Doctor of Philosophy / Department of Human Ecology-Personal Financial Planning / Sonya L. Lutter / This study explored the influence of present-fatalism, present-hedonism, and future time perspectives on financial net worth. Time perspective has been shown to influence many behaviors, both non-financial and financial, but this is the first study that evaluated the relationship between time perspective and net worth. Net worth was divided into two variables, a dichotomous variable indicating those who had a negative net worth (defined as a net worth less than or equal to zero) and a continuous variable of the actual dollars of net worth of those who had a positive net worth (defined as a net worth greater than zero). Developing a separate negative net worth variable allowed this study to expand on prior research that focused solely on that aspect of net worth (Chen & Finke, 1996; Mountain & Hanna, 2012). Data was taken from the National Longitudinal Survey of Youth 1979 (NLSY79), using results primarily from the 2014 survey. A logistic regression was used to evaluate the negative net worth variable (Model 1) while an ordinary least squares (OLS) regression was used to analyze the influence on positive net worth (Model 2). This study found that present-fatalism increased the odds that an individual would have a negative net worth, while a future-orientation would decrease those odds. It found that present hedonism and future-orientation contributed to having a positive net worth. Model 1 had a Nagelkerke R Square of .367 and was able correctly to classify 77.2% of those who had a negative net worth (compared to 67.6% using only the intercept). In addition to time perspective findings, several control variables were incorporated into the study. Those who had a higher current income, who were male, who were married, and who owned a home had lower odds of having a negative net worth. Those who had a college education had lower odds of having a negative net worth compared to those with a high school diploma, while those who only attended grade school had greater odds of having a negative net worth compared to high school graduates. Blacks and Hispanics had greater odds of having a negative net worth compared to Whites. Risk tolerance, parent socio-economic status, and age were not significant predictors of negative net worth. Model 2 was significant, with an R² of .419. Risk tolerance, current income, parent socio-economic status, gender, age, marital status, and homeownership all contributed to a positive net worth. Compared to high school graduates, having a college education contributed to a positive net worth while having only a grade school education detracted from having a positive net worth. Being Black or Hispanic, as compared to being White, detracted from positive net worth. The results of this study must be juxtaposed against the limitations, which include the use of proxy variables for time perspective (which may not accurately reflect the constructs), erosion of the longitudinal sample over time, the use of a variable (risk tolerance) from a different year, non-normal distribution of some control variables, and potential endogeneity caused by the inclusion of homeownership as a control variable. Those limitations having been noted, this study found that the strong influence of future-orientation on reducing the odds of having a negative net worth and contributing to positive net worth is significant because it validates the entire concept of financial planning, which proposes that having a future financial path will help clients achieve financial success. It also opens up new possibilities in financial counseling, in that clients may benefit from time-perspective therapy and coaching. In addition, the findings of this study emphasize the positive influence of homeownership on net worth.

Going into retirement, near-retirees are looking at increased debt levels, which can offset any asset accumulations and reduce retirement income. By using data from the 2008 Health and Retirement Study (HRS), this study examines the debt and negative net worth of near-retirees. This study further investigates what factors are associated with the likelihood of holding consumer debt, holding mortgage debt, and holding home equity debt over holding no debt, and what factors are associated with the likelihood of holding negative net worth over holding a high level of net worth among near-retirees. The study sample includes 3,745 individuals between the ages of 51 and 64. The results of the multinomial logistic regression analysis indicate that, all else being equal, human capital factors such as education, physical health problems, and depression symptoms play a significant role in predicting the likelihood of holding debt and negative net worth. In particular, education is positively associated with the likelihood of holding consumer, mortgage, and home equity debt over holding no debt, while it is negatively associated with the likelihood of having negative net worth over having a high level of net worth. Among the socioeconomic characteristics that influence the likelihood of near-retirees holding debt and negative net worth are household income, working in the labor force, and race. In particular, household income positively influences the likelihood of holding mortgage debt over holding no mortgage debt as well as the likelihood of holding home equity debt over holding no home equity debt. However, household income negatively influences the likelihood of having negative net worth over having a higher level of net worth. The findings of this study could help financial educators, financial planners, and policymakers understand the differences in human capital and socioeconomic characteristics of near-retirees who hold some levels of debt over no debt and who hold no net worth or a lower level of net worth over a higher level of net worth. This study concludes that it is important for professionals, consumer educators, and financial planners to provide those who hold higher levels of debt and lower levels of net worth with financial literacy education; therefore, these individuals might be able to attain economic well-being in retirement.

本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。 本研究之結論為： 一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。 二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。 三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio. The conclusion of this research are: Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items. Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items. Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.