Application of improved convolution neural network in financial forecasting

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

Financial vulnerability and the impact of COVID-19 on American households

In May 2020, Westat, in partnership with Stanford University School of Medicine, conducted a nationally-representative household survey of American attitudes and behaviors regarding COVID-19. In this article, we examine what the Coronavirus Attitudes and Behaviors Survey tells us about the impact of COVID-19 on financial status and how this impact varies by demographic characteristics, the presence of health risk factors, and financial status (including employment factors). The survey reveals significant inequality in financial impact, as those who were most financially vulnerable prior to the pandemic found themselves under greater financial strain, while those who were more financially secure have experienced a neutral or even positive impact of the pandemic on household finances. These findings have important implications for public policy as policymakers seek to target aid to those who need it most.

Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud.

Literacy Challenges in Rural China

This article discusses the obstacles encountered in the process of literacy in rural China. Although China's overall literacy rate is high, there is a huge literacy rate gap between urban and rural areas. On this basis, this article explores the factors affecting the literacy rate in rural China, and concludes that the main factors are financial status, gender differences, health challenges, and policy interventions.

Social Insecurity At The Intersection Of Disabilities And Poverty In John Steinbeck’s Of Mice And Men

Poverty is much experienced by disabled people and vice versa. Having less or even no chance to public access has caused the feeling of uncertainty and social unworthiness. Social insecurity triggers anxiety about everything, and mostly about people’s self-confidence to create a relationship with others. Of Mice and Men represents society’s bad treatment for two disabled characters. The paralyzed condition, which is also worsened by their low-financial status, makes the two characters have some problems in adapting themselves to society. This paper aims to discuss kinds of social insecurity constituted at the intersection of disabilities and poverty using qualitative research analysis and descriptive methods. Intersectionality theory helps this paper to see and understand how oppression is formed because of people’s multiple identities. The result of this paper showed that the multiple identities of disabled people become barriers that give them the feeling of insecure to build relations with others and improve their life.

Exploring the issues, practices, and prospects of family planning among married couples in Southern Philippines

Background: Nurses are involved in all aspects of health, including reproductive health. They play a fundamental role in family planning and are often designated as point persons in family planning-related concerns. In order to provide effective counseling on family planning, the nurse must understand issues, practices, and prospects of family planning among married couples in their community. Objective: This exploratory multiple case study investigates family planning issues, practices, and prospects among couples in a municipality located in Southern Philippines. Methods: Ten married couples of varying characteristics were interviewed to elicit their perspectives on family planning practices. The data were analyzed using coding transcriptions and thematic analysis. Results: Five sub-themes emerged under the theme of Family Planning Issues: family planning as a burden; fear of side effects; peer-driven contraceptive choice; family planning as a social stigma; family planning as a sin. Two sub-themes emerged under the theme of Family Planning Practices: knowledge of family planning commodities; availability and acceptance of the contraceptive method. Finally, two sub-themes also emerged under the theme of Family Planning Prospects: family planning as a financially beneficial practice; prospects on family planning depend on husband’s acceptance. Conclusion: These sub-themes were also distinguished by their similarities and differences based on the four parameters identified, including age, sex, financial status, and educational attainment, which aided in the development of recommendations that could be implemented in the local community. The results of this study especially have a bearing on nurses and their role in the family planning process. Nurses play a fundamental role in guiding community members and families toward health and wellness. Thus, it is crucial for nurses to understand family planning issues underpinning their community so they can better exercise their role.