Risk Tolerance Research Articles

Predicting Mutual Fund Stress Levels Utilizing SEBI’s Stress Test Parameters in MidCap and SmallCap Funds Using Deep Learning Models

Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our study utilizes AMFI datasets from February 2024 to September 2024 which consisted of 14 variables. Among these, the primary variable identified in grading mutual funds is the stress test parameter, expressed as number of days required to liquidate between 50% and 25% of the portfolio, respectively, on a pro-rata basis under stress conditions as a response variable. The objective of our paper is to build and test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors. The results suggest that the simpler neural network model architectures show higher accuracy. We used Artificial Neural Networks (ANN) over other machine learning methods due to its ability to analyze the impact of dynamic interrelationships among 14 variables on the dependent variable, independent of the statistical distribution of parameters considered. Predicting stress levels with the highest accuracy in MidCap and SmallCap mutual funds will benefit investors by reducing information asymmetry while allocating investments based on their risk tolerance. It will help policy makers in designing controls to protect smaller investors and provide warnings for funds with unusually high risk.

A data privacy survey on personal identifiable information (PII) left on rental vehicle infotainment systems

In the current automotive ecosystem, the trend of pairing mobile devices to connected vehicles is gaining momentum, providing a vast number of benefits such as hands-free driving and remote vehicle control. However, along with these conveniences arises the issue of data accumulation, ranging from vehicle diagnostics to personal identifiable information (PII). The problem emerges when a consumer rents a vehicle, pairs their mobile device to the infotainment system, and neglects to remove their device prior to returning the vehicle. This oversight can potentially expose vulnerabilities with the current renter’s PII for subsequent renters to exploit. Research indicates that renters often overlook the deletion process prior to returning the rental vehicle and are unaware of whose responsibility it is to perform this task. In this survey, we investigated the experiences and perceptions of a group of consumers who have previously rented vehicles. We wanted to know if the participants were aware they were responsible for deleting their mobile device from the rental vehicle before returning it, the renters’ importance and tolerance for risk if they overlooked the deletion process leaving their shared data on the infotainment system for subsequent users, and if they were aware who was responsible for deleting their shared data. Lastly, we explored if the participants supported an automated solution to perform this manual deletion process. The goal of this paper is to demonstrate the significance of safeguarding PII in connected vehicles and advocate for the adoption of an automated solution to mitigate this privacy risk.

PENGUJIAN DECISION MAKING MODEL DENGAN MEDIASI FINANCIAL LITERACY DAN RISK TOLERANCE DALAM MEMPREDIKSI PERILAKU INVESTASI MAHASISWA

This study aims to test the investment decision model among college students by integrating financial literacy and risk tolerance as mediating variables. The sample in this study were students who had invested. The data analysis technique used in this research is Structural Equation Modeling (SEM). Testing includes measurement models (outer model) and structural models (inner model). The results of this study indicate that mental accounting, herding behavior, and risk preference directly affect investment interest. Risk tolerance can mediate the effect of herding behavior on investment interest. In addition, financial literacy can mediate the influence between mental accounting and risk preference on investment interest. Furthermore, interest is the dominant factor in driving investment decisions. This research contributes theoretically by expanding the investment decision-making model, especially among students.

“I just want to get it done”—a mixed-methods study on how people undergoing hallux valgus surgery perceive risk

BackgroundThis scoping study explored patients’ preoperative perspectives of their expectations, their tolerance for risk, and what factors may influence their decision to undergo hallux valgus surgery.MethodsRecently consented patients for hallux valgus surgery at a tertiary public hospital were recruited for this mixed-methods study. Firstly, semi-structured interviews were undertaken to explore attitudes, perceptions, feelings about risk, and expectations of surgery. Following the interview, questionnaires assessing demographics, pain, and health-related quality of life were completed. The data were analysed using NVivo and SPSS.ResultsSeven participants (Five women; mean age 63, range 47–79, years) were recruited for this study. Foot pain was rated as moderate to high, but catastrophisation and sensitisation were low. Surgery was viewed a definitive resolution and expectations were high. Participants with higher pain had higher expectations and the experience of others was used to judge the chance of a successful outcome.ConclusionPeople with high foot pain and hallux valgus may have higher expectations of a successful operation. Expectations and acceptance of risk in people having surgery for hallux valgus are optimistic.

Women’s Investment Decision: An Examination of Risk Tolerance and Financial Literacy

“This study investigates the impact of Digital Financial Literacy (DFL), Access to Technology (AT), and Socioeconomic Status (SES) on Financial Decision-Making (FDM). Using a sample of 147 participants, the research employs regression analysis to reveal significant positive relationships between DFL, AT, and SES with FDM. Findings indicate that higher levels of digital financial literacy and improved access to technology enhance individuals' ability to make informed financial decisions. Furthermore, SES is shown to play a crucial role in shaping financial behavior. The study's insights underscore the need for targeted educational initiatives and policies aimed at improving digital financial literacy, particularly among economically disadvantaged populations. This research contributes to the understanding of factors influencing financial decision-making and offers recommendations for stakeholders aiming to foster financial inclusion in an increasingly digital landscape.”

Predicting Portfolios Using Fuzzy Logic and Optimization Techniques: A Case Study of BSE-IT Listed Companies

Stock price trends were considered in this study, the companies in the BSE-IT index, with a view to optimizing portfolio allocation based on risk tolerance. The GARCH model has been applied here to allow an investigation into the volatility of the stock returns and to set up different clusters using K-Means clustering that group together companies owning the same attributes related to volatility for strategic classification. Data collection and processing occur between April 1, 2014, and March 31, 2024, for reasons of reliability and relevance. Descriptive statistics report on the variance of stock performance across companies; different risk-return profiles are thus exposed. The GARCH model measures each stock's long-term variance, along with sensitivity to past shocks, and overall volatility persistence, making this an excellent inventory for risk assessment. K-Means clustering groups companies based on similar volatility patterns, thereby informing portfolio construction. Findings present how clustering with econometric modeling may assist in the production of optimal portfolios that depend on specified risk capacities, thus efficiently managing and implementing an IT investment strategy.

The Determinants of Financial Risk Tolerance and Portfolio Allocation: Have the Covid-19 Pandemic and the Ukraine War affected our Risk Tolerance?

This study focuses on examining the factors affecting financial risk tolerance and portfolio allocation among working-age highly educated adults in Bosnia and Herzegovina. It also explores how the Covid-19 pandemic and the Russian invasion of Ukraine have affected these financial decisions. We employed multinomial logistic regression, incorporating subjective and objective risk tolerance measures, and considered various socio-demographic and psychological risk-taking factors. The findings reveal that risk attitudes, along with investment knowledge and experience are the primary determinants of financial risk tolerance, while socio-demographic variables have minimal effect. Moreover, the recent financial turmoil resulting from the Covid-19 pandemic and the Russian invasion of Ukraine substantially influenced individual's subjective risk tolerance, with no impact on the objective risk tolerance, which is our main contribution to the existing literature. This study contributes to understanding of financial risk tolerance in emerging markets, shedding light on the key determinants and impact of significant global events.

Does Society Influence the Gender Gap in Risk Attitudes? Evidence from East and West Germany

Previous international research suggests that women are on average more risk averse than men. This gives rise to the question of whether the gender gap in risk attitudes is shaped by the social environment. We address this question by examining risk attitudes among East and West Germans. Even many years after reunification, East Germans have much more equal gender roles than West Germans. Thus, if the gender gap reflects socially constructed norms, it should be smaller among East Germans. Using data of the German Socio-Economic Panel (SOEP), our empirical analysis confirms this prediction. Specifically with respect to career and financial matters, the gender gap in risk tolerance is smaller among East Germans. We find no evidence that the East German gender gap has converged to the higher West German level after reunification. By contrast, our estimates provide some cautious indications that the West German gap has narrowed over time.

Factors Affecting Investment Decisions On Millennials And Gen Z

Since 2019, capital market investors have surged, primarily those under 40 years old, called millennials and Gen Z. However, the financial literacy of Indonesians is low, with victims of fictitious investment cases increasing yearly. This study aims to determine the effect of Financial Literacy (FL) and Investment Experience (IE) on Investment Decisions (ID) with Risk Tolerance (RT) as a mediating variable. The method used is Partial Least Square and involves 185 stock investors aged 18 to 40 years domiciled in Java and Bali, using a purposive sampling technique and the Hair formula. This study found that FL and IE have a significant and positive effect on ID, FL and IE have a significant and positive effect on RT, RT has a significant effect and positive on ID, FL and IE have a significant and positive effect on ID through RT.

Impact of behavioral finance on financial decision making

This paper highlights the disproportionate effect of behavioral finance on investor’s investing decisions. While making any financial decisions, one easily gets influenced by psychological and emotional factors. Past individual experiences, tendency or opportunity for herd behavior, personal judgment, overconfidence in one's abilities, aversion to feelings of regret, reliance on advice from experts, and last but not least, something known as the disposition effect—by which investors tend to hold on to losing investments and professionally sell profitable ones too early. The impact of these factors on the investment decision-making process varies; on the one hand, they may enhance the quality of the decision-making process by aligning investments with risk tolerance and financial goals. However, on the other hand, behavioral factors can be a cause of inefficiency if investment decisions are based more on emotions than objective analysis. In our recent research, we questioned 187 investors, using a questionnaire from whom we acquired crucial information regarding the impact of behavioral finance on financial decisions. The primary objective of the questioning was to determine the effect of behavioral finance on financial decision-making. The secondary objective was to suggest some methods for making one aware of the repercussions of impulsive or emotional decision-making and making prudent investments.

A Critical Review of Overconfidence in Investment Decisions: A Bibliometric Analysis

The precedence of behavior bias-based studies with respect to investment is quite prevalent. Hence, a bibliometric analysis of available literature may lead to fresh topics in association with overconfidence bias. The literature ranging between 1970 and 2024 were scanned using terms related to overconfidence in finance as keywords to reach the related publications. After screening, 1327 publications related to overconfidence, 728 documents were identified. Martin Weber emerged as the most cited author. SSRN electronic journal, qualitative research in financial matters and review of behavioral finance have published most cited papers in the field of overconfidence and investment decision. The United States have published highest number of papers and citations, while Germany, India and China have also emerged as nations with highest citations. The United States of America has published highest number of papers with citations. Germany, India and China have also emerged as nations with highly citated papers. Considering year on year growth in the publications, more work may be expected from the arena of overconfidence, and authors of the present study have taken the liberty to suggest a few possibilities that can be explored. Studies with risk tolerance, after pandemic impact, experience and financial/tax literacy, loss aversion and availability bias-based studies may be considered by researchers in accordance to overconfidence.

Policy approaches to increase external investment by US pension funds in African countries

AbstractLimited external investment has dampened the prospect for a prosperous African continent that capitalises on economic integration and its demographic dividend. This article explores US pension funds as a candidate for increased investment by framing it through an institutional lens and the scholarly understanding of the risk–return dynamics in private capital allocation. The original research is centered on interviews of senior‐level professionals who work in the US pension fund investment ecosystem as well as experts at multilateral organisations. It identifies the primary investment impediments articulated in interviews and offers approaches policymakers in OECD member countries could undertake to increase the scale of US pension fund investment in African countries. These include: (1) Transform guarantee programs in size, scope, and risk tolerance; (2) Establish large pass‐through investment vehicles; (3) Focus resources to improve capital markets in African countries; and (4) Mandate development organisations to coordinate and proactively collaborate with firms in the US investment ecosystem.

Evaluating robo-advisors through behavioral finance: a critical review of technology potential, rationality, and investor expectations

The mini review assesses the value propositions of robo-advisors through the lens of behavioral finance. Despite their promise of data-driven, rational investment strategies, robo-advisors may not fully replicate the personalized service of human financial advisors or eliminate human biases in decision-making. A content analysis of 80 peer-reviewed articles and publications was conducted, focusing on the intersection of financial technology and behavioral finance. Literature was retrieved using The Chicago School University Library's OneSearch and the EBSCO host database, with key terms including “robo-advisor,” “investment behavior,” “risk tolerance,” “financial literacy,” and “affective trust.” The review identifies four key limitations of robo-advisors: (1) their inability to replicate the service-relationship of human advisors; (2) the presence of human bias in supposedly rational algorithms; (3) the inability to minimize market risk; and (4) their limited impact on improving users' financial literacy. Instead, robo-advisors temporarily compensate for a lack of financial knowledge through passive investment strategies. The findings suggest that integrating behavioral finance principles could enhance the predictive power of robo-advisors, though this would introduce additional complexities. The review calls for further research and regulatory measures to ensure that these technologies prioritize investor protection and financial literacy as they continue to evolve.

Necessary and sufficient conditions for open-loop equilibrium portfolio for a DC pension plan with piecewise linear state-dependent risk tolerance

Wang, Chen, and Yang (2023) investigated the equilibrium behavioral strategy for a defined contribution (DC) pension plan during the accumulation phase and obtained the explicit expressions for the suboptimal equilibrium behavioral strategy. However, the authors failed to obtain the optimal solution and did not completely solve the pension investment problem with piecewise linear state-dependent risk tolerance. The primary objective of this article is to solve an improved version of the above DC pension plan problem thoroughly. We investigate the open-loop equilibrium portfolio for a DC pension plan and derive necessary and sufficient conditions for the open-loop equilibrium portfolio through coupled forward-backward stochastic differential equations (FBSDEs). The uniqueness of an equilibrium portfolio is obtained by decoupling FBSDEs. Finally, the economic significance and implications of the obtained open-loop equilibrium portfolio are illustrated from the theoretical perspective.

Psychological Determinants of Investment Decisions: Analyzing Financial Behavior in Personal Investments

Understanding the psychological factors that influence investor behavior is critical in the dynamic world of financial markets. Financial conduct encompasses the decisions and behaviors individuals exhibit in managing their finances, including investments in various asset classes. Factors such as risk tolerance, cognitive biases, emotional influences, and financial knowledge significantly shape investment outcomes. Gaining long-term financial success requires mastery over these behavioral aspects. This study investigates the influence of three psychological factors—information asymmetry, problem framing, and risk propensity—on the investment decisions of 220 active investors trading on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). A quantitative research approach was employed, utilizing a structured questionnaire to collect data. Statistical analyses, including regression and correlation analysis, were used to assess the relationships between these psychological variables and investment behaviors. The results reveal significant correlations between psychological factors and investment decisions. Information asymmetry, problem framing, and risk propensity were found to strongly influence individual investor choices. These findings shed light on the intricate role that cognitive biases and psychological processes play in shaping financial decision-making. The study's findings offer valuable insights into the psychological drivers behind investment behavior. By highlighting the impact of these factors, the research contributes to both academic understanding and practical applications for financial professionals. The results underscore the importance of enhancing financial literacy and investor education, enabling more informed decision-making and promoting improved financial outcomes.

A Comparison of Financial Risk-Tolerance Assessment Methods in Predicting Subsequent Risk Tolerance and Future Portfolio Choices

This study explores the effectiveness of various methods for measuring risk tolerance, with the aim to better understand the risk-taking attitudes and behaviors of financial decision-makers. Using data collected between October 2020 and March 2021, the research investigates three key areas: (a) the stability of risk tolerance over a six-month period, (b) the individual and household characteristics that predict future risk tolerance, and (c) the predictive accuracy of various risk-tolerance assessment methods in relation to portfolio choices made by financial decision-makers. The results show that risk-tolerance scores derived from a psychometrically developed scale provide the most accurate insights into future risk-taking attitudes and portfolio decisions. For those looking for a simple way to assess both current and future risk tolerance and portfolio choices, a stated-preference item can be effective. Although less consistent, a revealed-preference test can also be used to predict risk tolerance and risk-taking behavior. Findings provide guidance for financial decision-makers and financial advisors by comparing the key features of the three primary risk-tolerance assessment methods evaluated in this study. The study also establishes a foundational basis for selecting the most appropriate evaluation approach, based on the variables identified in the findings.

Associations between polygenic scores for cognitive and non-cognitive factors of educational attainment and measures of behavior, psychopathology, and neuroimaging in the adolescent brain cognitive development study.

Educational attainment (EduA) is correlated with life outcomes, and EduA itself is influenced by both cognitive and non-cognitive factors. A recent study performed a 'genome-wide association study (GWAS) by subtraction,' subtracting genetic effects for cognitive performance from an educational attainment GWAS to create orthogonal 'cognitive' and 'non-cognitive' factors. These cognitive and non-cognitive factors showed associations with behavioral health outcomes in adults; however, whether these correlations are present during childhood is unclear. Using data from up to 5517 youth (ages 9-11) of European ancestry from the ongoing Adolescent Brain Cognitive DevelopmentSM Study, we examined associations between polygenic scores (PGS) for cognitive and non-cognitive factors and cognition, risk tolerance, decision-making & personality, substance initiation, psychopathology, and brain structure (e.g. volume, fractional anisotropy [FA]). Within-sibling analyses estimated whether observed genetic associations may be consistent with direct genetic effects. Both PGSs were associated with greater cognition and lower impulsivity, drive, and severity of psychotic-like experiences. The cognitive PGS was also associated with greater risk tolerance, increased odds of choosing delayed reward, and decreased likelihood of ADHD and bipolar disorder; the non-cognitive PGS was associated with lack of perseverance and reward responsiveness. Cognitive PGS were more strongly associated with larger regional cortical volumes; non-cognitive PGS were more strongly associated with higher FA. All associations were characterized by small effects. While the small sizes of these associations suggest that they are not effective for prediction within individuals, cognitive and non-cognitive PGS show unique associations with phenotypes in childhood at the population level.

Multivariate Decision Tree-Oriented Early Warning Method for College Students’ Psychological Crisis Behavior

To prevent the occurrence of psychological crisis behavior and help college students grow from the crisis is not only an important issue related to college students’ mental health, but also a very necessary and urgent practical issue related to college students’ family happiness, college talent training, and social harmony and stability. Therefore, a multivariable decision tree-oriented early warning method for college students’ psychological crisis behavior is proposed. Using the laser beam emitted by the sensor as a reference standard, the psychological crisis behavior data is extracted, converted into different formats, divided into linear regions, and used to build a multivariate decision tree model. The model is used as the judgment processing target, and the judgment equation of mental health status data is established. The status data is divided into different processing groups as the processing object, calculate the maximum state value of the state data in the two data combinations to obtain the optimal early warning result, and finally realize the early warning of psychological crisis behavior. The experimental results show that the final success rate of early warning obtained by this method is high, which can improve the efficiency of early warning; the number of missing data sets was the least, and most of the subjects’ psychological risk tolerance was at the medium level.

Determinants of Crypto Investment Decisions: Ra-tional Behavior or Irrational Behavior?

The current phenomenon is that crypto asset investment is considered high risk, yet the number of crypto asset investors is increasing. This research investigates the determinants of crypto investment decisions: driven by rational or irrational behavior, specifically testing the moderating role of social influence on the determinants of investment decisions. The research combines technological factors (crypto platforms) and psychological factors (herding behavior FOMO (Fear of Missing Out)) with external factors of social influence a moderating variable. This study uses quantitative research methods and primary data from respondents who are crypto asset investors domiciled in Indonesia. The sample consisted of 124 respondents consisting of 95 male respondents and 29 female respondents. Statistical analysis including analysis of the Outer Model, Inner Model and direct effect and Moderated Regression Analysis was carried out using SmartPLS 0.3 software. The results showed that crypto platforms, financial literacy, herding behavior, FoMO had an effect on investment decisions, and social influence was able to moderate the influence of crypto platforms, financial literacy, herding behavior, FoMO on investment decisions. Suggestions given for further research are: develop further research models that can represent the determinants of investment decisions in crypto by using other research variables in predicting their influence on investment decisions such as regret experience, attitude, risk tolerance, etc.

The Usage of Paylater Among College Students: The Role of Self-Control, Hedonistic Conformity, and Family Financial Education

Research aims: The increasing phenomenon of the unwise use of Paylater in society, even among college students, is interesting to review further. This study aims to test the role of self-control and hedonistic conformity with the moderation of family financial education on the use of Paylater by university students.Design/Methodology/Approach: Primary data was obtained through a survey involving 117 respondents who have or are currently using the Paylater service. Furthermore, the data was processed using the Partial Least Squares (PLS) method.Research findings: This study produced the following findings: hedonistic conformity was proven to have a positive effect on the use of Paylater in the full sample and in two sub-samples. This finding is in line with the TPB modification used in this research, hedonic conformity as a proxy for subjective norms has been shown to contribute positively to the use of Paylater among colleege students. Meanwhile, the role of family financial education has not been proven to moderate the regression path of self-control and hedonic conformity on the use of Paylater.Theoretical Contribution/Originality: This study contributes to enriching the literature on personal financial management, especially regarding the use of Paylater based on internal and external factors of students.Practitioners/Policy Implications: These results offer recommendations for personal financial management, especially for university students, to avoid the risk of being trapped by Paylater by paying attention to their social community and prioritizing family financial education.Research Limitations/Implications: Respondents were mostly undergraduate students, so this study cannot explain the dynamics of financial behavior among undergraduate and postgraduate students who have different demographic characteristics. Therefore, future research needs to analyze different sample groups to enrich the scientific perspective. Furthermore, to provide depth of analysis, this study proposes to add other internal aspects of the individual to the TPB theoretical framework (e.g.: perception of usury and financial risk tolerance). Last but not least, regarding the development of indicators from the family financial education variable that does not yet include personal debt management education items, hence this indicator can be added as a proxy for this variable to obtain in-depth measurements.