Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network and long short-term memory model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their (...) models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function. (shrink)

In the traditional system of Chinese families, individuals are embedded in the institution of the family with defined obligations to enhance family development. As a consequence of the male-biased sex ratio at birth in China since the 1980s, an increasing number of surplus rural males have been affected by a marriage squeeze becoming involuntary bachelors. Under China’s universal heterosexual marriage tradition, family development of rural involuntary bachelors has largely been ignored, but in China’s gender-imbalanced society, it is necessary to adopt (...) a family-based approach to identify and study the plight of rural involuntary bachelors. Studies on gender imbalance indicate that these men face multiple risks from the perspectives of their life course, the family life cycle, and the family ethic. To a certain extent, these risks are caused by a conflict between the individual’s family life and family ethics and are mainly reflected in problems concerning marriage, health, and old-age support. Not only do these vulnerabilities affect the individual and family development across the whole life cycle but also pose major risks to social development in the face of strong gender imbalance. In order to deal with risks faced by rural involuntary bachelors, core ethical principles, including autonomy, beneficence, and justice, need to be adopted. Through adjustments to informal support provided by the family and formal support provided by policy-makers, risk of uncertainty in family development faced by rural involuntary bachelors could be reduced. (shrink)

Fraud can cause severe financial losses and affect the physical and mental health of victims. This study aimed to explore the manipulative characteristics of fraudsters and their relationship with other psychological variables. Thirty-four fraudsters were selected from a medium-security prison in China, and thirty-one healthy participants were recruited online. Both groups completed an emotional face-recognition task and self-report measures assaying emotional manipulation, psychopathy, emotion recognition, and empathy. Results showed that imprisoned fraudsters had higher accuracy in identifying fear and surprise faces (...) but lower accuracy in identifying happiness than controls. Significantly lower scores on non-prosocial factors on the Managing the Emotions of Others scale were found for imprisoned fraudsters, relative to controls. Imprisoned fraudsters had low scores in the assessment of psychopathy than the control group, especially Factor 1. For empathy, imprisoned fraudsters had significantly higher scores in perspective-taking than controls. Correlation analyses revealed that psychopathic traits were positively correlated with non-prosocial factors in both groups. However, the relationships between emotional manipulation and emotional recognition and empathy were not consistent across the groups. The results suggest that fraudsters may pretend to be as prosocial as healthy controls, who had lower antisocial tendencies, normal empathy ability, and would like to manipulate others’ emotions positively during social interaction. (shrink)

Background: Aggression is an important risk factor for delinquency and crime in adolescents. Previous studies have indicated that childhood maltreatment plays an important role in the development of aggression. However, whether the effect could be mediated by other factors is still unknown. Evidence suggests that callous-unemotional (CU) traits and self-control may be candidate mediators in the relationship between childhood maltreatment and aggression.Methods: A total of 585 male juvenile delinquents from China were recruited for the present study. We measured self-reported childhood (...) maltreatment, CU traits, self-control, and aggression with the short form of the Childhood Trauma Questionnaire (CTQ-SF), the Inventory of Callous-Unemotional Traits (ICU), the Self-Control Scale (SCS), and the Aggression Questionnaire (AQ), respectively. Furthermore, we constructed multiple mediation models to investigate the mediating effects of CU traits and self-control on the relationship between childhood maltreatment and aggression. Childhood maltreatment and aggression were entered into the model as the independent and dependent variables, respectively, and CU traits and self-control were treated as the mediating variables. In addition, the moderating role of self-control in the relationship between childhood maltreatment and aggression was tested by constructing a moderation model.Results: Correlation analysis showed significant positive correlations among childhood maltreatment, CU... (shrink)

ObjectiveThe objective of this paper is to evaluate the cognitive benefit of exercise after stroke, so as to provide more accurate and reliable guidance for targeted exercise intervention. Methods. Randomized controlled trials of the relationship between exercise and cognition after stroke were identified in Cochrane Library and PubMed. Methodological quality was assessed using the Cochrane tool of bias. SMD and 95% confidence intervals were calculated, and Chi-squared test was adopted to estimate the heterogeneity. Results. Twenty-three studies met inclusion criteria, including (...) 1528 participants. Heterogeneity was from low to high such as attention, executive function, cognition, and working memory. The overall effect on cognition was small but significant and there is a difference between cognitive domains in attention, executive function, and working memory. Exercise training was less effective before the 18th month after stroke. Higher benefits on cognition were found after combined therapy compared with other exercise programs, and the older the stroke survivors, the less the cognitive benefit of exercise. Conclusion. Small-to-moderate effect of exercise on cognitive benefit after stroke was found, and the effect was moderated by treatment protocols and sample characteristics. (shrink)

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are (...) inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. (shrink)