Financial software offers an appealing substitute for an investment in complex financial knowledge to help individuals make better financial decisions. Little is known, however, about which consumers use financial software and whether the use of financial software results in improved financial outcomes. Using data from the 2008 National Longitudinal Survey of Youth 1979 cohort (NLSY79), we find that respondents with greater human capital and financial resources are more likely to use financial software. The use of a financial software program to calculate retirement needs is a stronger independent predictor of accumulated retirement wealth than calculating retirement needs without a computer aid and is surpassed only by cognitive ability as an independent predictor of retirement savings. Results suggest that financial software is used primarily by those that have greater endowed and attained human capital and may be a complement to (rather than a substitute for) financial literacy.

Al-Ti-C-(Ce) grain refiners were prepared by combining in-situ reaction, hot extrusion, and adding CeO2. The effects of second phase TiC particle size and distribution, extrusion ratio, and Ce addition on the grain-refining performance of grain refiners were investigated. The results show that about 10 nm TiC particles are dispersed on the surface and inside of 100–200 nm Ti particles by in-situ reaction. The Al-Ti-C grain refiners, which are made, by hot extrusion, of a mixture of in-situ reaction Ti/TiC composite powder and Al powder, increase the effective nucleation phase of α-Al and hinder grain growth due to the fine and dispersed TiC; this results in the average size of pure aluminum grains to decrease from 1912.4 μm to 504.8 μm (adding 1 wt.% Al-Ti-C grain refiner). Additionally, with the increase of the extrusion ratio from 13 to 30, the average size of pure aluminum grains decreases further to 470.8 μm. This is because the micropores in the matrix of grain refiners are reduced, and the nano-TiC aggregates are dispersed with the fragmentation of Ti particles, resulting in a sufficient Al-Ti reaction and an enhanced nucleation effect of nano-TiC. Furthermore, Al-Ti-C-Ce grain refiners were prepared by adding CeO2. Under the conditions of holding for 3–5 min and adding a 5.5 wt.% Al-Ti-C-Ce grain refiner, the average size of pure aluminum grains is reduced to 48.4–48.8 μm. The reason for the excellent grain-refining and good anti-fading performance of the Al-Ti-C-Ce grain refiner is presumedly related to the Ti2Al20Ce rare earth phases and [Ce] atoms, which hinder agglomeration, precipitation, and dissolution of the TiC and TiAl3 particles.

Since the “high stock dividend” of A-share companies in China often leads to the short-term stock price increase, this phenomenon’s prediction has been widely concerned by academia and industry. In this study, a new multi-layer stacking ensemble algorithm is proposed. Unlike the classic stacking ensemble algorithm that focused on the differentiation of base models, this paper used the equal weight comprehensive feature evaluation method to select features before predicting the base model and used a genetic algorithm to match the optimal feature subset for each base model. After the base model’s output prediction, the LightGBM (LGB) model was added to the algorithm as a secondary information extraction layer. Finally, the algorithm inputs the extracted information into the Logistic Regression (LR) model to complete the prediction of the “high stock dividend” phenomenon. Using the A-share market data from 2010 to 2019 for simulation and evaluation, the proposed model improves the AUC (Area Under Curve) and F1 score by 0.173 and 0.303, respectively, compared to the baseline model. The prediction results shed light on event-driven investment strategies.

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