ABSTRACT Financial planners and advisors routinely translate downside protection features of retirement income products into client facing language. Across four studies, we examine whether framing a zero downside as “gain nothing” or “lose nothing,” compared with “lose $1,” affects willingness to invest in an indexed annuity. In three online experiments, investment intention is lower when the downside is framed as “gain nothing” than when it is framed as “lose $1,” but higher when the downside is framed as “lose nothing” than when it is framed as “lose $1.” Positive affect statistically mediates these effects, and risk–reward beliefs moderate the comparison between gain nothing and lose $1, and promotion focus moderates the comparison between gain nothing and lose nothing. The central gain nothing versus lose nothing effect also emerges in a sample of retail investors. These findings contribute to financial planning research by identifying downside protection wording as an advisor client communication variable that can change affective response and investment intention even when expected values are identical or nearly identical. The results have implications for financial planners, annuity providers, disclosure designers and marketers seeking to communicate principal‐protection features clearly and ethically.
ABSTRACT With the advent of Artificial Intelligence (AI)‐driven tools in financial services and planning, there is concern about the role of AI‐driven financial advice and openness to these in relation to individual financial behaviors, particularly regarding clients' interpretation of this advice and its applicability to their financial situations. Therefore, this study, using data from the 2024 National Financial Capability Study (NFCS), investigated the relationship between the willingness to use AI for financial advice and financial behaviors (short and long term). Furthermore, this study tested how financial knowledge (objective and self‐assessed) may moderate the aforementioned relationship. Findings showed that willingness to use AI relates to short‐ and long‐term financial behaviors differently, and that only self‐assessed financial knowledge significantly moderated these associations. These findings have implications for individuals and financial professionals working with clients and highlight the importance of perceived financial knowledge in how individuals translate AI‐based advice into financial behaviors. This study suggests that while AI tools in financial planning are beneficial, they cannot eliminate the role of human financial advisors.
ABSTRACT Since the U.S. dollar has been operating as a fiat currency, there has been a strong link between U.S. government deficits as a percentage of GDP and forward‐looking equity returns. As federal deficits get larger, the expected return to the market over the subsequent 10 years increases. This effect is material. For each 1% increase in the deficit‐to‐GDP ratio, annualized returns increase by at least 1.29%, all else equal. However, this forecasting ability of the deficit‐to‐GDP ratio does not make it a market timing mechanism. Rather, it is a conditioning variable that helps determine the appropriate expected return for the market for financial planning and asset allocation.
This study investigates the relationships between individual preferences, personality traits, abilities, and multiple indicators of financial well‐being (FWB). Employing survey data from the Understanding America Study (UAS), we analyze FWB across its different dimensions, including a composite scale, single‐item perceptions of FWB, objective outcomes indicative of FWB, and positive financial behaviors. Logistic and OLS regression results show that time preferences, financial self‐efficacy, and financial literacy are significantly related to many different FWB indicators. Analysis of interaction effects reveals that financial literacy has an important amplifying role in relation to the individual discount rate, financial self‐efficacy, and income. This study provides insights into how financial planning practitioners can incorporate their clients' time preferences, confidence, and financial literacy into individualized strategies to help them reach their financial goals.
Artificial intelligence (AI) can support financial advisors in developing the communication skills essential to client trust and financial wellness. This paper presents a conceptual framework—the AI‐MIFA (artificial intelligence–motivational interviewing for financial advisors) feedback tool—an AI‐powered training model designed to strengthen empathetic listening skills and guide future research on how AI can enhance advisor communication. As a proof of concept, AI‐MIFA delivers personalized, real‐time feedback on advisor–client conversations. Grounded in motivational interviewing (MI)—an evidence‐based communication method—AI‐MIFA analyzes meeting transcripts to evaluate core elements of empathy, including reflective statements and the mitigation of the “fixing” reflex. The paper outlines potential benefits such as scalable training and performance tracking while also addressing limitations, including the complexity of MI and the need for field‐specific validation. Together, these ideas position AI‐MIFA as a conceptual foundation for scaling empathy and communication skill development in financial advising—core components of financial wellness.
Over the past few decades, there has been substantial growth in “private” financial markets, which generally have restrictions on who can participate and lower regulatory requirements. A primary way for individuals to qualify for private investments is to be an “accredited investor,” typically meaning that they meet certain income, wealth, or experience thresholds. We analyze novel, nationally representative survey data to characterize accredited investors in the United States and show how they differ from non‐accredited investors. Approximately 12.6% of the population qualifies as an accredited investor, primarily based on net worth. Most accredited investors (75%) qualify under only a single threshold. Accredited investors have higher overall income, educational attainment, and indicators of financial sophistication than those who do not qualify. Finally, they are more likely to own various asset types, including 4.3% who report owning private market securities, as compared to 1.1% of non‐accredited investors. We discuss implications for policymakers and practitioners.
This study provides the first formal psychometric validation of the Retirement Income Literacy Scale (RILS) and examines the relationship between professional designations and specialized knowledge among financial advisors. Using a two-study design, we first validate the 38-item RILS with 3745 American consumers aged 50 to 75, demonstrating strong internal consistency and confirming a unidimensional factor structure through exploratory and confirmatory factor analyses. The validated scale exhibits good model fit and adequate construct validity. In the second study, we assess retirement income literacy among 906 financial professionals using the validated RILS. Results reveal that financial advisors with professional designations score significantly higher than those without designations, representing a practically meaningful 9.37-point difference. LASSO regression analysis identifies the Certified Financial Planner (CFP) credential as the strongest predictor among professional designations. A curvilinear relationship emerges between the number of designations held and RILS scores, indicating diminishing returns to multiple credentials specifically for retirement income literacy knowledge. These findings provide empirical support for both Signaling Theory and Human Capital Theory in the financial planning context, demonstrating that professional designations function as meaningful indicators of specialized knowledge. The study contributes a validated assessment tool for retirement income literacy research and offers evidence-based insights for professional development in financial planning, with implications for advisor credentialing, consumer choice, and industry standards. FPR classification codes:I.5. I.7.
This study investigates the impact of narrative R&D disclosure characteristics—readability, sentiment, and quantity—on stock return volatility. A comprehensive longitudinal regression model that includes these three characteristics, along with their interactions with R&D investment intensity, outperforms the other models. The readability of narrative R&D disclosures significantly affects volatility, with more readable disclosures associated with reduced fluctuations. Additionally, readability moderates the effect of R&D investment intensity on volatility. No direct effect of disclosure sentiment on stock return volatility is observed, but disclosure sentiment influences the relation between R&D investment intensity and stock return volatility. Finally, there is a strong positive correlation between the quantity of R&D disclosures and stock return volatility, suggesting that excessive R&D information tends to increase stock return fluctuations. The study provides practical implications for both investors and firms. It suggests that investors may consider R&D disclosure characteristics to better assess the risk of stock return fluctuations when selecting shares of R&D-intensive firms and offers guidance for firms on delivering clearer and more balanced R&D disclosures to help reduce market volatility.
