Financial Asset Management Using Artificial Neural Networks

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.

The Association between Subjective and Objective Financial Knowledge: Path Analysis to Investor Behavior by Age

Financial literacy affects stock market participation, as well as individuals’ age, gender, income, and education level. However, measuring financial literacy is more appropriate to identify individuals with strong knowledge of finance rather than average individuals with general knowledge. This could be problematic to identify general participation of the stock market and investment as more individuals are now participating without having to have such knowledge. This study explored how individuals’ subjective financial skills and well-being affect investment participation by age. Overall, males are likely to participate more in both retirement and non-retirement investment. In between the boomer generation and younger group, the younger generation who reported participating in a non-retirement investment, such as stock market were having a higher score on subjective financial well-being (STDYX = .052, 95% CI [.07, .08]; p < .05). Importantly, among the older group, subjective financial skill score becomes a predictor of participating stock market (STDYX = .09, 95% CI [.01, .17]; p < .05). As the result suggest, while younger participants focus more on financial well-being, such as having security on finances, when they are participating in a non-retirement investment, whereas older adults are likely to invest based on their beliefs on financial skills regardless of secured finances. A retirement plan has shifted toward less on savings and more on investing. Older adults are now interested more in participating in investments, such as the stock market than the young population, and the proper preparedness for those older adults in participating in the investment is needed.

The Effects of Income Tax Timing on Retirement Investment Decisions

In an online experiment, the immediate (Roth) versus deferred taxation of retirement income affects taxpayers' investment decisions such that tax-deferred plan investors under-adjust for future tax burdens and overestimate their future wealth compared to Roth investors. When presented with a specific, after-tax monetary goal, Roth account holders invest more in higher-risk, higher-return assets than tax-deferred account holders. We investigate four aspects of this investment context that could alleviate these differences: 1) implementing a "do-your-best" goal, 2) reframing specific goals in pre-tax dollars, 3) explicitly prompting investors to estimate future tax burdens, and 4) providing performance feedback. These interventions reduce differences between Roth and tax-deferred investor behaviors, but do not entirely close the gap on their own. In combination, reframing goals and prompting future tax estimations encourage tax-deferred account holders to invest in risky assets to the same degree as Roth investors only when paired with performance feedback.

Financial Asset Management Using Artificial Neural Networks

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.

Affectivity and riskiness of retirement investment decisions

PurposeDrawing on broaden-and-build theory and promotion- and prevention-focus theory, the authors examined the role of positive and negative affectivity (PANA) on the riskiness of investment decisions. The authors also examined the mediating impact of financial knowledge network intensity (i.e. the level of communication with financially literate others in employees' social network) on the PANA—riskiness of investment decisions relationship.Design/methodology/approachStudy 1 used a sample of undergraduate students and operationalized risk using a hypothetical investment scenario. Study 2 replicated and extended the Study 1 findings using employees and operationalized risk using their real-world investment allocations.FindingsBoth Studies 1 and 2 provided support for the negative direct relationship between NA and the riskiness of investment decisions. Study 2 found PA was marginally positively related to the riskiness of investment decisions. Financial knowledge network intensity mediated the relationship between NA and the riskiness of investment decisions in Study 2.Research limitations/implicationsThe findings suggest that employees who see the world in a generally negative light tended to have weaker financial knowledge networks, and this may be one mechanism that explains why they make low-risk investments.Practical implicationsFinancial knowledge networks can provide access to critical information regarding investment opportunities. Socialization training or social mixers can be used to help employees build and improve their financial knowledge networks.Originality/valueThe authors integrate the research on PANA, social networks, and investment decisions to illuminate the social network processes that explain how affectivity impacts the riskiness of retirement investment decisions.

The role of financial literacy in retirement investment choice

Research background: Preparation for retirement is a major concern for the people in the workforce as they have to encounter considerable difficulties in making the right investment decisions for their retirement. Purpose of the article: This research extends the literature on personal finance by investigating the impact of both financial literacy levels and pension knowledge on employees’ investment choice decision for their retirement, while in previous literature the role of these factors has mainly been explored separately. Methods: To conduct the research, a survey questionnaire was applied to collect data in three main regions of Vietnam comprising Northern, Central and Southern Vietnam. Data collection was made in 2018, in which 427 valid questionnaires were used for data analysis from 700 questionnaires. Two estimation methods are employed for analysis in this study, including a linear probability model (LPM) and two-stage least squares (2SLS) model. The findings of this research remain significant after the Two-Stage Least Squares (2SLS) regression model is used as an estimation technique to eliminate potential bias caused by endogenous problems. Finding & Value added: The results show that basic financial literacy level and pension knowledge are principal factors which significantly increase the probability of exercising retirement investment choice of employees, while advanced financial literacy level factor has a significant effect on choosing growth investing options for their retirement. Further, this research finds that there is no correlation between employees’ financial risk tolerance and their retirement investment choice. Furthermore, the study proposes and offers new evidence that pension knowledge is a decisive factor providing employees with encouragement to exercise retirement investment choice and those who consult with financial advisors tend to take part in growth investing option.

Switching Costs and Competition in Retirement Investment

How do different switching costs affect choices and competition in a private pension system? I answer this question in a setting in which variation in employment status allows me to identify two switching costs that jointly affect enrollees’ decisions: the cost of evaluating financial information and the cost of the bureaucratic process that enrollees must navigate when switching. I use this variation to estimate the different switching costs and study their impact on competition among pension funds. I find that though eliminating all switching costs decreases equilibrium fees the most, eliminating either switching cost decreases fees significantly. JEL (D14, G23, J26, J32, O15)

Actuarial Modeling and Analysis of the Hong Kong Life Annuity Scheme

The Hong Kong Mortgage Corporation (HKMC) Limited, which was established in March 1997 and is wholly owned by the government of the Hong Kong Special Administrative Region, has a major mission to develop and provide different financial retirement instruments to Hong Kong residents to help address the income poverty of retirees. In June 2017, HKMC Annuity Limited, a wholly-owned subsidiary of the HKMC was incorporated to implement a new life annuity scheme which would be launched by mid-2018 to cater for the needs of cash-rich Hong Kong old age residents. The objective of the scheme is to provide an additional financial retirement planning option with minimum credit risk and a certain level of liquidity to the elderly by turning lump-sum premiums into lifetime streams of monthly income at a reasonable and stable return rate. In this paper, we establish an actuarial framework to model this life annuity scheme. The framework enables us to estimate the monthly annuity payments one might receive for a certain amount of investment. It also allows us to analyze the risk entailed in the product, thus shedding light on how the underlying risk can be managed through product design. Our findings will help potential subscribers to understand the scheme and decide whether this scheme should be included in their retirement investment portfolios when it is launched in 2018.