 99 |
Coefficients
| Model | B | Std. Error | Beta | t | Sig. |
|---|---|---|---|---|---|
| (Constant) | 1.880 | .383 | 4.914 | .000 | |
| Age | .041 | .075 | .056 | .549 | .584 |
| Edu | -.047 | .061 | -.081 | -.781 | .437 |
| Income | .006 | .062 | .010 | .094 | .926 |
Dependent Variable: Literacy Score
Table 7 Regression Analysis for predictive relationship
Model Summary
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | .043 | .002 | -.008 | .85508 |
Predictors: (Constant), Literacy Score
ANOVA
| Model | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| Regression | .136 | 1 | .136 | .185 | .668 |
| Residual | 71.654 | 98 | .731 | ||
| Total | 71.790 | 99 |
Dependent Variable: Do you consider yourself knowledgeable about the stock market?
Predictors: (Constant), Literacy Score
Coefficients
| Model | B | Std. Error | Beta | t | Sig. |
|---|---|---|---|---|---|
| (Constant) | 2.815 | .194 | 14.527 | .000 | |
| Literacy Score | .040 | .094 | .043 | .431 | .668 |
Dependent Variable: Do you consider yourself knowledgeable about the stock market?
The second aspect of H3 was the measure of the role of literacy around portfolio diversification. Ordinal logistic regression on self-reported level of diversification gave a non-significant literature effect (B =.038, p =.852; 2 = 0.036, p =.850), most investors having moderate (41%) or high level of diversification irrespective of the level of literacy. These findings were supported by a secondary test conducted on the Diversification_Index (08 holdings of product literacy) once again, literacy was not a predictor (B = -.218, p =.261, R 2 = 0.013). These results disprove conventional finance forecasts that advise informed investors to diversify in order to minimize unsystematic risk. Instead, they are consistent with behavioural accounts which indicate that external constraints, availability of products or dependence on informal advice and not just knowledge drives diversification.
Table 8 Ordinal Logistic Regression Predicting Diversification Level from Financial Literacy
| Predictor | Estimate | SE | Wald | df | p | 95% CI (LL, UL) |
|---|---|---|---|---|---|---|
| Literacy Score | 0.038 | 0.202 | 0.035 | 1 | .852 | -0.358, 0.433 |
Model Fit:
χ²(1) = 0.036, p = .850
Pseudo R²:
Cox & Snell = .000; Nagelkerke = .000;
McFadden = .000
Note. DV = Self-reported diversification (Q9, ordinal).
The last analysis was concerned with risk-related knowledge and trust. Investment confidence was not correlated with literacy (r = -.005, p =.959). A non-significant negative relationship was found between literacy and the risk-understanding scale (r = -.178, p =. 076), indicating that literacy did not significantly improve the risk-understanding scale. Remarkably, risk-understanding scale was found to be significantly correlated with behavioural cues, such as the dependence on news media (r =.689, p <.01) and trust in investment (r =.641, p <.01) with the significance of informational environment and heuristics.
Table 9 Correlations between Financial Literacy and Risk-Understanding Scale
Variable |
1 |
2 |
|---|---|---|
1. Risk-Understanding Scale |
— |
|
2. Literacy Score |
−.178 (.076) |
— |
Note. Values are Pearson’s r ; p -values are in parentheses. N = 100.
Overall, H3 is not supported. Financial literacy did not play a significant role in perceived knowledge, diversification behaviour and understanding of risk. These findings indicate that behavioural, contextual and experiential factors influence investor strategies in this sample more than literacy as an independent variable, and that more extensive behavioural interventions are required, other than education programs.
Discussion
This study aimed at examining to what degree financial literacy affects the behaviour of retail investors in the UK stock market, especially in risk taking, diversification of their portfolio, and selection of cost efficient products. The research also tested the hypothesis of whether age, education, and income are predictors of financial literacy. The research gives a detailed insight into behavioural, demographic and informational determinants of investment decisions among retail investors with the city base being Manchester by testing three hypotheses. The results, in general, indicate that the concept of financial literacy, thought to be at the centre of activities in both classical and behavioural finance models, is not a powerful or independent predictor of investment behaviour in this sample. Rather, the investor decision making seems to be influenced more significantly by behavioural cues, informational settings and contextual influences. The section addresses these findings with respect to theoretical expectations, available literature, as well as policy and practice implications.
The initial hypothesis was that risk-appropriate behaviour, increased diversification, and investment in low-cost investment products would be linked to increased financial literacy. The findings did not support this hypothesis as opposed to the assumptions in the rational choice theory and aspects of the Theory of Planned Behaviour (TPB). There was no significant correlation between financial literacy and trust to handle investment, classification of risk in portfolio and high-risk preference of investments and self-reported and objective diversification. It was also not predictive of ETF adoption. These findings are a refutation of the classical belief that literacy is a rational base on which investors develop an efficient and well-diversified portfolio. TPB assumes that behavioural intentions are dependent on attitudes, subjective norms and perceived behavioural control; nevertheless, literacy, which can be viewed as the proxy of perceived control, did not have any significant impact on investment decisions. This would also suggest that peer influence and media stories (subjective norms) and investor attitudes (e.g., optimism, fear of loss) could have even stronger influences than objective knowledge. It is especially interesting to note that the null relationship with ETF adoption is the largest, since ETFs are universally understood in the academic literature as cost-effective vehicles. The result indicates that there is a possibility that the adoption can be stronger because of product awareness, access, or social proof rather than the awareness about costs or diversification benefits.
At least the rejection of H1 is not inconsistent with the main conclusions of the Behavioural Finance Theory which accentuate the fact that in the real world financial situations heuristics, mental shortcuts, and emotional states tend to prevail over the use of the rational knowledge. To use an example, low-literate investors can still exhibit their high level of confidence because of the overconfidence bias, but knowledgeable investors can fail to act on their knowledge because of the loss aversion or the perception of risk. The earlier research such as Lusardi and Mitchell (2014) tends to highlight the favourable role of literacy in diversification and product selection. The fact that there is a divergence in the current findings indicates that there is a chance that literacy has an interaction with other mediating and moderating factors. In a digitalised investment environment, the direct impact of literacy will be weakened by the power of other factors like financial press and social trading networks, as well as peer discussion networks, to influence investor discourses and expectations more significantly than factual knowledge. These findings are consistent with behavioural theory that indicates the growing influence of investment behaviour on the availability of information, media salience, and perceived norms and decreased effects on financial capability, at an individual level.
The second hypothesis stated that demographic variables such age, education, and income would have significant predictability of financial literacy. This assumption was not supported in the empirical findings. Age, education and income did not show significant correlations with the literacy and the regression model had a very small percentage in explaining the variance (1%). These results are a stark contrast to the previous studies, where the results are always similar showing that, older, more educated, and higher-income individuals have higher literacy levels (Disney & Gathergood, 2013; Bucher-Koenen and Lusardi, 2011). The deviation could also indicate the relative uniformity of the sample where all the respondents were already invested in the stock market. Active investors are also likely to have a minimum level of familiarity with the concepts of investment, which would possibly lessen demographic differences. Moreover, the modern world of learning, especially online trading platforms, online material, and social investment groups, could enable younger or less affluent people to obtain the information on finance beyond conventional learning or career choices.
Theoretically, these findings have issues on the human capital theory which considers education and income as important determinants of knowledge accumulation. They also put into doubt the Resource-Based View (RBV) according to which literacy is understood as a competence, which is a result of demographic resources. Rather, the findings indicate that the experiential learning, digital financial ecology and informal networks may progressively influence the process of literacy development at the expense of education and income. To policymakers, this highlights the importance of having financial education programs that cuts across demographic lines instead of having small demographic-focused initiatives.
The third hypothesis stated that the increased literacy could make a significant difference in the perceived knowledge of investors, their choice of diversification, and their perception of risk. Once again, the findings did not bear this expectation. Self-perceived knowledge or diversification behaviour, as well as risk-understanding scores, were not significantly based on literacy. Interestingly, the risk-understanding was also found to be closely related with reliance on the financial news and self-reported confidence. This indicates that alarmism can be influenced by informational signals more than literacy levels especially because of the financial media. This interpretation is supported in the literature on behavioural finance: availability bias, narrative framing, and anchoring tend to have a greater effect on risk perception than quantitative knowledge of risk-return relationships. Other researchers, including Almenberg and Dreber (2015), also indicate that subjective financial confidence often does not match objective knowledge, which converts the observation that literacy does not directly lead to perceived expertise to a research result.
Collectively, these three tests rejects imply that financial literacy though conceptually significant is not an independent source of investment behaviour in this sample. Rather, the results indicate that heuristics, social influence, media exposure and situational conditions are more powerful within an ecosystem of behavioural finance. Such results soften conforming anticipations of that augmenting literacy will contribute to superior investment results. In the case of academic theory enhance the argument in support of incorporating the views of behavioural finance in models of investor decision-making. To policymakers and practitioners, they insist that the support initiatives to investors should not be limited to educational programmes, but should incorporate behavioural nudges, easier access to products, better communication tools and mechanisms should be faced with bias-driven decision-making.
Policy and Theoretical Implications
The results of this research have important implications on the UK policy of retail investor and the overall theoretical explanation of financial decision-making. The findings indicate that financial literacy as a single variable cannot be strong to drive rational investment behaviour because behavioural indicators, especially media stories, website user interfaces, confidence bias and social influence are more effective to drive risk-taking, diversification and product selection. To regulators like the FCA, this highlights the necessity of ensuring no longer rely on purely literacy-based interventions but instead on behaviourally informed consumer-protection interventions. Features such as simplified disclosures, default diversification, early warning of risks, platform-based friction, and narrative-corrective communication can serve to counter overconfidence, herding and the exaggerating of risks by digital trading platforms. Local capability programmes based in the region (i.e. Manchester) where retail involvement is increasing at an alarming rate, yet behavioural susceptibility remains a factor would also promote resilience through the integration of education with the local socio-economic conditions. Theoretically, the results support the claims of behavioural finance models that heuristics and affective forces prevail over the classical finance assumptions of rational agents. They also doubt the predictive power of the human capital theory because demographic resources did not significantly influence literacy or behaviour. This implies that the results of investing are becoming increasingly influenced by a process of psychological biases, contextual cues and informational environments and integrated behavioural-financial models are likely to play a crucial role in future UK investor studies.
Conclusions and Recommendations
Conclusions
This research aimed to investigate whether financial literacy has a significant effect on investment behaviour of UK retail investors, in terms of risk-taking, diversification, cost-effective product decisions, and whether the demographic characteristics predict the level of literacy. In all analyses, the findings show that financial literacy was not a major influence of investor behaviour among the sampled population in Manchester. The level of knowledge did not forecast the risk preferences and the effects of diversification and neither did it affect the use of low-cost products like ETFs. Equally, age, education, and income did not have significant correlation with literacy, which conflicts with human capital theory and most of the financial literacy literature. Such results point to the idea that the behaviour of investors is impacted not only by formal knowledge but also rather by behavioural, situational and informational factors, such as confidence, exposure in the media, peer effects and platform-based signals. The results indicate the increased relevance of the behavioural finance views to the retail investing in the UK digital context. The implications of the results to policymakers and financial educators are that literacy-based interventions themselves might not be enough to help facilitate improved financial decision-making, and that more integrated methods that involve the use of behavioural nudges, investor protection mechanisms, and better access to transparent and cost-effective products may be necessary.
Limitations
Some limitations to this study must be taken into consideration during the interpretation of the results. First, the cross-sectional design only records the investor behaviour at a particular time thus allowing no opportunity to evaluate how the literacy or behaviour can vary with experience or the altering market conditions. Second, the use of self-reported data presents the possibility of bias, including the social desirability bias and false memory, especially on such variables as diversification and confidence. Third, the sample size is 100 retail investors in Manchester, and thus, it is not possible to generalise the findings to larger populations in the UK with varying socio-economic, cultural, or regional backgrounds. Fourth, financial literacy scale measures fundamental conceptual knowledge, and this might not be completely relevant to applied or contextual financial literacy. Lastly, behavioural biases or platform influences are not directly managed in the study, which could be part of the weak literacy-behaviour relationships found.
Recommendation for Future Studies
Future research ought to expand this research by incorporating longitudinal designs in order to follow the development of financial literacy and investment behaviour over time, particularly as investors accumulate some experience or are exposed to varying market conditions. Bigger and more varied samples and areas of the UK should also be used by the researchers to be able to obtain socio-economic and geographic heterogeneity which cannot be disclosed by single-city research. The measurement of behavioural bias which include validated scales of overconfidence, herding or mental accounting would allow more accurate modelling of the interaction between psychological factors and literacy in influencing investment decisions. The platform-level variables that may be incorporated in the future research include effects of digital interfaces, social media cues, and algorithmic nudges. Experimental or mixed methods design can also be used in the discovery of causal mechanisms of investor decision making. Lastly, the applied financial capability should be examined instead of simple literacy to understand how usability knowledge can be translated in realistic investment decisions.
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